Bibliographies thématiques / High spatial and spectral remote sensing

Littérature scientifique sur le sujet « High spatial and spectral remote sensing »

Auteur : Grafiati

Publié le 11 mars 2023

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Articles de revues sur le sujet "High spatial and spectral remote sensing"

Rocchini, Duccio. « Ecological Remote Sensing : A Challenging Section on Ecological Theory and Remote Sensing ». Remote Sensing 13, n^o 5 (25 février 2021) : 848. http://dx.doi.org/10.3390/rs13050848.

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Han, Yanling, Cong Wei, Ruyan Zhou, Zhonghua Hong, Yun Zhang et Shuhu Yang. « Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification ». Mathematical Problems in Engineering 2020 (7 avril 2020) : 1–15. http://dx.doi.org/10.1155/2020/8065396.

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Sea ice is one of the most prominent marine disasters in high latitudes. Remote sensing technology provides an effective means for sea ice detection. Remote sensing sea ice images contain rich spectral and spatial information. However, most traditional methods only focus on spectral information or spatial information, and do not excavate the feature of spectral and spatial simultaneously in remote sensing sea ice images classification. At the same time, the complex correlation characteristics among spectra and small sample problem in sea ice classification also limit the improvement of sea ice classification accuracy. For this issue, this paper proposes a new remote sensing sea ice image classification method based on squeeze-and-excitation (SE) network, convolutional neural network (CNN), and support vector machines (SVMs). The proposed method designs 3D-CNN deep network so as to fully exploit the spatial-spectrum features of remote sensing sea ice images and integrates SE-Block into 3D-CNN in-depth network in order to distinguish the contributions of different spectra to sea ice classification. According to the different contributions of spectral features, the weight of each spectral feature is optimized by fusing SE-Block in order to further enhance the sample quality. Finally, information-rich and representative samples are chosen by combining the idea of active learning and input into SVM classifier, and this achieves superior classification accuracy of remote sensing sea ice images with small samples. In order to verify the effectiveness of the proposed method, we conducted experiments on three different data from Baffin Bay, Bohai Bay, and Liaodong Bay. The experimental results show that compared with other classical classification methods, the proposed method comprehensively considers the correlation among spectral features and the small samples problems and deeply excavates the spatial-spectrum characteristics of sea ice and achieves better classification performance, which can be effectively applied to remote sensing sea ice image classification.

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Wei, Lifei, Ming Yu, Yajing Liang, Ziran Yuan, Can Huang, Rong Li et Yiwei Yu. « Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery ». Remote Sensing 11, n^o 17 (27 août 2019) : 2011. http://dx.doi.org/10.3390/rs11172011.

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The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a precise crop classification method using spectral-spatial-location fusion based on conditional random fields (SSLF-CRF) for UAV-borne hyperspectral remote sensing imagery is proposed in this paper. The proposed method integrates the spectral information, the spatial context, the spatial features, and the spatial location information in the conditional random field model by the probabilistic potentials, providing complementary information for the crop discrimination from different perspectives. The experimental results obtained with two UAV-borne high spatial resolution hyperspectral images confirm that the proposed method can solve the problems of large-scale spatial information modeling and spectral variability, improving the classification accuracy for each crop type. This method has important significance for the precise classification of crops in hyperspectral remote sensing imagery.

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Duan, Meimei, et Lijuan Duan. « High Spatial Resolution Remote Sensing Data Classification Method Based on Spectrum Sharing ». Scientific Programming 2021 (20 décembre 2021) : 1–12. http://dx.doi.org/10.1155/2021/4356957.

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Existing remote sensing data classification methods cannot achieve the sharing of remote sensing image spectrum, leading to poor fusion and classification of remote sensing data. Therefore, a high spatial resolution remote sensing data classification method based on spectrum sharing is proposed. A page frame recovery algorithm (PFRA) is introduced to allocate the wireless spectrum resources in low-frequency band, and a dynamic spectrum sharing mechanism is designed between the primary and secondary users of remote sensing images. Based on this, D-S evidence theory is used to fuse high spatial resolution remote sensing data and correct the pixel brightness of the fused multispectral image. The initial data are normalized, the feature of spectral image is extracted, the convolution neural network classification model is constructed, and the remote sensing image is segmented. Experimental results show that the proposed method takes shorter time and has higher accuracy for high spatial resolution image segmentation. High spatial resolution remote sensing data classification is more efficient, and the accuracy of data classification and remote sensing image fusion are more ideal.

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Peng, Mingyuan, Lifu Zhang, Xuejian Sun, Yi Cen et Xiaoyang Zhao. « A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset ». Remote Sensing 12, n^o 23 (27 novembre 2020) : 3888. http://dx.doi.org/10.3390/rs12233888.

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With the growing development of remote sensors, huge volumes of remote sensing data are being utilized in related applications, bringing new challenges to the efficiency and capability of processing huge datasets. Spatiotemporal remote sensing data fusion can restore high spatial and high temporal resolution remote sensing data from multiple remote sensing datasets. However, the current methods require long computing times and are of low efficiency, especially the newly proposed deep learning-based methods. Here, we propose a fast three-dimensional convolutional neural network-based spatiotemporal fusion method (STF3DCNN) using a spatial-temporal-spectral dataset. This method is able to fuse low-spatial high-temporal resolution data (HTLS) and high-spatial low-temporal resolution data (HSLT) in a four-dimensional spatial-temporal-spectral dataset with increasing efficiency, while simultaneously ensuring accuracy. The method was tested using three datasets, and discussions of the network parameters were conducted. In addition, this method was compared with commonly used spatiotemporal fusion methods to verify our conclusion.

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Imanian, A., M. H. Tangestani et A. Asadi. « INVESTIGATION OF SPECTRAL CHARACTERISTICS OF CARBONATE ROCKS – A CASE STUDY ON POSHT MOLEH MOUNT IN IRAN ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (18 octobre 2019) : 553–57. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-553-2019.

