Journal articles: 'Spatial information extraction'

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Zenasni, Sarah, Eric Kergosien, Mathieu Roche, and Maguelonne Teisseire. "Spatial Information Extraction from Short Messages." Expert Systems with Applications 95 (April 2018): 351–67. http://dx.doi.org/10.1016/j.eswa.2017.11.025.

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Hickman, Betty L., Michael P. Bishop, and Michael V. Rescigno. "Advanced computational methods for spatial information extraction." Computers & Geosciences 21, no. 1 (February 1995): 153–73. http://dx.doi.org/10.1016/0098-3004(94)00063-z.

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Shen, Xiangfei, and Wenxing Bao. "Hyperspectral Endmember Extraction Using Spatially Weighted Simplex Strategy." Remote Sensing 11, no. 18 (September 15, 2019): 2147. http://dx.doi.org/10.3390/rs11182147.

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Spatial information is increasingly becoming a vital factor in the field of hyperspectral endmember extraction, since it takes into consideration the spatial correlation of pixels, which generally involves jointing spectral information for preprocessing and/or endmember extraction in hyperspectral imagery (HSI). Generally, simplex-based endmember extraction algorithms (EEAs) identify endmembers without considering spatial attributes, and the spatial preprocessing strategy is an independently executed module that can provide spatial information for the endmember search process. Despite this interest, to the best of our knowledge, no one has studied the integration framework of the spatial information-embedded simplex for hyperspectral endmember extraction. In this paper, we propose a spatially weighted simplex strategy, called SWSS, for hyperspectral endmember extraction that investigates a novel integration framework of the spatial information-embedded simplex for identifying endmember. Specifically, the SWSS generates the spatial weight scalar of each pixel by determining its corresponding spatial neighborhood correlations for weighting itself within the simplex framework to regularize the selection of the endmembers. The SWSS could be implemented in the traditional simplex-based EEAs, such as vertex component analysis (VCA), to introduce spatial information into the data simplex framework without the computational complexity excessively increasing or endmember extraction accuracy loss. Based on spectral angle distance (SAD) and root-mean-square-error (RMSE) evaluation criteria, experimental results on both synthetic and C u p r i t e real hyperspectral datasets indicate that the simplex-based EEA re-implemented by the SWSS has a significant improvement on endmember extraction performance over the techniques on their own and without re-implementing.

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Chaitanya, Aravapalli Sri, Suvarna Vani Koneru, and Praveen Kumar Kollu. "Road Network Extraction using Atrous Spatial Pyramid Pooling." International Journal of Innovative Technology and Exploring Engineering 8, no. 9 (July 30, 2019): 31–33. http://dx.doi.org/10.35940/ijitee.h7459.078919.

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Road extraction from satellite images has several Applications such as geographic information system (GIS). Having an accurate and up-to-date road network database will facilitate transportation, disaster management and GPS navigation. Most active field of research for automatic extraction of road network involves semantic segmentation using convolutional neural network (CNN). Although they can produce accurate results, typically the models give up performance for accuracy and vice-versa. In this paper, we are proposing architecture for semantic segmentation of road networks using Atrous Spatial Pyramid Pooling (ASPP). The network contains residual blocks for extracting low level features. Atrous convolutions with different dilation rates are taken and spatial pyramid pooling is performed on these features for extracting the spatial information. The low level features from residual blocks are added to the multi scale context information to produce the final segmentation image. Our proposed model significantly reduces the number of parameters that are required to train the model. The proposed model was trained on the Massachusetts roads dataset and the results have shown that our model produces superior results than that of popular state-of-the art models.

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Tao, Chao, Ji Qi, Yansheng Li, Hao Wang, and Haifeng Li. "Spatial information inference net: Road extraction using road-specific contextual information." ISPRS Journal of Photogrammetry and Remote Sensing 158 (December 2019): 155–66. http://dx.doi.org/10.1016/j.isprsjprs.2019.10.001.

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Abdul-samad, Sarmad. "COLOR FEATURE WITH SPATIAL INFORMATION EXTRACTION METHODS FOR CBIR: A REVIEW." Iraqi Journal for Computers and Informatics 45, no. 1 (May 1, 2019): 15–19. http://dx.doi.org/10.25195/ijci.v45i1.45.

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Inn then last two decades the Content Based Image Retrieval (CBIR) considered as one of the topic of interest for theresearchers. It depending one analysis of the image’s visual content which can be done by extracting the color, texture and shapefeatures. Therefore, feature extraction is one of the important steps in CBIR system for representing the image completely. Color featureis the most widely used and more reliable feature among the image visual features. This paper reviews different methods, namely LocalColor Histogram, Color Correlogram, Row sum and Column sum and Colors Coherences Vectors were used to extract colors featurestaking in consideration the spatial information of the image.

