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Tan, Mingzhen, and Anqi Qiu. "Large Deformation Multiresolution Diffeomorphic Metric Mapping for Multiresolution Cortical Surfaces: A Coarse-to-Fine Approach." IEEE Transactions on Image Processing 25, no. 9 (September 2016): 4061–74. http://dx.doi.org/10.1109/tip.2016.2574982.
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Xia, Hedi, and Hector D. Ceniceros. "Kernel Treelets." Advances in Data Science and Adaptive Analysis 11, no. 03n04 (July 2019): 1950006. http://dx.doi.org/10.1142/s2424922x19500062.
Повний текст джерелаАнотація:
A new method for hierarchical clustering of data points is presented. It combines treelets, a particular multiresolution decomposition of data, with a mapping on a reproducing kernel Hilbert space. The proposed approach, called kernel treelets (KT), uses this mapping to go from a hierarchical clustering over attributes (the natural output of treelets) to a hierarchical clustering over data. KT effectively substitutes the correlation coefficient matrix used in treelets with a symmetric and positive semi-definite matrix efficiently constructed from a symmetric and positive semi-definite kernel function. Unlike most clustering methods, which require data sets to be numeric, KT can be applied to more general data and yields a multiresolution sequence of orthonormal bases on the data directly in feature space. The effectiveness and potential of KT in clustering analysis are illustrated with some examples.
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Fieguth, P., D. Menemenlis, T. Ho, A. Willsky, and C. Wunsch. "Mapping Mediterranean Altimeter Data with a Multiresolution Optimal Interpolation Algorithm." Journal of Atmospheric and Oceanic Technology 15, no. 2 (April 1998): 535–46. http://dx.doi.org/10.1175/1520-0426(1998)015<0535:mmadwa>2.0.co;2.
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Wang, Le, José L. Silván-Cárdenas, Jun Yang, and Amy E. Frazier. "Invasive Saltcedar (Tamariskspp.) Distribution Mapping Using Multiresolution Remote Sensing Imagery." Professional Geographer 65, no. 1 (February 2013): 1–15. http://dx.doi.org/10.1080/00330124.2012.679440.
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LIU, YONG-JIN, KAI TANG, AJAY JONEJA, and MATTHEW MING-FAI YUEN. "MULTIRESOLUTION SHAPE MODELING AND EDITING IN REVERSE ENGINEERING." International Journal of Image and Graphics 05, no. 04 (October 2005): 765–87. http://dx.doi.org/10.1142/s0219467805001999.
Повний текст джерелаАнотація:
In this paper we propose a multiresolution framework in reverse engineering for complex free-form object modeling and editing. The proposed framework starts with dense point data scanned from the surface of a physical prototype and produces CAD models ready for downstream applications. Targeting on achieving maximum efficiency in the whole reverse engineering process, the proposed framework adopt a hierarchy of shape representations in a special order, i.e. implicit, piecewise linear and parametric surfaces. Based on the proposed hierarchical shape structure, a set of shape editing operators such as Boolean operators, blending, offset, morphing, free-form deformation and texture mapping, is efficiently integrated into the framework. A great diversity of free-form shape models with various modeling operations is presented to demonstrate the effectiveness and efficiency of the proposed multiresolution framework.
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Shyh-Fang, Huang. "Video Classification and Adaptive QoP/QoS Control for Multiresolution Video Applications on IPTV." International Journal of Digital Multimedia Broadcasting 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/801641.
Повний текст джерелаАнотація:
With the development of heterogeneous networks and video coding standards, multiresolution video applications over networks become important. It is critical to ensure the service quality of the network for time-sensitive video services. Worldwide Interoperability for Microwave Access (WIMAX) is a good candidate for delivering video signals because through WIMAX the delivery quality based on the quality-of-service (QoS) setting can be guaranteed. The selection of suitable QoS parameters is, however, not trivial for service users. Instead, what a video service user really concerns with is the video quality of presentation (QoP) which includes the video resolution, the fidelity, and the frame rate. In this paper, we present a quality control mechanism in multiresolution video coding structures over WIMAX networks and also investigate the relationship between QoP and QoS in end-to-end connections. Consequently, the video presentation quality can be simply mapped to the network requirements by a mapping table, and then the end-to-end QoS is achieved. We performed experiments with multiresolution MPEG coding over WIMAX networks. In addition to the QoP parameters, the video characteristics, such as, the picture activity and the video mobility, also affect the QoS significantly.
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Zhang, Ziyou, Ziliang Feng, Yanqiong Guo, and Wei Wang. "Three-dimensional Face Recognition Method Based on Multiresolution Model and Fuzzy Random Matrix." Mathematical Problems in Engineering 2022 (June 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/2393014.
Повний текст джерелаАнотація:
Three-dimensional face recognition is one of the hotspots of biometric recognition and has a wide range of applications in the fields of information security, video surveillance, video tracking, and so on. The core part of 3D face recognition is to establish the corresponding 3D face model, and the key of building the model is how to obtain the shape model and accurate texture mapping. The problem has not been well solved in the field of face reconstruction. Based on the background, this paper makes an in-depth study and proposes a three-dimensional face recognition method based on multiresolution model and fuzzy random matrix. The face model reconstruction method of image and model can accurately obtain the contour change of face and the representation of specific features through multiresolution model, reduce the inaccurate description of error and noise in samples through fuzzy random matrix, and enhance the effectiveness of image information of classification and recognition. The experimental outcomes exhibit that the 3D face focus approach based totally on multiresolution mannequin and fuzzy random matrix efficiently improves the evaluation effectivity of the model, improves the great of mannequin matching, improves the function extraction in the cognizance process, and improves the attention rate.
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Pham, Quoc Thai, and Ba Chien Thai. "HDR Image Tone Mapping Approach Using Multiresolution and Piecewise Linear Perceptual Quantization." Advances in Science, Technology and Engineering Systems Journal 5, no. 2 (2020): 606–13. http://dx.doi.org/10.25046/aj050276.
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Loveland, Thomas R. "Toward a national fuels mapping strategy: Lessons from selected mapping programs." International Journal of Wildland Fire 10, no. 4 (2001): 289. http://dx.doi.org/10.1071/wf01030.
Повний текст джерелаАнотація:
This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 The establishment of a robust national fuels mapping program must be based on pertinent lessons from relevant national mapping programs. Many large-area mapping programs are under way in numerous Federal agencies. Each of these programs follows unique strategies to achieve mapping goals and objectives. Implementation approaches range from highly centralized programs that use tightly integrated standards and dedicated staff, to dispersed programs that permit considerable flexibility. One model facilitates national consistency, while the other allows accommodation of locally relevant conditions and issues. An examination of the programmatic strategies of four national vegetation and land cover mapping initiatives can identify the unique approaches, accomplishments, and lessons of each that should be considered in the design of a national fuel mapping program. The first three programs are the U.S. Geological Survey Gap Analysis Program, the U.S. Geological Survey National Land Cover Characterization Program, and the U.S. Fish and Wildlife Survey National Wetlands Inventory. A fourth program, the interagency Multiresolution Land Characterization Program, offers insights in the use of partnerships to accomplish mapping goals. Collectively, the programs provide lessons, guiding principles, and other basic concepts that can be used to design a successful national fuels mapping initiative.
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GRANELL, CLARA, SERGIO GÓMEZ, and ALEX ARENAS. "UNSUPERVISED CLUSTERING ANALYSIS: A MULTISCALE COMPLEX NETWORKS APPROACH." International Journal of Bifurcation and Chaos 22, no. 07 (July 2012): 1230023. http://dx.doi.org/10.1142/s0218127412300236.
Повний текст джерелаАнотація:
Unsupervised clustering, also known as natural clustering, stands for the classification of data according to their similarities. Here we study this problem from the perspective of complex networks. Mapping the description of data similarities to graphs, we propose to extend two multiresolution modularity based algorithms to the finding of modules (clusters) in general data sets producing a multiscales' solution. We show the performance of these reported algorithms to the classification of a standard benchmark of data clustering and compare their performance.