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Abstract. Recent developments in the image processing approaches and the availability of multi and/or hyper spectral remote sensing data with high spectral, spatial and temporal resolutions have made remote sensing technique of great interest in investigations of geological sciences. One of the biggest advantage of the application of remote sensing in geology is recognizing the type of unknown rocks and minerals. In this study, an investigation on spectral features of carbonate rocks (i.e. calcite, dolomite, and dolomitized calcite) were done in terms of main absorptions, the reasons of those absorptions and comparison of these absorption with Johns Hopkins University (JHU) spectral library and laboratory spectra of Analytical Spectral Devices (ASD) instrument. For this purpose, we used the VNIR and SWIR bands of ASTER and OLI datasets. Finally, we applied the Spectral Analyst Algorithm in order to comparison between the obtained spectra from ASTER dataset and carbonate spectra of JHU spectral library.

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Xu, Qingsong, Xin Yuan, Chaojun Ouyang et Yue Zeng. « Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images ». Remote Sensing 12, n^o 21 (24 octobre 2020) : 3501. http://dx.doi.org/10.3390/rs12213501.

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Unlike conventional natural (RGB) images, the inherent large scale and complex structures of remote sensing images pose major challenges such as spatial object distribution diversity and spectral information extraction when existing models are directly applied for image classification. In this study, we develop an attention-based pyramid network for segmentation and classification of remote sensing datasets. Attention mechanisms are used to develop the following modules: (i) a novel and robust attention-based multi-scale fusion method effectively fuses useful spatial or spectral information at different and same scales; (ii) a region pyramid attention mechanism using region-based attention addresses the target geometric size diversity in large-scale remote sensing images; and (iii) cross-scale attention in our adaptive atrous spatial pyramid pooling network adapts to varied contents in a feature-embedded space. Different forms of feature fusion pyramid frameworks are established by combining these attention-based modules. First, a novel segmentation framework, called the heavy-weight spatial feature fusion pyramid network (FFPNet), is proposed to address the spatial problem of high-resolution remote sensing images. Second, an end-to-end spatial-spectral FFPNet is presented for classifying hyperspectral images. Experiments conducted on ISPRS Vaihingen and ISPRS Potsdam high-resolution datasets demonstrate the competitive segmentation accuracy achieved by the proposed heavy-weight spatial FFPNet. Furthermore, experiments on the Indian Pines and the University of Pavia hyperspectral datasets indicate that the proposed spatial-spectral FFPNet outperforms the current state-of-the-art methods in hyperspectral image classification.

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NanLan, Wang, et Zeng Xiaoyong. « Hyperspectral Data Classification Algorithm considering Spatial Texture Features ». Mobile Information Systems 2022 (22 mars 2022) : 1–11. http://dx.doi.org/10.1155/2022/9915809.

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As a cutting-edge technology, hyperspectral remote sensing has been widely applied in many fields, including agricultural production, mineral identification, target detection, disaster warning, military reconnaissance, and urban planning. The collected hyperspectral data have high spectral resolution and spatial resolution and are characterized by a large amount of information, redundancy, and high dimension. At the same time, there is a strong correlation between the bands. Therefore, hyperspectral data not only provides rich information but also brings great challenges for subsequent processing. Hyperspectral image classification is a hot issue in remote sensing information processing. Traditional hyperspectral remote sensing image classification methods only use the spectral features of the image without considering the spatial features of each pixel in the hyperspectral remote sensing image. In this paper, a hyperspectral image classification method is proposed not only considering spectral features but also considering texture features. This method jointly considers both these features. Firstly, six texture features contributing a lot to each pixel of hyperspectral remote sensing image are extracted by using a gray level cooccurrence matrix, and then, the spectral features of each pixel in neighbor are combined to form the texture-spectral features. Finally, the classification experiment of the Indian Pines and Pavia University scene is carried out based on a support vector machine and extreme random tree algorithm, and the obtained results show that the proposed method achieves higher classification performance than the traditional method.

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Zhao, Rui, et Shihong Du. « Spectral-Spatial Residual Network for Fusing Hyperspectral and Panchromatic Remote Sensing Images ». Remote Sensing 14, n^o 3 (8 février 2022) : 800. http://dx.doi.org/10.3390/rs14030800.

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Fusing hyperspectral and panchromatic remote sensing images can obtain the images with high resolution in both spectral and spatial domains. In addition, it can complement the deficiency of high-resolution hyperspectral and panchromatic remote sensing images. In this paper, a spectral–spatial residual network (SSRN) model is established for the intelligent fusion of hyperspectral and panchromatic remote sensing images. Firstly, the spectral–spatial deep feature branches are built to extract the representative spectral and spatial deep features, respectively. Secondly, an enhanced multi-scale residual network is established for the spatial deep feature branch. In addition, an enhanced residual network is established for the spectral deep feature branch This operation is adopted to enhance the spectral and spatial deep features. Finally, this method establishes the spectral–spatial deep feature simultaneity to circumvent the independence of spectral and spatial deep features. The proposed model was evaluated on three groups of real-world hyperspectral and panchromatic image datasets which are collected with a ZY-1E sensor and are located at Baiyangdian, Chaohu and Dianchi, respectively. The experimental results and quality evaluation values, including RMSE, SAM, SCC, spectral curve comparison, PSNR, SSIM ERGAS and Q metric, confirm the superior performance of the proposed model compared with the state-of-the-art methods, including AWLP, CNMF, GIHS, MTF_GLP, HPF and SFIM methods.

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Shi, Xue, Yu Wang, Yu Li et Shiqing Dou. « Remote Sensing Image Segmentation Based on Hierarchical Student’s-t Mixture Model and Spatial Constrains with Adaptive Smoothing ». Remote Sensing 15, n^o 3 (1 février 2023) : 828. http://dx.doi.org/10.3390/rs15030828.