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Syed, Mehtab Alam, Elena Arsevska, Mathieu Roche, and Maguelonne Teisseire. "GeoXTag: Relative Spatial Information Extraction and Tagging of Unstructured Text." AGILE: GIScience Series 3 (June 10, 2022): 1–10. http://dx.doi.org/10.5194/agile-giss-3-16-2022.

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Abstract. Spatial information has gained more attention in natural language processing tasks in different interdisciplinary domains. Moreover, the spatial information is available in two forms: Absolute Spatial Information (ASI) e.g., Paris, London, and Germany and Relative Spatial Information (RSI) e.g., south of Paris, north Madrid and 80 km from Rome. Therefore, it is challenging to extract RSI from textual data and compute its geotagging. This paper presents two strategies and the associated prototypes to address the following tasks: 1) extraction of relative spatial information from textual data and 2) geotagging of this relative spatial information. Experiments show promising results for RSI extraction and tagging.

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Tan, Kok Kiong, Arun Shankar Narayanan, Choon Huat Koh, Kevin Caves, and Helen Hoenig. "Extraction of spatial information for low-bandwidth telerehabilitation applications." Journal of Rehabilitation Research and Development 51, no. 5 (2014): 825–40. http://dx.doi.org/10.1682/jrrd.2013.09.0217.

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Xu, Mingming, Bo Du, and Liangpei Zhang. "Spatial-Spectral Information Based Abundance-Constrained Endmember Extraction Methods." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 6 (June 2014): 2004–15. http://dx.doi.org/10.1109/jstars.2013.2268661.

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Grosse-Wentrup, Moritz, and Martin Buss. "Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction." IEEE Transactions on Biomedical Engineering 55, no. 8 (August 2008): 1991–2000. http://dx.doi.org/10.1109/tbme.2008.921154.

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Li, Qifeng, Huijie Wang, Xiangyun Ma, Yang Wang, Xinwei Zheng, and Da Chen. "Integrated spectral and spatial information extraction in Raman spectroscopy." Spectroscopy Letters 51, no. 9 (October 21, 2018): 472–75. http://dx.doi.org/10.1080/00387010.2018.1493695.

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Fujita, Hideyuki. "Information Extraction and Visualization from Twitter Considering Spatial Structure." Cartographica: The International Journal for Geographic Information and Geovisualization 52, no. 2 (June 2017): 178–93. http://dx.doi.org/10.3138/cart.52.2.3875.

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Gao, Yu, Yinsong Pan, Hong Huang, Ehab R. Mohamed, and Zahraa M. I. Aly. "Swarm intelligence algorithm for extracting spatial spectrum features of hyperspectral remote sensing image and decomposing mixed pixels." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5045–55. http://dx.doi.org/10.3233/jifs-179990.

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Hyperspectral remote sensing combines spectrum, ground space and images organically to provide humans with unprecedented rich information. However, a prominent problem faced in the extraction and identification of hyperspectral remote sensing information is mixed pixels, and the method to solve mixed pixels is mixed pixel decomposition. The purpose of this paper is to study the swarm intelligence algorithm of spatial-spectral feature extraction and mixed pixel decomposition of hyperspectral remote sensing images. This paper first introduces two different methods for extracting spatial spectrum features, then studies linear and non-linear spectral hybrid models, and then studies end element extraction methods based on quantum particle swarm optimization. The degree inversion method, the experimental part is based on the accuracy of the quantum particle swarm optimization-based end-element extraction method and two spatial-spectrum feature extraction methods. The experimental results show that the algorithm proposed in this paper improves the effect of group pixel decomposition based on the swarm intelligence algorithm. The classification accuracy of the 3DLBP spatial spectrum feature proposed in this paper is 94.22%.

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Haris, Erum, Keng Hoon Gan, and Tien-Ping Tan. "Spatial information extraction from travel narratives: Analysing the notion of co-occurrence indicating closeness of tourist places." Journal of Information Science 46, no. 5 (June 10, 2019): 581–99. http://dx.doi.org/10.1177/0165551519837188.