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KUMAR, ASHWANI, D. P. AGRAWAL, and S. D. JOSHI. "MULTIRESOLUTION FORECASTING FOR US RETAILING USING WAVELET DECOMPOSITIONS." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 04 (December 2003): 449–63. http://dx.doi.org/10.1142/s0219691303000281.
Повний текст джерелаАнотація:
In this paper we propose a simple forecasting strategy which exploits the multiresolution property of the wavelet transform. US aggregate retail sales data have strong trend and seasonal patterns, providing a good testing ground for the proposed forecasting method. First a wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in details and approximation. Then a forecasting engine (neural network or fuzzy inference system) is trained on each of the relevant resolution scales, and individual wavelet scale forecasts are recombined to form the overall forecast. Substantial information in both the dynamic nonlinear trend and seasonal patterns of the time series is efficiently exploited: we choose short past windows for the inputs to the forecasting engines at lower scales and long past windows at higher scales. The forecasting engines learn the mapping hierarchically: using a scale-recursive strategy, we combine only those scales where significant events are detected. Univariate simulation results on US aggregate retailing indicate that the proposed method fares favourably in relation to forecasting results obtained by training a neural network on original time series. Multivariate simulation results obtained by including structural components inflation, recession, interest rates, unemployment, show improvement in sales-trend forecast.
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He, Shaobai, Huaqiang Du, Guomo Zhou, Xuejian Li, Fangjie Mao, Di’en Zhu, Yanxin Xu, et al. "Intelligent Mapping of Urban Forests from High-Resolution Remotely Sensed Imagery Using Object-Based U-Net-DenseNet-Coupled Network." Remote Sensing 12, no. 23 (November 30, 2020): 3928. http://dx.doi.org/10.3390/rs12233928.
Повний текст джерелаАнотація:
The application of deep learning techniques, especially deep convolutional neural networks (DCNNs), in the intelligent mapping of very high spatial resolution (VHSR) remote sensing images has drawn much attention in the remote sensing community. However, the fragmented distribution of urban land use types and the complex structure of urban forests bring about a variety of challenges for urban land use mapping and the extraction of urban forests. Based on the DCNN algorithm, this study proposes a novel object-based U-net-DenseNet-coupled network (OUDN) method to realize urban land use mapping and the accurate extraction of urban forests. The proposed OUDN has three parts: the first part involves the coupling of the improved U-net and DenseNet architectures; then, the network is trained according to the labeled data sets, and the land use information in the study area is classified; the final part fuses the object boundary information obtained by object-based multiresolution segmentation into the classification layer, and a voting method is applied to optimize the classification results. The results show that (1) the classification results of the OUDN algorithm are better than those of U-net and DenseNet, and the average classification accuracy is 92.9%, an increase in approximately 3%; (2) for the U-net-DenseNet-coupled network (UDN) and OUDN, the urban forest extraction accuracies are higher than those of U-net and DenseNet, and the OUDN effectively alleviates the classification error caused by the fragmentation of urban distribution by combining object-based multiresolution segmentation features, making the overall accuracy (OA) of urban land use classification and the extraction accuracy of urban forests superior to those of the UDN algorithm; (3) based on the Spe-Texture (the spectral features combined with the texture features), the OA of the OUDN in the extraction of urban land use categories can reach 93.8%, thereby the algorithm achieved the accurate discrimination of different land use types, especially urban forests (99.7%). Therefore, this study provides a reference for feature setting for the mapping of urban land use information from VHSR imagery.
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Jung, Myung Hee, and Eui Jung Yun. "Change Detection/Feature Extraction System Based on Remotely Sensed Imagery." Key Engineering Materials 277-279 (January 2005): 349–54. http://dx.doi.org/10.4028/www.scientific.net/kem.277-279.349.
Повний текст джерелаАнотація:
Natural land cover patterns continuously undergo changes, impacted by various natural as well as human-managed factors. The remotely sensed data are commonly utilized to detect land cover change, which is important to understanding long-term landscape dynamics. Generally, a methodology for global change is composed of mapping, quantifying, and monitoring changes in the physical characteristics of land cover. The selected processing and analysis techniques affect the quality of the obtained information. In this research, a change detection/feature extraction system is proposed based on remotely sensed data: preprocessing, change detection and segmentation, resulting in the mapping of the change-detected areas. Here, appropriate methods are studied for each step and in particular, in the segmentation process, a multiresolution framework to reduce computational complexity is investigated for multitemporal images of large size.
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Shakoor, Mohammad Hossein, and Reza Boostani. "Extended Mapping Local Binary Pattern Operator for Texture Classification." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 06 (March 30, 2017): 1750019. http://dx.doi.org/10.1142/s0218001417500197.
Повний текст джерелаАнотація:
In this paper, an Extended Mapping Local Binary Pattern (EMLBP) method is proposed that is used for texture feature extraction. In this method, by extending nonuniform patterns a new mapping technique is suggested that extracts more discriminative features from textures. This new mapping is tested for some LBP operators such as CLBP, LBP, and LTP to improve the classification rate of them. The proposed approach is used for coding nonuniform patterns into more than one feature. The proposed method is rotation invariant and has all the positive points of previous approaches. By concatenating and joining two or more histograms significant improvement can be made for rotation invariant texture classification. The implementation of proposed mapping on Outex, UIUC and CUReT datasets shows that proposed method can improve the rate of classifications. Furthermore, the introduced mapping can increase the performance of any rotation invariant LBP, especially for large neighborhood. The most accurate result of the proposed technique has been obtained for CLBP. It is higher than that of some state-of-the-art LBP versions such as multiresolution CLBP and CLBC, DLBP, VZ_MR8, VZ_Joint, LTP, and LBPV.
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Duan, GongHao, JunChi Zhang, and Shuiping Zhang. "Assessment of Landslide Susceptibility Based on Multiresolution Image Segmentation and Geological Factor Ratings." International Journal of Environmental Research and Public Health 17, no. 21 (October 27, 2020): 7863. http://dx.doi.org/10.3390/ijerph17217863.
Повний текст джерелаАнотація:
Evaluating the susceptibility of regional landslides is one of the core steps in spatial landslide prediction. Starting from multiresolution image segmentation and object-oriented classification theory, this paper uses the four parameters of entropy, energy, correlation, and contrast from remote-sensing images in the Zigui–Badong section of Three Gorges Reservoir as image texture factors; the original image data for the study area were divided into 2279 objects after segmentation. According to the various indicators of the existing historical landslide database in the Three Gorges Reservoir area, combined with the classification processing steps for different types of multistructured data, the relevant geological evaluation factors, including the slope gradient, slope structure, and engineering rock group, were rated based on expert experience. From the perspective of the object-oriented segmentation of multiresolution images and geological factor rating classification, the C5.0 decision tree susceptibility classification model was constructed for the prediction of four types of landslide susceptibility units in the Zigui–Badong section. The mapping results show that the engineering rock group of a high-susceptibility unit usually develops in soft rock or soft–hard interphase rock groups, and the slope is between 15°–30°. The model results show that the average accuracy is 91.64%, and the kappa coefficients are 0.84 and 0.51, indicating that the C5.0 decision tree algorithm provides good accuracy and can clearly divide landslide susceptibility levels for a specific area, respectively. This landslide susceptibility classification, based on multiresolution image segmentation and geological factor classification, has potential applicability.
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Ouma, Y. O., and R. Tateishi. "Lake water body mapping with multiresolution based image analysis from medium‐resolution satellite imagery." International Journal of Environmental Studies 64, no. 3 (June 2007): 357–79. http://dx.doi.org/10.1080/00207230500196856.
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Viennois, G., N. Barbier, I. Fabre, and P. Couteron. "Multiresolution quantification of deciduousness in West Central African forests." Biogeosciences Discussions 10, no. 4 (April 23, 2013): 7171–200. http://dx.doi.org/10.5194/bgd-10-7171-2013.