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Image segmentation is an important task in image processing and analysis but due to the same ground object having different spectra and different ground objects having similar spectra, segmentation, particularly on high-resolution remote sensing images, can be significantly challenging. Since the spectral distribution of high-resolution remote sensing images can have complex characteristics (e.g., asymmetric or heavy-tailed), an innovative image segmentation algorithm is proposed based on the hierarchical Student’s-t mixture model (HSMM) and spatial constraints with adaptive smoothing. Considering the complex distribution of spectral intensities, the proposed algorithm constructs the HSMM to accurately build the statistical model of the image, making more reasonable use of the spectral information and improving segmentation accuracy. The component weight is defined by the attribute probability of neighborhood pixels to overcome the influence of image noise and make a simple and easy-to-implement structure. To avoid the effects of artificially setting the smoothing coefficient, the gradient optimization method is used to solve the model parameters, and the smoothing coefficient is optimized through iterations. The experimental results suggest that the proposed HSMM can accurately model asymmetric, heavy-tailed, and bimodal distributions. Compared with traditional segmentation algorithms, the proposed algorithm can effectively overcome noise and generate more accurate segmentation results for high-resolution remote sensing images.

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Thèses sur le sujet "High spatial and spectral remote sensing"

Jay, Steven Charles. « Detection of leafy spurge (Euphorbia esula) using affordable high spatial, spectral and temporal resolution imagery ». Thesis, Montana State University, 2010. http://etd.lib.montana.edu/etd/2010/jay/JayS0510.pdf.

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Leafy spurge is a designated noxious weed. Accurate mapping and monitoring of this species are needed to understand leafy spurge's extent and spread. Current methods are based on ground crews who survey patches. Development of an affordable technique to map and monitor leafy spurge would contribute to the control of this species. High spatial, temporal, and spectral resolution imagery was used to classify the amount of leafy spurge present with ground and aerial-based imagery. A proof of concept study was performed in 2008 using ground-based images of an area infested with leafy spurge. This proof of concept project guided the development of the methods to be used for the 2009 aerial portion of the study. Thirty-five randomly selected reference points were selected in a range area in southwest Montana. These reference points were ground surveyed to record the density of leafy spurge in a 0.5-m radius area around the reference point. Images were captured approximately 108-m from the study area and classified using random forest classification. Multiple images were collected throughout the summer in order to determine at which time period leafy spurge is most easily detected. A classification using multiple image dates was also performed to determine if a time series of images improves classification. Single date accuracies were highest late in the summer with the highest single date classification achieving 83% accuracy. The multiple date classification significantly increased overall accuracy. Several aerial images were acquired in southwest Montana over the 2009 summer. Fifty randomly selected 2-m x 2-m reference areas were surveyed for percent cover of leafy spurge as well as several other variables. Aerial images were collected at flight elevations between 300-m to 460-m. Classifications were performed using random forest classifier, and both single date and multiple date classifications were performed. Leafy spurge was most accurately detected early and late in the growing season, and significant classification accuracy increases were observed with the multiple date classification. Single date accuracies achieved 90% accuracy in early June, while multiple date classifications achieved over 96% accuracy.

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Arkun, Sedat. « Hyperspectral remote sensing and the urban environment : a study of automated urban feature extraction using a CASI image of high spatial and spectral resolution ». Title page, contents, research aims and abstract only, 1999. http://web4.library.adelaide.edu.au/theses/09ARM/09arma721.pdf.

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Lee, Jong Yeol. « Integrating spatial and spectral information for automatic feature identification in high resolution remotely sensed images ». Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1600.

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Thesis (Ph. D.)--West Virginia University, 2000.
Title from document title page. Document formatted into pages; contains x, 132 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references (p. 124-132).

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Kaufman, Jason R. « Spatial-Spectral Feature Extraction on Pansharpened Hyperspectral Imagery ». Ohio University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1408706595.

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Mitri, Georges Habib. « An investigation in the use of advanced remote sensing and geographic information system techniques for post-fire impact assessment on vegetation ». Doctoral thesis, Università degli studi di Trieste, 2008. http://hdl.handle.net/10077/2662.