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Recent advancements in social media have generated a myriad of unstructured geospatial data. Travel narratives are among the richest sources of such spatial clues. They are also a reflection of writers’ interaction with places. One of the prevalent ways to model this interaction is a points of interest (POIs) graph depicting popular POIs and routes. A relevant notion is that frequent pairwise occurrences of POIs indicate their geographic proximity. This work presents an empirical interpretation of this theory and constructs spatially enriched POI graphs, a clear augmentation to popularity-based POI graphs. A triplet pattern, rule-based spatial relation extraction technique SpatRE is proposed and compared with standard relation extraction systems Ollie and Stanford OpenIE. A travel blogs data set is also contributed containing labelled spatial relations. The performance is further evaluated on SemEval 2013 benchmark data sets. Finally, spatially enriched POI graphs are qualitatively compared with TripAdvisor and Google Maps to visualise information accuracy.

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Tan, Hai, Hao Xu, and Jiguang Dai. "BSIRNet: A Road Extraction Network with Bidirectional Spatial Information Reasoning." Journal of Sensors 2022 (January 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/6391238.

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Automatic extraction of road information from remote sensing images is widely used in many fields, such as urban planning and automatic navigation. However, due to interference from noise and occlusion, the existing road extraction methods can easily lead to road discontinuity. To solve this problem, a road extraction network with bidirectional spatial information reasoning (BSIRNet) is proposed, in which neighbourhood feature fusion is used to capture spatial context dependencies and expand the receptive field, and an information processing unit with a recurrent neural network structure is used to capture channel dependencies. BSIRNet enhances the connectivity of road information through spatial information reasoning. Using the public Massachusetts road dataset and Wuhan University road dataset, the superiority of the proposed method is verified by comparing its results with those of other models.

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Pour, T., J. Burian, and J. Miřijovský. "ADVANCED EXTRACTION OF SPATIAL INFORMATION FROM HIGH RESOLUTION SATELLITE DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 905–7. http://dx.doi.org/10.5194/isprs-archives-xli-b3-905-2016.

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In this paper authors processed five satellite image of five different Middle-European cities taken by five different sensors. The aim of the paper was to find methods and approaches leading to evaluation and spatial data extraction from areas of interest. For this reason, data were firstly pre-processed using image fusion, mosaicking and segmentation processes. Results going into the next step were two polygon layers; first one representing single objects and the second one representing city blocks. In the second step, polygon layers were classified and exported into Esri shapefile format. Classification was partly hierarchical expert based and partly based on the tool SEaTH used for separability distinction and thresholding. Final results along with visual previews were attached to the original thesis. Results are evaluated visually and statistically in the last part of the paper. In the discussion author described difficulties of working with data of large size, taken by different sensors and different also thematically.

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Pour, T., J. Burian, and J. Miřijovský. "ADVANCED EXTRACTION OF SPATIAL INFORMATION FROM HIGH RESOLUTION SATELLITE DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 905–7. http://dx.doi.org/10.5194/isprsarchives-xli-b3-905-2016.

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In this paper authors processed five satellite image of five different Middle-European cities taken by five different sensors. The aim of the paper was to find methods and approaches leading to evaluation and spatial data extraction from areas of interest. For this reason, data were firstly pre-processed using image fusion, mosaicking and segmentation processes. Results going into the next step were two polygon layers; first one representing single objects and the second one representing city blocks. In the second step, polygon layers were classified and exported into Esri shapefile format. Classification was partly hierarchical expert based and partly based on the tool SEaTH used for separability distinction and thresholding. Final results along with visual previews were attached to the original thesis. Results are evaluated visually and statistically in the last part of the paper. In the discussion author described difficulties of working with data of large size, taken by different sensors and different also thematically.

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Schröder, M., M. Walessa, H. Rehrauer, K. Seidel, and M. Datcu. "Gibbs random field models: a toolbox for spatial information extraction." Computers & Geosciences 26, no. 4 (May 2000): 423–32. http://dx.doi.org/10.1016/s0098-3004(99)00122-3.

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Liu, J. Jay, and John F. MacGregor. "On the extraction of spectral and spatial information from images." Chemometrics and Intelligent Laboratory Systems 85, no. 1 (January 2007): 119–30. http://dx.doi.org/10.1016/j.chemolab.2006.05.011.

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Della Penna, Giuseppe, Daniele Magazzeni, and Sergio Orefice. "A spatial relation-based framework to perform visual information extraction." Knowledge and Information Systems 30, no. 3 (April 9, 2011): 667–92. http://dx.doi.org/10.1007/s10115-011-0394-4.

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Wang, Yong, Xiangqiang Zeng, Xiaohan Liao, and Dafang Zhuang. "B-FGC-Net: A Building Extraction Network from High Resolution Remote Sensing Imagery." Remote Sensing 14, no. 2 (January 7, 2022): 269. http://dx.doi.org/10.3390/rs14020269.