Повний текст джерелаАнотація:
Abstract. The characterization of leaf phenology in tropical forests is of major importance and improves our understanding of earth-atmosphere-climate interactions. The availability of satellite optical data with a high temporal resolution has permitted the identification of unexpected phenological cycles, particularly over the Amazon region. A primary issue in these studies is the relationship between the optical reflectance of pixels of 1 km or more in size and ground information of limited spatial extent. In this paper, we demonstrate that optical data with high to very-high spatial resolution can help bridge this scale gap by providing snapshots of the canopy that allow discernment of the leaf-phenological stage of trees and the proportions of leaved crowns within the canopy. We also propose applications for broad-scale forest characterization and mapping in West Central Africa over an area of 141 000 km2. Eleven years of the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data were averaged over the wet and dry seasons to provide a dataset of optimal radiometric quality at a spatial resolution of 250 m. Sample areas covered at a very-high (GeoEye) and high (SPOT-5) spatial resolution were used to identify forest types and to quantify the proportion of leaved trees in the canopy. The dry season EVI was positively correlated with the proportion of leaved trees in the canopy. This relationship allowed the conversion of EVI into canopy deciduousness at the regional level. On this basis, ecologically important forest types could be mapped, including young secondary, open Marantaceae, Gilbertiodendron dewevrei and swamp forests. We show that in west central African forests, a large share of the variability in canopy reflectance, as captured by the EVI, is due to variation in the proportion of leaved trees in the upper canopy, thereby opening new perspectives for biodiversity and carbon-cycle applications.
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Silva, Bruno Aparecido da, Ericson Hideki Hayawaka, and Vanda Moreira Martins. "Espacialização das classes solos a partir da utilização de atributos geomorfométricos na Bacia do Paraná 3, Brasil." Revista Brasileira de Geografia Física 14, no. 7 (January 3, 2022): 4126. http://dx.doi.org/10.26848/rbgf.v14.7.p4126-4147.
Повний текст джерелаАнотація:
O emprego de atributos geomorfométricos para a espacialização das classes de solos, associado aos pressupostos da relação solo-relevo, tornou-se uma das bases para a obtenção das Unidades de Mapeamento (UM) dos solos. Essa abordagem norteou o objetivo deste trabalho que foi verificar a possibilidade de espacializar as classes de solos a partir de atributos geomorfométricos na Bacia do Paraná 3 (BP3), região Oeste do estado do Paraná. Os procedimentos incluíram a obtenção de atributos topográficos de Declividade (D), Sediment Transport Index (STI), Terrain Wetness Index (TWI), Multiresolution Index of Valley Bottom Flatness (MRVBF), Multiresolution Ridge Top Flatness Index (MRRTF) e Índice de Posição Relativa (IPR), utilizando Sistemas de Informações Geográficas (SIG). As operações de manipulação dos campos geográficos (variáveis geomorfométricas) permitiram a obtenção de cinco UM de solos. A Análise de Componentes Principais (ACP) retratou a correlação entre as unidades mapeadas e cada variável empregada. O mapa de solos gerado foi comparado com os mapas oficiais disponíveis, obtendo-se avanço no detalhamento da escala de mapeamento dos solos e aprimoramento dos limites das UM. As UM foram aferidas e validadas com a verificação de 170 pontos amostrais em campo. São elas: LATOSSOLO VERMELHO; NITOSSOLO VERMELHO; NEOSSOLO REGOLÍTICO + NEOSSOLO LITÓLICO + CAMBISSOLO HÁPLICO; GLEISSOLO HÁPLICO + CAMBISSOLO FLÚVICO e CAMBISSOLO HÁPLICO + NEOSSOLO REGOLÍTICO + GLEISSOLO HÁPLICO. Os resultados da validação (Exatidão global – 0,80) demonstraram a eficiência dos procedimentos adotados para a espacialização das UM de solos na escala adotada, com abrangência regional.Palavras-chave: mapeamento de solos; geomorfometria; solo-relevo. Spatial distribution of soil classes based on the use of geomorphometric attributes in Paraná Basin 3, Brazil A B S T R A C TThe use of geomorphometric attributes for the spatial distribution of soil classes, associated with suppositions on the soil-relief relationship, has become a basis to obtain soil Map Units (MU). This approach guided the aim of this study, which was to verify the possibility of soil class spatialization based on geomorphometric attributes in Paraná Basin 3 (BP3), in the western region of the state of Paraná. The procedures include obtaining topographical attributes of Declivity (D), Sediment Transport Index (STI), Terrain Wetness Index (TWI), Multiresolution Index of Valley Bottom Flatness (MRVBF), Multiresolution Ridge Top Flatness Index (MRRTF) and Relative Slope Position, using Geographic Information Systems (GIS). Manipulation of the geographic fields (geomorphometric variables) enabled attainment of five soil MU. Principal Components Analysis (PCA) portrayed the correlation between the mapped units and each variable. The generated soil map was compared with the available official maps, to improve the detail of the scale of soil mapping and enhance the MU limits. The MU were measured and validated with the verification of 170 sample points in the field. They are: Rhodic Ferrasols + Eutric Nitosols, Regosols + Leptsols + Eutric Cambisols, Eutric Gleysols + Eutric Cambisols and Eutric Cambisols + Regosols + Eutric Gleysols. The validation results (Global accuracy – 0.80) demonstrated the efficiency of the procedures adopted for the spatial distribution of the soil MU at the adopted scale, with regional coverage.Keywords: soil mapping; geomorphometry; soil-geomorphology.
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Viennois, G., N. Barbier, I. Fabre, and P. Couteron. "Multiresolution quantification of deciduousness in West-Central African forests." Biogeosciences 10, no. 11 (November 4, 2013): 6957–67. http://dx.doi.org/10.5194/bg-10-6957-2013.
Повний текст джерелаАнотація:
Abstract. The characterization of leaf phenology in tropical forests is of major importance for forest typology as well as to improve our understanding of earth–atmosphere–climate interactions or biogeochemical cycles. The availability of satellite optical data with a high temporal resolution has permitted the identification of unexpected phenological cycles, particularly over the Amazon region. A primary issue in these studies is the relationship between the optical reflectance of pixels of 1 km or more in size and ground information of limited spatial extent. In this paper, we demonstrate that optical data with high to very-high spatial resolution can help bridge this scale gap by providing snapshots of the canopy that allow discernment of the leaf-phenological stage of trees and the proportions of leaved crowns within the canopy. We also propose applications for broad-scale forest characterization and mapping in West-Central Africa over an area of 141 000 km2. Eleven years of the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data were averaged over the wet and dry seasons to provide a data set of optimal radiometric quality at a spatial resolution of 250 m. Sample areas covered at a very-high (GeoEye) and high (SPOT-5) spatial resolution were used to identify forest types and to quantify the proportion of leaved trees in the canopy. The dry-season EVI was positively correlated with the proportion of leaved trees in the canopy. This relationship allowed the conversion of EVI into canopy deciduousness at the regional level. On this basis, ecologically important forest types could be mapped, including young secondary, open Marantaceae, Gilbertiodendron dewevrei and swamp forests. We show that in West-Central African forests, a large share of the variability in canopy reflectance, as captured by the EVI, is due to variation in the proportion of leaved trees in the upper canopy, thereby opening new perspectives for biodiversity and carbon-cycle applications.
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Jiménez-Lao, R., M. A. Aguilar, C. Ladisa, F. J. Aguilar, and A. Nemmaoui. "MULTIRESOLUTION SEGMENTATION FOR EXTRACTING PLASTIC GREENHOUSES FROM DEIMOS-2 IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022 (May 17, 2022): 251–58. http://dx.doi.org/10.5194/isprs-annals-v-2-2022-251-2022.
Повний текст джерелаАнотація:
Abstract. Accurate greenhouse mapping can support environment monitoring and resource management. In an object-based image analysis (OBIA) approach focused on plastic covered greenhouses (PCG) classification, the segmentation is a crucial step for the goodness of the final results. Multiresolution segmentation (MRS) is one of the most used algorithms in OBIA approaches, being greatly enabled by the advent of the commercial software eCognition. Therefore, in addition to the segmentation algorithm used, it is very important to count on tools to assess the quality of segmentation results from digital images in order to obtain the most similar segments to the real PCG objects. In this work, several factors affecting MRS such as the type of input image and the best MRS parameters (i.e., scale, compactness and shape), have been analysed. In this regard, more than 2800 segmentations focused on PCG land cover were conducted from four pre-processed Deimos-2 very high-resolution (VHR) satellite orthoimages taken in the Southeast of Spain (Almería). Specifically, one multispectral and one pansharpened Deimos-2 orthoimages, both with and without atmospheric correction were tested in this work. The free access AssesSeg command line tool, based on a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2), was used to determine the best MRS parameters for all the VHR satellite images. According to both the supervised discrepancy measure ED2 and visual perception, the best segmentation on PCG was obtained over the atmospherically corrected pansharpened Deimos-2 orthoimage, achieving very good results.