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2006/2007
Gli incendi boschivi rappresentano uno dei maggiori problemi ambientali nella regione Mediterranea con vaste superfici colpite ogni estate. Una stima dell’impatto ambientale degli incendi (a breve e a lungo termine) richiede la raccolta di informazioni accurate post-incendio relative al tipo di incendio, all’intensità, alla rigenerazione forestale ed al ripristino della vegetazione. L’utilizzo di tecniche avanzate di telerilevamento può fornire un valido strumento per lo studio di questi fenomeni. L’importanza di queste ricerche è stata più volte sottolineata dalla Commissione Europea che si è concentrata sullo studio degli incendi boschivi ed il loro effetto sulla vegetazione attraverso lo sviluppo di adeguati metodi di stima dell’impatto e di mitigazione. Scopo di questo lavoro è la stima dell’impatto post-incendio sulla vegetazione in ambiente Mediterraneo per mezzo di immagini satellitari ad alta risoluzione, di rilievi a terra e mediante tecniche avanzate di analisi dei dati. Il lavoro ha riguardato lo sviluppo di un sistema per l’integrazione di dati telerilevati ad altissima risoluzione spaziale e spettrale. Per la stima dell’impatto a breve termine, un modello di classificazione ad oggetti è stato sviluppato utilizzando immagini Ikonos ad altissima risoluzione spaziale per cartografare il tipo di incendio, differenziando l’incendio radente dall’incendio di chioma. I risultati mostrano che la classificazione ad oggetti potrebbe essere utilizzata per distinguere con elevata accuratezza (87% di accuratezza complessiva) le due tipologie di incendio, in particolare nei boschi Mediterranei aperti. È stata inoltre valutata la capacità della classificazione ad oggetti di distinguere e cartografare tre livelli di intensità del fuoco utilizzando le immagini Ikonos e l’accuratezza del risultato è stimata all’ 83%. Per la stima dell’impatto a lungo termine, la mappatura della rigenerazione post-incendio (pino) e la ripresa della vegetazione arbustiva sono state valutate mediante tre approcci: 1) la classificazione ad oggetti di immagini ad altissima risoluzione QuickBird che ha permesso di mappare la ripresa della vegetazione e l’impatto sulla copertura a seguito dell’incendio distinguendo due livelli di intensità dell’incendio (accuratezza della classificazione 86%). 2) l’analisi statistica di dati iperspettrali rilevati in campo che ha permesso una riduzione del 97% del volume di dati e la selezione delle migliori 14 bande per discriminare l’età e le specie di pino e le 18 migliori bande per la caratterizzazione delle specie arbustive. Successivamente, i dati iperspettrali Hyperion sono stati utlizzati per mappare la rigenerazione forestale e la ripresa della vegetazione. L’accuratezza complessiva della classificazione è stata del 75.1% considerando due diverse specie di pino ed altre specie vegetali. 3) una classificazione ad oggetti che ha combinato l’analisi dei dati QuickBird ed Hyperion. Si è registrato un aumento dell’accuratezza della classificazione pari all’8.06% rispetto all’utilizzo dei soli dati Hyperion. Complessivamente, si osserva che strumenti avanzati di telerilevamento consentono di raccogliere le informazioni relative alle aree incendiate, la rigenerazione forestale e la ripresa della vegetazione in modo accurato e vantaggioso in termini di costi e tempi.
Forest fires are a major environmental problem in the Mediterranean region, where large areas are affected each summer. An assessment of the environmental impact of forest fires (in the short-term and in the long-term) requires the collection of accurate and detailed post-fire information related to fire type, fire severity, forest regeneration and vegetation recovery. Advanced tools in remote sensing provide a powerful tool for the study of this phenomenon. The importance of this work was often emphasized by the European Commission, which focused on the studying of forest fires and their effect on vegetation through the development of appropriate impact assessment and mitigation methods. The aim of this study was to assess the post-fire impact on vegetation in a Mediterranean environment by employing high quality satellite and field data and by using advanced data processing techniques. The work entailed the development of a whole system integrating very high spatial and spectral resolution remotely sensed data. For short-term impact assessment, an object-oriented model was developed using very high spatial resolution Ikonos imagery to map the type of fire, namely, canopy fire and surface fire. The results showed that object-oriented classification could be used to accurately distinguish and map areas of surface and crown fire spread (overall accuracy of 87%), especially that occurring in open Mediterranean forests. Also, the performance of object-based classification in mapping three levels of fire severity by employing high spatial resolution Ikonos imagery was evaluated, and accuracy of the obtained results was estimated to be 83%. As for long-term impact assessment, the mapping of post-fire forest regeneration (pine) and vegetation recovery (shrub) was performed by following three different approaches. First, the developed object-based classification of QuickBird (very high spatial resolution) allowed post-fire vegetation recovery and survival mapping of canopy within two different fire severity levels (86% of classification accuracy). The main effect of fire has been to create a more homogeneous landscape. Second, statistical analysis of field hyperspectral data allowed a 97% reduction in data volume and recommended 14 best narrowbands to discriminate among pine trees (age and species) and 18 bands that best characterize the different shrub species. Then, hyperspectral Hyperion was employed for mapping post-fire forest regeneration and vegetation recovery. The overall classification accuracy was found to be 75.81% when mapping two different regenerated pine species and other species of vegetation recovery. Third, an object-oriented combined analysis of QuickBird and Hyperion was investigated for the same objective. An improvement in classification accuracy of 8.06% was recorded when combining both Hyperion and QuickBird imageries than by using only the Hyperion image. Overall, it was observed that advanced tools in remote sensing provided the necessary means for gathering information about the burned areas, the regenerated forests and the recovered vegetations in a successful and a timely/cost effective manner.
XX Ciclo
1977

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Sheffield, Kathryn Jane, et kathryn sheffield@dpi vic gov au. « Multi-spectral remote sensing of native vegetation condition ». RMIT University. Mathematical and Geospatial Sciences, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091110.112816.

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Native vegetation condition provides an indication of the state of vegetation health or function relative to a stated objective or benchmark. Measures of vegetation condition provide an indication of the vegetation's capacity to provide habitat for a range of species and ecosystem functions through the assessment of selected vegetation attributes. Subsets of vegetation attributes are often combined into vegetation condition indices or metrics, which are used to provide information for natural resource management. Despite their value as surrogates of biota and ecosystem function, measures of vegetation condition are rarely used to inform biodiversity assessments at scales beyond individual stands. The extension of vegetation condition information across landscapes, and approaches for achieving this, using remote sensing technologies, is a key focus of the work presented in this thesis. The aim of this research is to assess the utility of multi-spectral remotely sensed data for the recovery of stand-level attributes of native vegetation condition at landscape scales. The use of remotely sensed data for the assessment of vegetation condition attributes in fragmented landscapes is a focus of this study. The influence of a number of practical issues, such as spatial scale and ground data sampling methodology, are also explored. This study sets limitations on the use of this technology for vegetation condition assessment and also demonstrates the practical impact of data quality issues that are frequently encountered in these types of applied integrated approaches. The work presented in this thesis demonstrates that while some measures of vegetation condition, such as vegetation cover and stem density, are readily recoverable from multi-spectral remotely sensed data, others, such as hollow-bearing trees and log length, are not easily derived from this type of data. The types of information derived from remotely sensed data, such as texture measures and vegetation indices, that are useful for vegetation condition assessments of this nature are also highlighted. The utility of multi-spectral remotely sensed data for the assessment of stand-level vegetation condition attributes is highly dependent on a number of factors including the type of attribute being measured, the characteristics of the vegetation, the sensor characteristics (i.e. the spatial, spectral, temporal, and radiometric resolution), and other spatial data quality considerations, such as site homogeneity and spatial scale. A series of case studies are presented in this thesis that explores the effects of these factors. These case studies demonstrate the importance of different aspects of spatial data and how data manipulation can greatly affect the derived relationships between vegetation attributes and remotely sensed data. The work documented in this thesis provides an assessment of what can be achieved from two sources of multi-spectral imagery in terms of recovery of individual vegetation attributes from remotely sensed data. Potential surrogate measures of vegetation condition that can be derived across broad scales are identified. This information could provide a basis for the development of landscape scale multi-spectral remotely sensed based vegetation condition assessment approaches, supplementing information provided by established site-based vegetation condition assessment approaches.

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Song, Shi. « The Spectral Signature of Cloud Spatial Structure in Shortwave Radiation ». Thesis, University of Colorado at Boulder, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10151129.