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Deep learning (DL) shows remarkable performance in extracting buildings from high resolution remote sensing images. However, how to improve the performance of DL based methods, especially the perception of spatial information, is worth further study. For this purpose, we proposed a building extraction network with feature highlighting, global awareness, and cross level information fusion (B-FGC-Net). The residual learning and spatial attention unit are introduced in the encoder of the B-FGC-Net, which simplifies the training of deep convolutional neural networks and highlights the spatial information representation of features. The global feature information awareness module is added to capture multiscale contextual information and integrate the global semantic information. The cross level feature recalibration module is used to bridge the semantic gap between low and high level features to complete the effective fusion of cross level information. The performance of the proposed method was tested on two public building datasets and compared with classical methods, such as UNet, LinkNet, and SegNet. Experimental results demonstrate that B-FGC-Net exhibits improved profitability of accurate extraction and information integration for both small and large scale buildings. The IoU scores of B-FGC-Net on WHU and INRIA Building datasets are 90.04% and 79.31%, respectively. B-FGC-Net is an effective and recommended method for extracting buildings from high resolution remote sensing images.

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Gao, Yu Yu. "Improved Common Spatial Patterns on EEG Feature Extraction." Advanced Materials Research 926-930 (May 2014): 1814–17. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1814.

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For extracting relatively stable and invariable feature from non-stationary EEG in mult-class pattern, many scholars study a feature extraction method, which is called as modified multi-classcommon spatial pattern. It adopts one-to-one strategy to expand common spatial pattern to multi-class classification. While for the solution of airspace filter, Kullback-Leibler distance defines pattern of discrimination of minimize difference within class and maximize difference between classes. And it establishes a function to measure difference within the class. The experiment verifies that the algorithm can obtain feature information with recognition capability which implys in the non-stationary EEG and acquires preferable classification result.

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Zhang, Dong Hui, Ying Jun Zhao, Chuan Zhang, Ning Bo Zhao, and Dong Hua Lu. "Application of 3 "S" Technologies in Information Extraction of Nuclear Leakage Accident." Advanced Materials Research 718-720 (July 2013): 2011–14. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2011.

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The paper analyzes the theories of nuclear leakage accident information extraction based on 3S technology, and studying roundly the building process of GIS spatial and temporal databases; the contents of RS images collection and the function of GPS geography position information. In addition, information extraction and analysis methods both on spatial dimension and temporal dimension are summarized. The spatial distribution and trend of radiation leak after the nuclear accident are simulated by trend surface model. Calculation and mapping shows the variation of the radiation value with time using time series analysis model.

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Papadias, Evangelos, Margarita Kokla, and Eleni Tomai. "Educing knowledge from text: semantic information extraction of spatial concepts and places." AGILE: GIScience Series 2 (June 4, 2021): 1–7. http://dx.doi.org/10.5194/agile-giss-2-38-2021.

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Abstract. A growing body of geospatial research has shifted the focus from fully structured to semistructured and unstructured content written in natural language. Natural language texts provide a wealth of knowledge about geospatial concepts, places, events, and activities that needs to be extracted and formalized to support semantic annotation, knowledge-based exploration, and semantic search. The paper presents a web-based prototype for the extraction of geospatial entities and concepts, and the subsequent semantic visualization and interactive exploration of the extraction results. A lightweight ontology anchored in natural language guides the interpretation of natural language texts and the extraction of relevant domain knowledge. The approach is applied on three heterogeneous sources which provide a wealth of spatial concepts and place names.

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Rogge, D. M., B. Rivard, J. Zhang, A. Sanchez, J. Harris, and J. Feng. "Integration of spatial–spectral information for the improved extraction of endmembers." Remote Sensing of Environment 110, no. 3 (October 2007): 287–303. http://dx.doi.org/10.1016/j.rse.2007.02.019.

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Pflug, Anika, Daniel Hartung, and Christoph Busch. "Feature extraction from vein images using spatial information and chain codes." Information Security Technical Report 17, no. 1-2 (February 2012): 26–35. http://dx.doi.org/10.1016/j.istr.2012.02.003.

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Xu, Jun, and Xin Pan. "A Fuzzy Spatial Region Extraction Model for Object’s Vague Location Description from Observer Perspective." ISPRS International Journal of Geo-Information 9, no. 12 (November 25, 2020): 703. http://dx.doi.org/10.3390/ijgi9120703.