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KALERA, MEENAKSHI K., SARGUR SRIHARI, and AIHUA XU. "OFFLINE SIGNATURE VERIFICATION AND IDENTIFICATION USING DISTANCE STATISTICS." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 07 (November 2004): 1339–60. http://dx.doi.org/10.1142/s0218001404003630.
Повний текст джерелаАнотація:
This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78% and 93% for verification and identification, respectively. This method was successfully employed in our previous theory of individuality of handwriting developed at CEDAR — based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verification and identification as offline handwriting verification and identification tasks respectively, we depict a mapping from the handwriting domain to the signature domain.
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Wang, Yan, and Bart O. Nnaji. "Document-Driven Design for Distributed CAD Services in Service-Oriented Architecture." Journal of Computing and Information Science in Engineering 6, no. 2 (August 11, 2005): 127–38. http://dx.doi.org/10.1115/1.2194911.
Повний текст джерелаАнотація:
Current computer-aided design (CAD) systems only support interactive geometry generation, which is not ideal for distributed engineering services in enterprise-to-enterprise collaboration with a generic thin-client service-oriented architecture. This paper proposes a new feature-based modeling mechanism—document-driven design—to enable batch mode geometry construction for distributed CAD systems. A semantic feature model is developed to represent informative and communicative design intent. Feature semantics is explicitly captured as a trinary relation, which provides good extensibility and prevents semantics loss. Data interoperability between domains is enhanced by schema mapping and multiresolution semantics. This mechanism aims to enable asynchronous communication in distributed CAD environments with ease of design alternative evaluation and reuse, reduced human errors, and improved system throughput and utilization.
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Jamshidi, Sajad, Shahrokh Zand-parsa, Mojtaba Pakparvar, and Dev Niyogi. "Evaluation of Evapotranspiration over a Semiarid Region Using Multiresolution Data Sources." Journal of Hydrometeorology 20, no. 5 (May 1, 2019): 947–64. http://dx.doi.org/10.1175/jhm-d-18-0082.1.
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Abstract Evapotranspiration (ET) estimation is important for water management decision tools. In this study, different ET data with varying resolution, accuracy, and functionality were reviewed over a semiarid, data-sparse region in southern Iran. Study results showed that the widely used reanalysis and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets have relatively high uncertainty and underestimated ET over the sparse heterogeneous landscape. On the other hand, fine-resolution ET datasets using Landsat imagery with Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Surface Energy Balance System (SEBS) algorithms, yielded high accuracy. Evaluation of METRIC and SEBS models in estimating seasonal crop water use showed a mean absolute error of 5% and 13%, respectively. The Satellite Application Facility on Climate Monitoring (CMSAF) data were used as radiation input to the models and were found to be a representative data source with daily average RMSE of 70 W m−2. An average crop coefficient Kc was estimated for the region and was obtained as 0.77. The study proposes and applies a hybrid framework that uses reference ET from simple diagnostic models (such as the REF-ET tool) and calculates actual ET by using the satellite-derived regionally and locally representative Kc values. The ET estimates generated with the framework were regionally representative and required low computational resources. The study findings have the potential to provide practical guidance to local farmers and water managers to generate useful and usable decision-making tools, especially for ET assessments in the study region and other data-sparse areas.
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Zhang, R., X. Yi, H. Li, L. Liu, G. Lu, Y. Chen, and X. Guo. "MULTIRESOLUTION PATCH-BASED DENSE RECONSTRUCTION INTEGRATING MULTIVIEW IMAGES AND LASER POINT CLOUD." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 153–59. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-153-2022.
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Abstract. A dense point cloud with rich and realistic texture is generated from multiview images using dense reconstruction algorithms such as Multi View Stereo (MVS). However, its spatial precision depends on the performance of the matching and dense reconstruction algorithms used. Moreover, outliers are usually unavoidable as mismatching of image features. The lidar point cloud lacks texture but performs better spatial precision because it avoids computational errors. This paper proposes a multiresolution patch-based 3D dense reconstruction method based on integrating multiview images and the laser point cloud. A sparse point cloud is firstly generated with multiview images by Structure from Motion (SfM), and then registered with the laser point cloud to establish the mapping relationship between the laser point cloud and multiview images. The laser point cloud is reprojected to multiview images. The corresponding optimal level of the image pyramid is predicted by the distance distribution of projected pixels, which is used as the starting level for patch optimization during dense reconstruction. The laser point cloud is used as stable seed points for patch growth and expansion, and stored by the dynamic octree structure. Subsequently, the corresponding patches are optimized and expanded with the pyramid image to achieve multiscale and multiresolution dense reconstruction. In addition, the octree’s spatial index structure facilitates parallel computing with highly efficiency. The experimental results show that the proposed method is superior to the traditional MVS technology in terms of model accuracy and completeness, and have broad application prospects in high-precision 3D modeling of large scenes.
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Rahmani, A., F. Sarmadian, S. R. Mousavi, and S. E. Khamoshi. "DIGITAL SOIL MAPPING USING GEOMORPHOMETRIC ANALYSIS AND CASE-BASED FUZZY LOGIC APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 863–66. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-863-2019.
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Abstract. In low relief region such as plains, applied digital soil mapping has a controvertible issue, therefore, this study was aimed to digital mapping of soil classes at family levels by appropriate Geomorphometric variables along with fuzzy logic with area of 16,600 hectares in Qazvin Plain. Based on the geomorphologic map, the plain and pen plain are dominant landscape units. In this regards, 61 soil profiles were dogged. According to the expert’s opinion, covariates including diffuse insolation, standardized height, catchment area, valley depth and multiresolution valley bottom flatness (MrVBF) had the most important in order to generating soil map. Also, 19 fuzzy soil class maps were generated through using sample-based in ArcSIE software. Validation were carried out using achieved overall accuracy (OA) and Kappa index through error matrix. Subsequently, both ignorance and exaggerating uncertainty of hardened soil map were also done. The results showed that 19 soil families class were found. Accordingly, OA and the Kappa index were 54% and 46% respectively. The uncertainty of ignorance and exaggeration were obtained from 0 to 0.64 and 0 to 1, respectively. Moreover, the results indicated that exaggerated uncertainty was the highest in the northern and the lowest in the southern regions. Generally, applied geomorphometric parameters had the specific importance in the low relief areas for mapping of soils that have not been assessed properly so far.
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Yu, Chuantao, Weiying Chen, Xi Zhang, and Kangxin Lei. "Review and Challenges in the Geophysical Mapping of Coal Mine Water Structure." Geofluids 2022 (July 19, 2022): 1–14. http://dx.doi.org/10.1155/2022/4578072.
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Generally, the water-bearing structures of coal mines mainly include coal seam, roof, coal seam, floor, and goaf, while the main water-conducting structures include faults, collapsed columns, and collapsed goaf areas. The most commonly used methods for the detection of the above structures include the seismic method, high-density electrical method, controlled source audio-frequency magnetotelluric method, and transient electromagnetic method. Theoretically, the seismic methods have a higher resolution, which can be used to determine the targets’ geometry, but unable to determine whether the target is filled with water, while the electromagnetic methods are capable of this, although with lower resolution. Therefore, it is necessary to adopt the comprehensive geophysical prospecting in the actual field measurement of the coal mine water to guarantee the detection accuracy. Based on this, in the passage, first, we introduced the characteristics of the water-bearing and water-conducting structures and then analyzed the detection results of different methods by examples. Finally, we pointed out that, first, it is essential to develop the mine or airborne methods for the sake of convenience; and second, it is time that we adopt three-dimensional detection technology, multisource and multiresolution detection technology, and electromagnetic big data technology for high accuracy.