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In this thesis, we aim to systematically understand the relationship between cloud spatial structure and its radiation imprints, i.e., three-dimensional (3D) cloud effects, with the ultimate goal of deriving accurate radiative energy budget estimates from space, aircraft, or ground-based observations under spatially inhomogeneous conditions. By studying the full spectral information in the measured and modeled shortwave radiation fields of heterogeneous cloud scenes sampled during aircraft field experiments, we find evidence that cloud spatial structure reveals itself through spectral signatures in the associated irradiance and radiance fields in the near-ultraviolet and visible spectral range.

The spectral signature of 3D cloud effects in irradiances is apparent as a domain- wide, consistent correlation between the magnitude and spectral dependence of net horizontal photon transport. The physical mechanism of this phenomenon is molecular scattering in conjunction with cloud heterogeneity. A simple parameterization with a single parameter ϵ is developed, which holds for individual pixels and the domain as a whole. We then investigate the impact of scene parameters on the discovered correlation and find that it is upheld for a wide range of scene conditions, although the value of ϵ varies from scene to scene.

The spectral signature of 3D cloud effects in radiances manifests itself as a distinct relationship between the magnitude and spectral dependence of reflectance, which cannot be reproduced in the one-dimensional (1D) radiative transfer framework. Using the spectral signature in radiances and irradiances, it is possible to infer information on net horizontal photon transport from spectral radiance perturbations on the basis of pixel populations in sub-domains of a cloud scene.

We show that two different biases need to be considered when attempting radiative closure between measured and modeled irradiance fields below inhomogeneous cloud fields: the remote sensing bias (affecting cloud radiances and thus retrieved properties of the inhomogeneous scene) and the irradiance bias (ignoring 3D effects in the calculation of irradiance fields from imagery-based cloud retrievals). The newly established relationships between spatial and spectral structure lay the foundation for first-order corrections for these 3D biases within a 1D framework, once the correlations are explored on a more statistical basis.

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Suliman, Ahmed Saeid Ahmed. « Spectral and spatial variability of the soils on the Maricopa Agricultural Center, Arizona ». Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184678.

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Dry and wet fine earth spectral measurements were made on the Ap soil surface horizons on the Maricopa Agricultural Center by using a Barnes Modular Multiband Radiometer. Three subsets were used in the analyses 552, 101 and 11. There were three soil series, Casa Grande, Shontik and Trix, four soil mapping units, and three texture classes identified on the farm. The wet soil condition reduced the amplitude of the spectral curves over the entire spectrum range (0.45 to 2.35 μm). The spectral curves were statistically related to the soil mapping units to determine if the soil mapping units and texture classes could be separated. The wet soil condition and the smaller sample size increased the correct classification percentages for soil mapping units and texture classes. LSD tests showed there were significant differences between these groups. Simple- and Multiple-linear regression analysis were used to relate some soil physical (sand, silt and clay contents and color components) and chemical (iron oxide, organic carbon and calcium carbonate contents) to soil spectral responses in the seven bands under dry and wet conditions. There were high correlations levels among the spectral bands showing an overlap of spectral information. Generally, the red (MMR3) and near-infrared (MMR4) bands had the highest correlations with the studied soil properties under dry and wet conditions. Usually, the wet soil condition resulted in higher correlations than that for the dry soil condition over the total spectrum range. The predictive equations for sand, silt and clay and iron oxide contents were satisfactory. For organic carbon and color components, the greatest success was achieved when variation in spectral response within individual samples are smaller than that between soil mapping unit group averages. There was a poor relation between calcium carbonate and spectral response. A comparison of multi-level remotely sensed data collected by SPOT, aircraft, and ground instruments showed a strong agreement among the data sets, which correlated well to fine earth data, except for the SPOT data. Rough soil surfaces showed a reduction in reflectance altitude compared to laser level, and it appears to be directly proportional to the percent shadow in the viewing area measured by SPOT satellite and aircraft.

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Alam, Fahim Irfan. « Deep Feature Learning for Spectral-Spatial Classification of Hyperspectral Remote Sensing Images ». Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386535.

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The recent advances in aerial- and satellite-based hyperspectral imaging sensor technologies have led to an increased availability of Earth's images with high spatial and spectral resolution, which opened the door to a large range of important applications. Hyperspectral imaging records detailed spectrum of the received light in each spatial position in the image, in which each pixel contains a highly detailed representation of the reflectance of the materials present on the ground, and a better characterization in terms of geometrical details. Since different substances exhibit different spectral signatures, the abundance of informative content conveyed in the hyperspectral images permits an improved characterization of different land coverage. Therefore, hyperspectral imaging emerged as a well-suited technology for accurate image classi fication in remote sensing. In spite of that, a signi ficantly increased complexity of the analysis introduces a series of challenges that need to be addressed on a serious note. In order to fully exploit the potential offered by these sensors, there is a need to develop accurate and effective models for spectral-spatial analysis of the recorded data. This thesis aims at presenting novel strategies for the analysis and classifi cation of hyperspectral remote sensing images, placing the focal point on the investigation on deep networks for the extraction and integration of spectral and spatial information. Deep learning has demonstrated cutting-edge performances in computer vision, particularly in object recognition and classi cation. It has also been successfully adopted in hyperspectral remote sensing domain as well. However, it is a very challenging task to fully utilize the massive potential of deep models in hyperspectral remote sensing applications since the number of training samples is limited which limits the representation capability of a deep model. Furthermore, the existing architectures of deep models need to be further investigated and modifi ed accordingly to better complement the joint use of spectral and spatial contents of hyperspectral images. In this thesis, we propose three different deep learning-based models to effectively represent spectral-spatial characteristics of hyperspectral data in the interest of classifi cation of remote sensing images. Our first proposed model focuses on integrating CRF and CNN into an end-to-end learning framework for classifying images. Our main contribution in this model is the introduction of a deep CRF in which the CRF parameters are computed using CNN and further optimized by adopting piecewise training. Furthermore, we address the problem of over fitting by employing data augmentation techniques and increased the size of the training samples for training deep networks. Our proposed 3DCNN-CRF model can be trained to fully exploit the usefulness of CRF in the context of classi fication by integrating it completely inside of a deep model. Considering that the separation of constituent materials and their abundances provide detailed analysis of the data, our second algorithm investigates the potential of using unmixing results in deep models to classify images. We extend an existing region based structure preserving non-negative matrix factorization method to estimate groups of spectral bands with the goal to capture subtle spectral-spatial distribution from the image. We subsequently use these important unmixing results as input to generate superpixels, which are further represented by kernel density estimated probability distribution function. Finally, these abundance information-guided superpixels are directly supplied into a deep model in which the inference is implicitly formulated as a recurrent neural network to perform the eventual classifi cation. Finally, we perform a detailed investigation on the possibilities of adopting generative adversarial models into hyperspectral image classifi cation. We present a GAN-based spectral-spatial method that primarily focuses on signifi cantly improving the multiclass classi cation ability of the discriminator of GAN models. In this context, we propose to adopt the triplet constraint property and extend it to build a useful feature embedding for remote sensing images for use in classi cation. Furthermore, our proposed Triplet- 3D-GAN model also includes feedback from discriminator's intermediate features to improve the quality of the generator's sample generation process.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Garner, Jamada J. « Scene classification using high spatial resolution multispectral data ». Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FGarner.pdf.