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Atik, Saziye Ozge, and Cengizhan Ipbuker. "Integrating Convolutional Neural Network and Multiresolution Segmentation for Land Cover and Land Use Mapping Using Satellite Imagery." Applied Sciences 11, no. 12 (June 15, 2021): 5551. http://dx.doi.org/10.3390/app11125551.
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Depletion of natural resources, population growth, urban migration, and expanding drought conditions are some of the reasons why environmental monitoring programs are required and regularly produced and updated. Additionally, the usage of artificial intelligence in the geospatial field of Earth observation (EO) and regional land monitoring missions is a challenging issue. In this study, land cover and land use mapping was performed using the proposed CNN–MRS model. The CNN–MRS model consisted of two main steps: CNN-based land cover classification and enhancing the classification with spatial filter and multiresolution segmentation (MRS). Different band numbers of Sentinel-2A imagery and multiple patch sizes (32 × 32, 64 × 64, and 128 × 128 pixels) were used in the first experiment. The algorithms were evaluated in terms of overall accuracy, precision, recall, F1-score, and kappa coefficient. The highest overall accuracy was obtained with the proposed approach as 97.31% in Istanbul test site area and 98.44% in Kocaeli test site area. The accuracies revealed the efficiency of the CNN–MRS model for land cover map production in large areas. The McNemar test measured the significance of the models used. In the second experiment, with the Zurich Summer dataset, the overall accuracy of the proposed approach was obtained as 92.03%. The results are compared quantitatively with state-of-the-art CNN model results and related works.
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Suleymanov, Azamat, Evgeny Abakumov, Ruslan Suleymanov, Ilyusya Gabbasova, and Mikhail Komissarov. "The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes." ISPRS International Journal of Geo-Information 10, no. 4 (April 7, 2021): 243. http://dx.doi.org/10.3390/ijgi10040243.
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Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.
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Cao, Xiaonan. "Ink Art Three-Dimensional Big Data Three-Dimensional Display Index Prediction Model." Complexity 2021 (April 14, 2021): 1–10. http://dx.doi.org/10.1155/2021/5564361.
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This paper starts with the study of realistic three-dimensional models, from the two aspects of ink art style simulation model and three-dimensional display technology, explores the three-dimensional display model of three-dimensional model ink style, and conducts experiments through the software development platform and auxiliary software. The feasibility of the model is verified. Aiming at the problem of real-time rendering of large-scale 3D scenes in the model, efficient visibility rejection method and a multiresolution fast rendering method were designed to realize the rapid construction and rendering of ink art 3D virtual reality scenes in a big data environment. A two-dimensional cellular automaton is used to simulate a brushstroke model with ink and wash style, and outlines are drawn along the path of the brushstroke to obtain an effect close to the artistic style of ink and wash painting. Set the surface of the model with ink style brushstroke texture patterns, refer to the depth map, normal map, and curvature map information of the model, and simulate the drawing effect of the method by procedural texture mapping. Example verification shows that the rapid visualization analysis model of ink art big data designed in this paper is in line with the prediction requirements of ink art big data three-dimensional display indicators. The fast visibility removal method is used to deal with large-scale three-dimensional ink art in a big data environment. High efficiency is achieved in virtual reality scenes, and the multiresolution fast rendering method better maintains the appearance of the prediction model without major deformation.
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Lubis, Kesuma Anggraini, Muhammad Rusdi, and Sugianto Sugianto. "Proses Segmentasi Citra Satelit Untuk Pemetaan Tutupan Lahan." Jurnal Ilmiah Mahasiswa Pertanian 6, no. 4 (November 1, 2021): 691–98. http://dx.doi.org/10.17969/jimfp.v6i4.18414.
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Abstrak. Salah satu permasalahan penting dalam bidang pengolahan citra dan pengenalan pola adalah segmentasi citra ke dalam area homogen. Ekstraksi ciri dan segmentasi citra merupakan langkah awal dalam analisis citra. Tujuan utama segmentasi adalah membagi citra ke dalam bagian-bagian yang mempunyai korelasi kuat dengan objek dalam citra. Pada proses segmentasi dapat dilakukan dengan berbagai pendekatan algoritma, salah satu algoritma yang banyak digunakan pada penelitian-penelitian sebelumnya adalah algoritma multiresolusi segmentasi. Berdasarkan konsep segmentasi, untuk mendapatkan hasil segmentasi dengan menggunakan algoritma multiresolusi segmentasi tergantung dari lima parameter yaitu parameter skala, bentuk, warna, kehalusan dan kekompakan. Penelitian ini bertujuan untuk mengkaji proses metode segmentasi citra satelit untuk pemetaan tutupan lahan dengan menggunakan algoritma multiresolution segmentation.Satellite Image Segmentation Process for Land Cover MappingAbstract. One of the important problems in image processing and pattern recognition is image segmentation into homogeneous areas. Feature extraction and image segmentation are the first steps in image analysis. The main purpose of segmentation is to divide the image into parts that have a strong correlation with the objects in the image. The segmentation process can be done with various algorithm approaches, one of the algorithms that is widely used in previous studies is the multi-resolution segmentation algorithm. Based on the concept of segmentation, to obtain segmentation results using a multi-resolution segmentation algorithm depends on five parameters, namely the parameters of scale, shape, color, smoothness and compactness. This study aims to examine the process of satellite image segmentation method for land cover mapping using a multiresolution segmentation algorithm.
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Singh, Harbinder, Vinay Kumar, and Sunil Bhooshan. "A Novel Approach for Detail-Enhanced Exposure Fusion Using Guided Filter." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/659217.
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In this paper we propose a novel detail-enhancing exposure fusion approach using nonlinear translation-variant filter (NTF). With the captured Standard Dynamic Range (SDR) images under different exposure settings, first the fine details are extracted based on guided filter. Next, the base layers (i.e., images obtained from NTF) across all input images are fused using multiresolution pyramid. Exposure, contrast, and saturation measures are considered to generate a mask that guides the fusion process of the base layers. Finally, the fused base layer is combined with the extracted fine details to obtain detail-enhanced fused image. The goal is to preserve details in both very dark and extremely bright regions without High Dynamic Range Image (HDRI) representation and tone mapping step. Moreover, we have demonstrated that the proposed method is also suitable for the multifocus image fusion without introducing artifacts.
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Meng, Qingyu, Hongyan Guo, Xiaoming Zhao, Dongpu Cao, and Hong Chen. "Loop-Closure Detection With a Multiresolution Point Cloud Histogram Mode in Lidar Odometry and Mapping for Intelligent Vehicles." IEEE/ASME Transactions on Mechatronics 26, no. 3 (June 2021): 1307–17. http://dx.doi.org/10.1109/tmech.2021.3062647.
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BAI, XIAOLIANG, and SHUSHENG ZHANG. "HIERARCHICAL PARAMETERIZATION OF TRIANGULAR MESH WITH A BOUNDARY POLYGON TRIANGULATION." International Journal of Image and Graphics 10, no. 03 (July 2010): 449–66. http://dx.doi.org/10.1142/s0219467810003858.
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Parameterizing a 3D triangular mesh is the process of finding an isomorphic planar mesh. It is widely used in graphics, as it is required, for instance, for surface fitting, texture mapping and re-meshing. In this paper, we present a new 3D approach to triangular mesh parameterization, which includes three steps: (1) construct a boundary polygon triangulation by mesh simplification; (2) parameterize the boundary polygon triangulation by first smoothing and then flattening it; (3) parameterize the interior vertices by parameterizing the vertex-split-cells one by one while refining the boundary polygon triangulation to the original one. The fact that all calculations are local makes it a fast approach, and the fact that a series of meshes in a multiresolution representation model could be well parameterized makes it appropriate for hierarchical surface fitting. Experiments show that the approach presented can result in a low distortion parameterization.
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Shirvani, Zeinab, Omid Abdi, and Manfred Buchroithner. "A Synergetic Analysis of Sentinel-1 and -2 for Mapping Historical Landslides Using Object-Oriented Random Forest in the Hyrcanian Forests." Remote Sensing 11, no. 19 (October 2, 2019): 2300. http://dx.doi.org/10.3390/rs11192300.
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Despite increasing efforts in the mapping of landslides using Sentinel-1 and -2, research on their combination for discerning historical landslides in forest areas is still lacking, particularly using object-oriented machine learning approaches. This study was accomplished to test the efficiency of Sentinel-derived features and digital elevation model (DEM) derivatives for mapping old and new landslides, using object-oriented random forest. Two forest subsets were selected including a protected and non-protected forest in northeast Iran. Landslide samples were obtained from CORONA images and aerial photos (old landslides), and also field mensuration and high-resolution images (new landslides). Segment objects were generated from a set combination of Sentinel-1A, Sentinel-2A, and some topographic-derived indices using multiresolution segmentation algorithm. Various object features were derived from the main channels of Sentinel images and DEM derivatives in the seven main groups, including spectral layers, spectral indices, geometric, contextual, textural, topographic, and hydrologic features. A single database was created, including landslide samples and Sentinel- and DEM-derived object features. Roughly 20% of landslide-affected objects and non-landslide-affected objects were randomly selected as an input for training the random forest classifier. Two-thirds of the selected objects were assigned as learning samples for classification, and the remainder were used for testing the accuracy of landslide and non-landslide classification. Results indicated that: (1) The sensitivity of mapping historical landslides was 86.6% and 80.3% in the protected and non-protected forests, respectively; (2) the object features of Sentinel-2A and DEM obtained the highest importance with the total scores of 55.6% and 32%, respectively in the protected forests, and 65.4% and 21% respectively in the non-protected forests; (3) the features derived from the combination of Sentinel-1 and -2A demonstrated a total importance of 10% for mapping new landslides; and (4) textural features were obtained in approximately two-thirds of the total scores for mapping new landslides, however a combination of topographic, spectral, textural, and contextual features were the effective predictors for mapping old landslides. This research proposes applying a synergetic analysis of Sentinel- and DEM-derived features for mapping historical landslides; however, there are no uniformly pre-defined influential variables for mapping historical landslides in different forest areas.
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Yong Li, Wei Qiao, Hongbin Jin, Jing Jing, and Chunxiao Fan. "Reliable and Fast Mapping of Keypoints on Large-Size Remote Sensing Images by Use of Multiresolution and Global Information." IEEE Geoscience and Remote Sensing Letters 12, no. 9 (September 2015): 1983–87. http://dx.doi.org/10.1109/lgrs.2015.2441731.
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Woźniak, Edyta, and Sebastian Aleksandrowicz. "Self-Adjusting Thresholding for Burnt Area Detection Based on Optical Images." Remote Sensing 11, no. 22 (November 15, 2019): 2669. http://dx.doi.org/10.3390/rs11222669.
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Mapping of regional fires would make it possible to analyse their environmental, social and economic impact, as well as to develop better fire management systems. However, automatic mapping of burnt areas has proved to be a challenging task, due to the wide diversity of vegetation cover worldwide and the heterogeneous nature of fires themselves. Here, we present an algorithm for the automatic mapping of burnt areas using medium-resolution optical images. Although developed for Landsat images, it can be also applied to Sentinel-2 images without modification. The algorithm draws upon the classical concept of differences in pre- and post-fire reflectance, but also takes advantage of the object-oriented approach and a new threshold calculation method. It consists of four steps. The first concerns the calculation of spectral indices and their differences, together with differences in spectral layers based on pre- and post-fire images. In the second step, multiresolution segmentation and masking are performed (clouds, water bodies and non-vegetated areas are removed from further analysis). Thirdly, ‘core’ burnt areas are detected using automatically-adjusted thresholds. Thresholds are calculated on the basis of specific functions established for difference layers. The last step combines neighbourhood analysis and patch growing to define the final shape of burnt areas. The algorithm was tested in 27 areas located worldwide, and covered by various types of vegetation. Comparisons with manual interpretation show that the fully-automated classification is accurate. Over 82% of classifications were considered satisfactory (overall accuracy > 90%; user and producer accuracy > 70%).
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Boier-Martin, Ioana, and Holly Rushmeier. "Reverse Engineering Methods for Digital Restoration Applications." Journal of Computing and Information Science in Engineering 6, no. 4 (May 30, 2006): 364–71. http://dx.doi.org/10.1115/1.2356497.
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In this paper we discuss the challenges of processing and converting 3D scanned data to representations suitable for interactive manipulation in the context of virtual restoration applications. We present a constrained parametrization approach that allows us to represent 3D scanned models as parametric surfaces defined over polyhedral domains. A combination of normal- and spatial-based clustering techniques is used to generate a partition of the model into regions suitable for parametrization. Constraints can be optionally imposed to enforce a strict correspondence between input and output features. We consider two types of virtual restoration methods: (a) a paint restoration method that takes advantage of the normal-based coarse partition to identify large regions of reduced metric distortion suitable for texture mapping and (b) a shape restoration approach that relies on a refined partition used to convert the input model to a multiresolution subdivision representation suitable for intuitive interactive manipulation during digital studies of historical artifacts.
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Boutsoukis, Christos, Ioannis Manakos, Marco Heurich, and Anastasios Delopoulos. "Canopy Height Estimation from Single Multispectral 2D Airborne Imagery Using Texture Analysis and Machine Learning in Structurally Rich Temperate Forests." Remote Sensing 11, no. 23 (December 1, 2019): 2853. http://dx.doi.org/10.3390/rs11232853.
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Canopy height is a fundamental biophysical and structural parameter, crucial for biodiversity monitoring, forest inventory and management, and a number of ecological and environmental studies and applications. It is a determinant for linking the classification of land cover to habitat categories towards building one-to-one relationships. Light detection and ranging (LiDAR) or 3D Stereoscopy are the commonly used and most accurate remote sensing approaches to measure canopy height. However, both require significant time and budget resources. This study proposes a cost-effective methodology for canopy height approximation using texture analysis on a single 2D image. An object-oriented approach is followed using land cover (LC) map as segmentation vector layer to delineate landscape objects. Global texture feature descriptors are calculated for each land cover object and used as variables in a number of classifiers, including single and ensemble trees, and support vector machines. The aim of the analysis is the discrimination among classes in a wide range of height values used for habitat mapping (from less than 5 cm to 40 m). For that task, different spatial resolutions are tested, representing a range from airborne to spaceborne quality ones, as well as their combinations, forming a multiresolution training set. Multiple dataset alternatives are formed based on the missing data handling, outlier removal, and data normalization techniques. The approach was applied using orthomosaics from DMC II airborne images, and evaluated against a reference LiDAR-derived canopy height model (CHM). Results reached overall object-based accuracies of 67% with the percentage of total area correctly classified exceeding 88%. Sentinel-2 simulation and multiresolution analysis (MRA) experiments achieved even higher accuracies of up to 85% and 91%, respectively, at reduced computational cost, showing potential in terms of transferability of the framework to large spatial scales.
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Deodoro, Sandra Cristina, William Zanete Bertolini, and Plinio da Costa Temba. "Quaternary formations mapping in the region of Volta Grande do Rio Uruguai (Brazil)." Geography Department University of Sao Paulo 41 (June 11, 2021): e174174. http://dx.doi.org/10.11606/eissn.2236-2878.rdg.2021.174174.
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Quaternary formations (detrital and weathered materials) are an important natural resource for different areas of scientific investigation, from understanding their relation to erosive processes and morphodynamic processes that create landforms or to understanding the history of the first human settlements (geoarcheology). Quaternary coverings can be formed in situ or be transported by external geologic agents. Regarding soils, Quaternary formations are related to landscape topography and are transformed according to the characteristics of this topography. Hence, classifying and mapping these soils is not always easy. The present article aims to map the Quaternary formations along a stretch of the Uruguay River basin known as Volta Grande (SC/RS-Brazil), by using topographic attributes derived from the SRTM GL1-Up Sampled digital elevation model, soil particle-size analysis, and a generated Multiresolution Index of Valley Bottom Flatness (MRVBF) index . The results of the analysis show that: (i) colluvium is the predominant Quaternary formation in the study area; (ii) there is a predominance of clay, corroborating previous studies of the region; (iii) the spatial distribution of the study area’s Quaternary formations reflect local slope dynamics based on morphology and topographic position; and, (iv) the existence of colluvium-alluvium on the Uruguay River’s banks indicates that slope attributes contributed to the pedogeomorphological dynamics of the study area and not only fluvial dynamics. Based on the results, the methodology applied in this study might be useful for pedogeomorphological studies, notably in the analysis and mapping of Quaternary formations, despite some of its limitations.