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Livres sur le sujet "High spatial and spectral remote sensing"

He, Yuhong, et Qihao Weng, dir. High Spatial Resolution Remote Sensing. Boca Raton, FL : Taylor & Francis, 2018. : CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196.

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Pedram, Ghamisi, dir. Spectral-spatial classififcation of hyperspectral remote sensing images. Boston : Artech House, 2015.

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Chedin, Alain, Moustafa T. Chahine et Noëlle A. Scott, dir. High Spectral Resolution Infrared Remote Sensing for Earth’s Weather and Climate Studies. Berlin, Heidelberg : Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84599-4.

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Alain, Chedin, Chahine Moustafa T, Scott Noëlle A. 1941-, North Atlantic Treaty Organization. Scientific Affairs Division. et NATO Advanced Research Workshop on High Spectral Resolution Infrared Remote Sensing for Earth's Weather and Climate Studies (1992 : Paris, France), dir. High spectral resolution infrared remote sensing for earth's weather and climate studies. Berlin : Springer, 1993.

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S, Carlson G., et George C. Marshall Space Flight Center., dir. Inter-comparison of wildfire and high-resolution interferometer sounder (HIS) data from STORM-FEST : An investigation of wildfire spectral channel discrepancies. [Marshall Space Flight Center, Ala.] : National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1994.

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Joanne, White, Mountain Pine Beetle Initiative (Canada) et Pacific Forestry Centre, dir. Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery. Victoria, B.C : Canadian Forest Service, Pacific Forestry Centre, 2005.

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J, Tucker Compton, Dye Dennis G et Goddard Space Flight Center, dir. North American vegetation patterns observed with the NOAA-7 Advanced Very High Resolution Radiometer. [Greenbelt, Md : National Aeronautics and Space Administration, Goddard Space Flight Center, 1985.

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Hlavka, Christine A. Unmixing AVHRR imagery to assess clearcuts and forest regrowth in Oregon. [Washington, D.C : National Aeronautics and Space Administration, 1995.

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Chapitres de livres sur le sujet "High spatial and spectral remote sensing"

Yang, Jian. « Suitable Spectral Mixing Space Selection for Linear Spectral Unmixing of Fine-Scale Urban Imagery ». Dans High Spatial Resolution Remote Sensing, 187–200. Boca Raton, FL : Taylor & Francis, 2018. : CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-9.

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Singh, Kunwar K., Lindsey Smart et Gang Chen. « LiDAR and Spectral Data Integration for Coastal Wetland Assessment ». Dans High Spatial Resolution Remote Sensing, 71–88. Boca Raton, FL : Taylor & Francis, 2018. : CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-4.

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Fu, Wenxue, Jianwen Ma, Pei Chen et Fang Chen. « Remote Sensing Satellites for Digital Earth ». Dans Manual of Digital Earth, 55–123. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9915-3_3.

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Abstract The term remote sensing became common after 1962 and generally refers to nonintrusive Earth observation using electromagnetic waves from a platform some distance away from the object of the study. After more than five decades of development, humankind can now use different types of optical and microwave sensors to obtain large datasets with high precision and high resolution for the atmosphere, ocean, and land. The frequency of data acquisition ranges from once per month to once per minute, the spatial resolution ranges from kilometer to centimeter scales, and the electromagnetic spectrum covers wavebands ranging from visible light to microwave wavelengths. Technological progress in remote sensing sensors enables us to obtain data on the global scale, remarkably expanding humanity’s understanding of its own living environment from spatial and temporal perspectives, and provides an increasing number of data resources for Digital Earth. This chapter introduces the developments and trends in remote sensing satellites around the world.

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van der Meer, Freek. « Image classification through spectral unmixing ». Dans Spatial Statistics for Remote Sensing, 185–93. Dordrecht : Springer Netherlands, 1999. http://dx.doi.org/10.1007/0-306-47647-9_11.

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Bishop, Michael P., Muthukumar V. Bagavathiannan, Dale A. Cope, Da Huo, Seth C. Murray, Jeffrey A. Olsenholler, William L. Rooney et al. « High-Resolution UAS Imagery in Agricultural Research Concepts, Issues, and Research Directions ». Dans High Spatial Resolution Remote Sensing, 3–32. Boca Raton, FL : Taylor & Francis, 2018. : CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-1.

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Zhang, Xiuyuan, Shihong Du et Dongping Ming. « Segmentation Scale Selection in Geographic Object-Based Image Analysis ». Dans High Spatial Resolution Remote Sensing, 201–28. Boca Raton, FL : Taylor & Francis, 2018. : CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-10.

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Seltzer, Joshua, Michael Guerzhoy et Monika Havelka. « Computer Vision Methodologies for Automated Processing of Camera Trap Data ». Dans High Spatial Resolution Remote Sensing, 229–42. Boca Raton, FL : Taylor & Francis, 2018. : CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-11.