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Singh, Harbinder, Vinay Kumar, and Sunil Bhooshan. "Anisotropic Diffusion for Details Enhancement in Multiexposure Image Fusion." ISRN Signal Processing 2013 (May 19, 2013): 1–18. http://dx.doi.org/10.1155/2013/928971.
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We develop a multiexposure image fusion method based on texture features, which exploits the edge preserving and intraregion smoothing property of nonlinear diffusion filters based on partial differential equations (PDE). With the captured multiexposure image series, we first decompose images into base layers and detail layers to extract sharp details and fine details, respectively. The magnitude of the gradient of the image intensity is utilized to encourage smoothness at homogeneous regions in preference to inhomogeneous regions. Then, we have considered texture features of the base layer to generate a mask (i.e., decision mask) that guides the fusion of base layers in multiresolution fashion. Finally, well-exposed fused image is obtained that combines fused base layer and the detail layers at each scale across all the input exposures. Proposed algorithm skipping complex High Dynamic Range Image (HDRI) generation and tone mapping steps to produce detail preserving image for display on standard dynamic range display devices. Moreover, our technique is effective for blending flash/no-flash image pair and multifocus images, that is, images focused on different targets.
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Filippi, Anthony M., İnci Güneralp, Cesar R. Castillo, Andong Ma, Gernot Paulus, and Karl-Heinrich Anders. "Comparison of Image Endmember- and Object-Based Classification of Very-High-Spatial-Resolution Unmanned Aircraft System (UAS) Narrow-Band Images for Mapping Riparian Forests and Other Land Covers." Land 11, no. 2 (February 7, 2022): 246. http://dx.doi.org/10.3390/land11020246.
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Riparian forests are critical for carbon storage, biodiversity, and river water quality. There has been an increasing use of very-high-spatial-resolution (VHR) unmanned aircraft systems (UAS)-based remote sensing for riparian forest mapping. However, for improved riparian forest/zone monitoring, restoration, and management, an enhanced understanding of the accuracy of different classification methods for mapping riparian forests and other land covers at high thematic resolution is necessary. Research that compares classification efficacies of endmember- and object-based methods applied to VHR (e.g., UAS) images is limited. Using the Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm (EEA) jointly with the Spectral Angle Mapper (SAM) classifier, and a separate multiresolution segmentation/object-based classification method, we map riparian forests/land covers and compare the classification accuracies accrued via the application of these two approaches to narrow-band, VHR UAS orthoimages collected over two river reaches/riparian areas in Austria. We assess the effect of pixel size on classification accuracy, with 7 and 20 cm pixels, and evaluate performance across multiple dates. Our findings show that the object-based classification accuracies are markedly higher than those of the endmember-based approach, where the former generally have overall accuracies of >85%. Poor endmember-based classification accuracies are likely due to the very small pixel sizes, as well as the large number of classes, and the relatively small number of bands used. Object-based classification in this context provides for effective riparian forest/zone monitoring and management.
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Emadi, Mostafa, Ruhollah Taghizadeh-Mehrjardi, Ali Cherati, Majid Danesh, Amir Mosavi, and Thomas Scholten. "Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran." Remote Sensing 12, no. 14 (July 12, 2020): 2234. http://dx.doi.org/10.3390/rs12142234.
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Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines (SVM), artificial neural networks (ANN), regression tree, random forest (RF), extreme gradient boosting (XGBoost), and conventional deep neural network (DNN) for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 14.9% of SOC spatial variability followed by the normalized difference vegetation index (12.5%), day temperature index of moderate resolution imaging spectroradiometer (10.6%), multiresolution valley bottom flatness (8.7%) and land use (8.2%), respectively. Based on 10-fold cross-validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 0.59%, a root mean squared error of 0.75%, a coefficient of determination of 0.65, and Lin’s concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 3.71%, followed by the aquic (2.45%) and xeric (2.10%) classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN (hidden layers = 7, and size = 50) is a promising algorithm for handling large numbers of auxiliary data at a province-scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC base-line map and minimal uncertainty.
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Adhikari, Kabindra, Douglas R. Smith, Harold Collins, Chad Hajda, Bharat Sharma Acharya, and Phillip R. Owens. "Mapping Within-Field Soil Health Variations Using Apparent Electrical Conductivity, Topography, and Machine Learning." Agronomy 12, no. 5 (April 24, 2022): 1019. http://dx.doi.org/10.3390/agronomy12051019.
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High-resolution maps of soil health measurements could help farmers finetune input resources and management practices for profit maximization. Within-field soil heath variations can be mapped using local topography and apparent electrical conductivity (ECa) as predictors. To address these issues, a study was conducted in Texas Blackland Prairie soils with the following objectives: (i) to assess and map within-field soil health variations using machine learning; (ii) to evaluate the usefulness of topography and ECa as soil health predictors; and (iii) to quantify the relationship between ECa and soil health index and use ECa to estimate soil health spatial distribution. We collected 218 topsoil (0–15 cm) samples following a 35 m × 35 m grid design and analyzed for one-day CO2, organic C, organic N, and soil health index (SHI) based on the Haney Soil Health Tool. A random forest model was applied to predict and map those properties on a 5 m × 5 m grid where ECa, and terrain attributes were used as predictors. Furthermore, the empirical relationship between SHI and ECa was established and mapped across the field. Results showed that the study area was variable in terms of one-day CO2, organic C, organic N, SHI, and ECa distribution. The ECa, wetness index, multiresolution valley bottom flatness, and topographic position index were among the top predictors of soil health measurements. The model was sufficiently robust to predict one day CO2, organic C, organic N (R2 between 0.24–0.90), and SHI (R2 between 0.47–0.90). Overall, we observed a moderate to strong spatial dependency of soil health measurements which could impact within-field yield variability. The study confirmed the applicability of easy to obtain ECa as a good predictor of SHI, and the predicted maps at high resolution which could be useful in site-specific management decisions within these types of soils.
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Silveira, Eduarda Martiniano de Oliveira, Fausto Weimar Acerbi Júnior, José Márcio de Mello, and Inácio Thomaz Bueno. "Object-based change detection using semivariogram indices derived from NDVI images: The environmental disaster in Mariana, Brazil." Ciência e Agrotecnologia 41, no. 5 (September 2017): 554–64. http://dx.doi.org/10.1590/1413-70542017415009817.
Повний текст джерелаАнотація:
ABSTRACT Object-based change detection is a powerful analysis tool for remote sensing data, but few studies consider the potential of temporal semivariogram indices for mapping land-cover changes using object-based approaches. In this study, we explored and evaluated the performance of semivariogram indices calculated from remote sensing imagery, using the Normalized Differential Vegetation Index (NDVI) to detect changes in spatial features related to land cover caused by a disastrous 2015 dam failure in Brazil’s Mariana district. We calculated the NDVI from Landsat 8 images acquired before and after the disaster, then created objects by multiresolution segmentation analysis based on post-disaster images. Experimental semivariograms were computed within the image objects and semivariogram indices were calculated and selected by principal component analysis. We used the selected indices as input data to a support vector machine algorithm for classifying change and no-change classes. The selected semivariogram indices showed their effectiveness as input data for object-based change detection analysis, producing highly accurate maps of areas affected by post-dam-failure flooding in the region. This approach can be used in many other contexts for rapid and accurate assessment of such land-cover changes.
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Singh, Harbinder, Vinay Kumar, and Sunil Bhooshan. "Weighted Least Squares Based Detail Enhanced Exposure Fusion." ISRN Signal Processing 2014 (February 17, 2014): 1–18. http://dx.doi.org/10.1155/2014/498762.