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Lu, Bing, et Yuhong He. « UAV-Based Multispectral Images for Investigating Grassland Biophysical and Biochemical Properties ». Dans High Spatial Resolution Remote Sensing, 245–59. Boca Raton, FL : Taylor & Francis, 2018. : CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-12.

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Tong, Alexander, Bing Lu et Yuhong He. « Inversion of a Radiative Transfer Model Using Hyperspectral Data for Deriving Grassland Leaf Chlorophyll ». Dans High Spatial Resolution Remote Sensing, 261–82. Boca Raton, FL : Taylor & Francis, 2018. : CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-13.

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Mui, Amy B. « Wetland Detection Using High Spatial Resolution Optical Remote Sensing Imagery ». Dans High Spatial Resolution Remote Sensing, 283–305. Boca Raton, FL : Taylor & Francis, 2018. : CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-14.

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Actes de conférences sur le sujet "High spatial and spectral remote sensing"

Winter, Edwin M. « Classification of vegetation types using a high-spectral and spatial resolution hyperspectral sensor ». Dans Remote Sensing, sous la direction de Hiroyuki Fujisada. SPIE, 1998. http://dx.doi.org/10.1117/12.333632.

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Ortenberg, Fred, V. Adasko, R. Salichov, V. Antoshkin et R. Muchamediarov. « Infrared scanning radiometer of high spatial and spectral resolution for the Meteor-3 satellite ». Dans Satellite Remote Sensing, sous la direction de Anton Kohnle et Adam D. Devir. SPIE, 1994. http://dx.doi.org/10.1117/12.197357.

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Matteoli, Stefania, Francesca Carnesecchi, Marco Diani, Giovanni Corsini et Leandro Chiarantini. « Comparative analysis of hyperspectral anomaly detection strategies on a new high spatial and spectral resolution data set ». Dans Remote Sensing, sous la direction de Lorenzo Bruzzone. SPIE, 2007. http://dx.doi.org/10.1117/12.738062.

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Yang, He, Ben Ma, Qian Du et Liangpei Zhang. « Comparison of spectral-spatial classification for urban hyperspectral imagery with high resolution ». Dans 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137604.

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Lazcano López, Raquel, Daniel Madroñal Quintín, Samuel Ortega, Himar Fabelo Gómez, Ruben Salvador, Gustavo M. Callicó, Eduardo Juárez Martínez et César Sanz Álvaro. « Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL ». Dans High-Performance Computing in Geoscience and Remote Sensing, sous la direction de Bormin Huang, Sebastián López et Zhensen Wu. SPIE, 2017. http://dx.doi.org/10.1117/12.2279613.

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Zhao, Ji, Yanfei Zhong, Hong Shu et Liangpei Zhang. « Spectral-spatial conditional random field classifier with location cues for high spatial resolution imagery ». Dans IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326797.

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Clough, Shepard A. « Retrieval of Atmospheric State Parameters from High Resolution Spectral Radiance Data ». Dans Optical Remote Sensing of the Atmosphere. Washington, D.C. : Optica Publishing Group, 1993. http://dx.doi.org/10.1364/orsa.1993.tua.1.

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The retrieval of temperature and molecular trace gas profiles from spectral radiance data is an important but complex problem in optimization. In this presentation we are concerned with profile retrievals along the radiating path, examples of which include zenith remote sensing from the surface and nadir remote sensing from space. The latter application is of particular interest in the context of obtaining global maps of trace molecular species using high resolution spectral radiance data from spectrometers orbiting in space. The retrieval method to be explored is the method of non-linear least squares in which the state parameters are optimized to obtain the minimum variance of the spectral residuals, Rodgers (1976) and Clough et al. (1990,1991). The residuals are defined as the spectral differences between the measurement and the calculated result from an appropriate forward radiative transfer model. In the present case we are concerned with high resolution so that the objective of the retrieval process is to obtain for a given spatial resolution, the most probable profile values for the state parameter of interest.

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Luo, Jiancheng, Yongwei Sheng, Zhanfeng Shen et Junli Li. « High-precise water extraction based on spectral-spatial coupled remote sensing information ». Dans IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5648978.

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Cavalli, Rosa Maria, Lorenzo Fusilli, Giovanni Laneve, Simone Pascucci, Angelo Palombo, Stefano Pignatti et Federico Santini. « Lake Victoria aquatic weeds monitoring by high spatial and spectral resolution satellite imagery ». Dans 2009 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5418284.

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Delalieux, S., D. Raymaekers, K. Nackaerts, E. Honkavaara, J. Soukkamaki et J. Van Den Borne. « High spatial and spectral remote sensing for detailed mapping of potato plant parameters ». Dans 2014 6th Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS). IEEE, 2014. http://dx.doi.org/10.1109/whispers.2014.8077564.

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Rapports d'organisations sur le sujet "High spatial and spectral remote sensing"

Deguise, J. C., M. McGovern, H. McNairn et K. Staenz. Spatial High Resolution Crop Measurements with Airborne Hyperspectral Remote Sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219371.

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Singhroy, V., J. E. Loehr et A. C. Correa. Landslide Risk Assessment with High Spatial Resolution Remote Sensing Satellite Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219716.

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Cohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer et Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, novembre 2013. http://dx.doi.org/10.32747/2013.7594385.bard.

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Potato yield and quality are highly dependent on an adequate supply of nitrogen and water. Opportunities exist to use airborne hyperspectral (HS) remote sensing for the detection of spatial variation in N status of the crop to allow more targeted N applications. Thermal remote sensing has the potential to identify spatial variations in crop water status to allow better irrigation management and eventually precision irrigation. The overall objective of this study was to examine the ability of HS imagery in the visible and near infrared spectrum (VIS-NIR) and thermal imagery to distinguish between water and N status in potato fields. To lay the basis for achieving the research objectives, experiments in the US and in Israel were conducted in potato with different irrigation and N-application amounts. Thermal indices based merely on thermal images were found sensitive to water status in both Israel and the US in three potato varieties. Spectral indices based on HS images were found suitable to detect N stress accurately and reliably while partial least squares (PLS) analysis of spectral data was more sensitive to N levels. Initial fusion of HS and thermal images showed the potential of detecting both N stress and water stress and even to differentiate between them. This study is one of the first attempts at fusing HS and thermal imagery to detect N and water stress and to estimate N and water levels. Future research is needed to refine these techniques for use in precision agriculture applications.

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Chen, Z., S. E. Grasby, C. Deblonde et X. Liu. AI-enabled remote sensing data interpretation for geothermal resource evaluation as applied to the Mount Meager geothermal prospective area. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330008.

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The objective of this study is to search for features and indicators from the identified geothermal resource sweet spot in the south Mount Meager area that are applicable to other volcanic complexes in the Garibaldi Volcanic Belt. A Landsat 8 multi-spectral band dataset, for a total of 57 images ranging from visible through infrared to thermal infrared frequency channels and covering different years and seasons, were selected. Specific features that are indicative of high geothermal heat flux, fractured permeable zones, and groundwater circulation, the three key elements in exploring for geothermal resource, were extracted. The thermal infrared images from different seasons show occurrence of high temperature anomalies and their association with volcanic and intrusive bodies, and reveal the variation in location and intensity of the anomalies with time over four seasons, allowing inference of specific heat transform mechanisms. Automatically extracted linear features using AI/ML algorithms developed for computer vision from various frequency bands show various linear segment groups that are likely surface expression associated with local volcanic activities, regional deformation and slope failure. In conjunction with regional structural models and field observations, the anomalies and features from remotely sensed images were interpreted to provide new insights for improving our understanding of the Mount Meager geothermal system and its characteristics. After validation, the methods developed and indicators identified in this study can be applied to other volcanic complexes in the Garibaldi, or other volcanic belts for geothermal resource reconnaissance.

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Suir, Glenn, Christina Saltus et Sam Jackson. Remote Assessment of Swamp and Bottomland Hardwood Habitat Condition in the Maurepas Diversion Project Area. Engineer Research and Development Center (U.S.), août 2021. http://dx.doi.org/10.21079/11681/41563.

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This study used high spatial resolution satellite imagery to identify and map Bottomland Hardwood (BLH) BLH and swamp within the Maurepas Diversion Project area and use Light Detection and Ranging (Lidar) elevation data, vegetation indices, and established stand-level thresholds to evaluate the condition of forested habitat. The Forest Condition methods and data developed as part of this study provide a remote sensing-based supplement to the field-based methods used in previous studies. Furthermore, several advantages are realized over traditional methods including higher resolution products, repeatability, improved coverage, and reduced effort and cost. This study advances previous methods and provides products useful for informing ecosystem decision making related to environmental assessments.

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Anderson, Gerald L., et Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, décembre 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.

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Huntley, D., D. Rotheram-Clarke, R. Cocking, J. Joseph et P. Bobrowsky. Current research on slow-moving landslides in the Thompson River valley, British Columbia (IMOU 5170 annual report). Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331175.

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Interdepartmental Memorandum of Understanding (IMOU) 5170 between Natural Resources Canada (NRCAN), the Geological Survey of Canada (GSC) and Transport Canada Innovation Centre (TC-IC) aims to gain new insight into slow-moving landslides, and the influence of climate change, through testing conventional and emerging monitoring technologies. IMOU 5107 focuses on strategically important sections of the national railway network in the Thompson River valley, British Columbia (BC), and the Assiniboine River valley along the borders of Manitoba (MN) and Saskatchewan (SK). Results of this research are applicable elsewhere in Canada (e.g., the urban-rural-industrial landscapes of the Okanagan Valley, BC), and around the world where slow-moving landslides and climate change are adversely affecting critical socio-economic infrastructure. Open File 8931 outlines landslide mapping and changedetection monitoring protocols based on the successes of IMOU 5170 and ICL-IPL Project 202 in BC. In this region, ice sheets, glaciers, permafrost, rivers and oceans, high relief, and biogeoclimatic characteristics contribute to produce distinctive rapid and slow-moving landslide assemblages that have the potential to impact railway infrastructure and operations. Bedrock and drift-covered slopes along the transportation corridors are prone to mass wasting when favourable conditions exist. In high-relief mountainous areas, rapidly moving landslides include rock and debris avalanches, rock and debris falls, debris flows and torrents, and lahars. In areas with moderate to low relief, rapid to slow mass movements include rockslides and slumps, debris or earth slides and slumps, and earth flows. Slow-moving landslides include rock glaciers, rock and soil creep, solifluction, and lateral spreads in bedrock and surficial deposits. Research efforts lead to a better understanding of how geological conditions, extreme weather events and climate change influence landslide activity along the national railway corridor. Combining field-based landslide investigation with multi-year geospatial and in-situ time-series monitoring leads to a more resilient railway national transportation network able to meet Canada's future socioeconomic needs, while ensuring protection of the environment and resource-based communities from landslides related to extreme weather events and climate change. InSAR only measures displacement in the east-west orientation, whereas UAV and RTK-GNSS change-detection surveys capture full displacement vectors. RTK-GNSS do not provide spatial coverage, whereas InSAR and UAV surveys do. In addition, InSAR and UAV photogrammetry cannot map underwater, whereas boat-mounted bathymetric surveys reveal information on channel morphology and riverbed composition. Remote sensing datasets, consolidated in a geographic information system, capture the spatial relationships between landslide distribution and specific terrain features, at-risk infrastructure, and the environmental conditions expected to correlate with landslide incidence and magnitude. Reliable real-time monitoring solutions for critical railway infrastructure (e.g., ballast, tracks, retaining walls, tunnels, and bridges) able to withstand the harsh environmental conditions of Canada are highlighted. The provision of fundamental geoscience and baseline geospatial monitoring allows stakeholders to develop robust risk tolerance, remediation, and mitigation strategies to maintain the resilience and accessibility of critical transportation infrastructure, while also protecting the natural environment, community stakeholders, and Canadian economy. We propose a best-practice solution involving three levels of investigation to describe the form and function of the wide range of rapid and slow-moving landslides occurring across Canada that is also applicable elsewhere. Research activities for 2022 to 2025 are presented by way of conclusion.

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