Повний текст джерелаАнотація:
Many recent computational photography techniques play a significant role to avoid limitation of standard digital cameras to handle wide dynamic range of the real-world scenes, containing brightly and poorly illuminated areas. In many of these techniques, it is often desirable to fuse details from images captured at different exposure settings, while avoiding visual artifacts. In this paper we propose a novel technique for exposure fusion in which Weighted Least Squares (WLS) optimization framework is utilized for weight map refinement. Computationally simple texture features (i.e., detail layer extracted with the help of edge preserving filter) and color saturation measure are preferred for quickly generating weight maps to control the contribution from an input set of multiexposure images. Instead of employing intermediate High Dynamic Range (HDR) reconstruction and tone mapping steps, well-exposed fused image is generated for displaying on conventional display devices. A further advantage of the present technique is that it is well suited for multifocus image fusion. Simulation results are compared with a number of existing single resolution and multiresolution techniques to show the benefits of the proposed scheme for variety of cases.
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Pradhan, Biswajeet, Hossein Rizeei, and Abdinur Abdulle. "Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images." Remote Sensing 10, no. 11 (October 29, 2018): 1705. http://dx.doi.org/10.3390/rs10111705.
Повний текст джерелаАнотація:
This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds.
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Akono, Alain, and Emmanuel Tonyé. "A Comparative Study of Two Multiresolution Methods for the Mapping of a Large Coastal Zone Area from a Synthetic Aperture Radar Image." Geocarto International 19, no. 4 (December 2004): 23–31. http://dx.doi.org/10.1080/10106040408542324.
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Chen, Lin, Chunying Ren, Guangdao Bao, Bai Zhang, Zongming Wang, Mingyue Liu, Weidong Man, and Jiafu Liu. "Improved Object-Based Estimation of Forest Aboveground Biomass by Integrating LiDAR Data from GEDI and ICESat-2 with Multi-Sensor Images in a Heterogeneous Mountainous Region." Remote Sensing 14, no. 12 (June 7, 2022): 2743. http://dx.doi.org/10.3390/rs14122743.
Повний текст джерелаАнотація:
Accurate and effective mapping of forest aboveground biomass (AGB) in heterogeneous mountainous regions is a huge challenge but an urgent demand for resource managements and carbon storage monitoring. Conventional studies have related the plot-measured or LiDAR-based biomass to remote sensing data using pixel-based approaches. The object-based relationship between AGB and multi-source data from LiDAR, multi-frequency radar, and optical sensors were insufficiently studied. It deserves the further exploration that maps forest AGB using the object-based approach and combines LiDAR data with multi-sensor images, which has the smaller uncertainty of positional discrepancy and local heterogeneity, in heterogeneous mountainous regions. To address the improvement of mapping accuracy, satellite LiDAR data from GEDI and ICEsat-2, and images of ALOS-2 yearly mosaic L band SAR (Synthetic Aperture Radar), Sentinel-1 C band SAR, Sentinel-2 MSI, and ALOS-1 DSM were combined for pixel- and object-based forest AGB mapping in a vital heterogeneous mountainous forest. For the object-based approach, optimized objects during a multiresolution segmentation were acquired by the ESP (Estimation of the Scale Parameter) tool, and suitable predictors were selected using an algorithm named VSURF (Variable Selection Using Random Forests). The LiDAR variables at the footprint-level were extracted to connect field plots to the multi-sensor objects as a linear bridge. It was shown that forests’ AGB values varied by elevations with a mean value of 142.58 Mg/ha, ranging from 12.61 to 514.28 Mg/ha. The north slope with the lowest elevation (<1100 m) had the largest mean AGB, while the smallest mean AGB was located in the south slope with the altitude above 2000 m. Using independent validation samples, it was indicated by the accuracy comparison that the object-based approach performed better on the precision with relative improvement based on root-mean-square errors (RIRMSE) of 4.46%. The object-based approach also selected more optimized predictors and markedly decreased the prediction time than the pixel-based analysis. Canopy cover and height explained forest AGB with their effects on biomass varying according to the elevation. The elevation from DSM and variables involved in red-edge bands from MSI were the most contributive predictors in heterogeneous temperate forests. This study is a pioneering exploration of object-based AGB mapping by combining satellite data from LiDAR, MSI, and SAR, which offers an improved methodology for regional carbon mapping in the heterogeneous mountainous forests.
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Jawak, Shridhar D., Sagar F. Wankhede, Alvarinho J. Luis, and Keshava Balakrishna. "Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas." Remote Sensing 14, no. 17 (September 4, 2022): 4403. http://dx.doi.org/10.3390/rs14174403.
Повний текст джерелаАнотація:
Advancements in remote sensing have led to the development of Geographic Object-Based Image Analysis (GEOBIA). This method of information extraction focuses on segregating correlated pixels into groups for easier classification. This is of excellent use in analyzing very-high-resolution (VHR) data. The application of GEOBIA for glacier surface mapping, however, necessitates multiple scales of segmentation and input of supportive ancillary data. The mapping of glacier surface facies presents a unique problem to GEOBIA on account of its separable but closely matching spectral characteristics and often disheveled surface. Debris cover can induce challenges and requires additions of slope, temperature, and short-wave infrared data as supplements to enable efficient mapping. Moreover, as the influence of atmospheric corrections and image sharpening can derive variations in the apparent surface reflectance, a robust analysis of the effects of these processing routines in a GEOBIA environment is lacking. The current study aims to investigate the impact of three atmospheric corrections, Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods, viz., Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on the classification of surface facies using GEOBIA. This analysis is performed on VHR WorldView-2 imagery of selected glaciers in Ny-Ålesund, Svalbard, and Chandra–Bhaga basin, Himalaya. The image subsets are segmented using multiresolution segmentation with constant parameters. Three rule sets are defined: rule set 1 utilizes only spectral information, rule set 2 contains only spatial and contextual features, and rule set 3 combines both spatial and spectral attributes. Rule set 3 performs the best across all processing schemes with the highest overall accuracy, followed by rule set 1 and lastly rule set 2. This trend is observed for every image subset. Among the atmospheric corrections, DOS displays consistent performance and is the most reliable, followed by QUAC and FLAASH. Pansharpening improved overall accuracy and GS performed better than HCS. The study reports robust segmentation parameters that may be transferable to other VHR-based glacier surface facies mapping applications. The rule sets are adjusted across the processing schemes to adjust to the change in spectral characteristics introduced by the varying routines. The results indicate that GEOBIA for glacier surface facies mapping may be less prone to the differences in spectral signatures introduced by different atmospheric corrections but may respond well to increasing spatial resolution. The study highlighted the role of spatial attributes for mapping fine features, and in combination with appropriate spectral features may enhance thematic classification.
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Fu, Shengyu, B. Muralikrishnan, and J. Raja. "Engineering Surface Analysis With Different Wavelet Bases." Journal of Manufacturing Science and Engineering 125, no. 4 (November 1, 2003): 844–52. http://dx.doi.org/10.1115/1.1616947.
Повний текст джерелаАнотація:
Traditional surface texture analysis involves filtering surface profiles into different wavelength bands commonly referred to as roughness, waviness and form. The primary motivation in filtering surface profiles is to map each band to the manufacturing process that generated the part and the intended functional performance of the component. Current trends in manufacturing are towards tighter tolerances and higher performance standards that require close monitoring of the process. Thus, there is a need for finer bandwidths for process mapping and functional correlation. Wavelets are becoming increasingly popular tools for filtering profiles in an efficient manner into multiple bands. While they have broadly been demonstrated as having superior performance and capabilities than traditional filtering, fundamental issues such as choice of wavelet bases have remained unaddressed. The major contribution of this paper is to present the differences between wavelets in terms of the transmission characteristics of the associated filter banks, which is essential for surface analysis. This paper also reviews fundamental mathematics of wavelet theory necessary for applying wavelets to surface texture analysis. Wavelets from two basic categories—orthogonal wavelet bases and biorthogonal wavelet bases are studied. The filter banks corresponding to the wavelets are compared and multiresolution analysis on surface profiles is performed to highlight the applicability of this technique.