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Descriptions of the spatial locations of disappeared objects are often recorded in eyewitness records, travel notes, and historical documents. However, in geographic information system (GIS), the observer-centered and vague nature of the descriptions causes difficulties in representing the spatial characters of these objects. To address this problem, this paper proposes a Fuzzy Spatial Region Extraction Model for Object’s Vague Location Description from Observer Perspective (FSREM-OP). In this model, the spatial relationship between the observer and the object are represented in spatial knowledge. It is composed of “phrase” and “region”. Based on the spatial knowledge, three components of spatial inference are constructed: Spatial Entities (SEs), Fuzzy Spatial Regions (FSRs), and Spatial Actions (SAs). Through spatial knowledge and the components of FSREM-OP, an object’s location can be inferred from an observer’s describing text, transforming the vagueness and subjectivity of location description into fuzzy spatial regions in the GIS. The FSREM-OP was tested by constructing a group of observers, object position relationships and vague descriptions. The results show that it is capable of extracting the spatial information and presenting location descriptions in the GIS, despite the vagueness and subjective spatial relation expressions in the descriptions.

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Akbari, D. "AN EXTENDED SPECTRAL–SPATIAL CLASSIFICATION APPROACH FOR HYPERSPECTRAL DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 37–41. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-37-2017.

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In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8&thinsp;% overall accuracy higher than the original MSF-based algorithm.

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Malang, Kanokwan, Shuliang Wang, Yuanyuan Lv, and Aniwat Phaphuangwittayakul. "Skeleton Network Extraction and Analysis on Bicycle Sharing Networks." International Journal of Data Warehousing and Mining 16, no. 3 (July 2020): 146–67. http://dx.doi.org/10.4018/ijdwm.2020070108.

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Skeleton network extraction has been adopted unevenly in transportation networks whose nodes are always represented as spatial units. In this article, the TPks skeleton network extraction method is proposed and applied to bicycle sharing networks. The method aims to reduce the network size while preserving key topologies and spatial features. The authors quantified the importance of nodes by an improved topology potential algorithm. The spatial clustering allows to detect high traffic concentrations and allocate the nodes of each cluster according to their spatial distribution. Then, the skeleton network is constructed by aggregating the most important indicated skeleton nodes. The authors examine the skeleton network characteristics and different spatial information using the original networks as a benchmark. The results show that the skeleton networks can preserve the topological and spatial information similar to the original networks while reducing their size and complexity.

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Han, Xiaobing, Yanfei Zhong, and Liangpei Zhang. "SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 25–31. http://dx.doi.org/10.5194/isprsannals-iii-7-25-2016.

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Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets – Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

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Han, Xiaobing, Yanfei Zhong, and Liangpei Zhang. "SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 25–31. http://dx.doi.org/10.5194/isprs-annals-iii-7-25-2016.

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Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets – Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

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Sun, Yan Hua, and Ping Wang. "Based on High Resolution of Remote Sensing Data Mining Houses Information Extraction Methods Research." Applied Mechanics and Materials 170-173 (May 2012): 2803–7. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.2803.

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High resolution remote sensing images generally refer to image to the spatial resolution within 10m aerospace、aviation remote sensing images. The emergence of high-resolution images strengthened the ability to recognize the large scale features, especially for the extraction of houses information in mining area. High spatial resolution image has rich delicate texture feature, it is urgent to solution the problem of how to extract the features. The technology is very useful for statistic houses information、village relocation assessment and research of pressure coal status, providing important data basis for village relocation, statistics, assessment. Taking henan as a mining area for example, houses information extraction methods are discussed. This paper mainly research contents as followings: It is combined with the space texture information of high resolution imaging rich, using different methods to extract building information, including followings: First, ordinary image segmentation technology; this method is simple and feasible, but extracted housing information is not accurate. Second, the object-oriented method of feature extraction technology, visualization degree and extracting accuracy of this method is higher; Third, it has conducted the preliminary height extraction of the houses; according to the solar altitude angles and the shadow of the houses to calculate the height of the houses. And considering the influence of undulating terrain, using the terrain DEM data to analyze study area, finally determined the shadow length, and then used solar altitude angles to calculate houses height. Based on the verification, accuracy evaluation results show that houses contour information extraction accuracy is: accuracy of the number and area is over 80%, the total rate of wrong classifications is lower. Houses highly information extraction accuracy is within the 85%. The research methods are effective.

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Wu, Hao, and Hongguo Jia. "Extraction of knowledge on spatial distribution and spatial relationship from scanned topographic map using Convolutional Neural Networks." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-407-2019.

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Abstract. Topographic maps (TM) contain plenty of geographic information, such as topographic fluctuations, hydrological networks, vegetation, administrative regions, residential areas, transportation routes and facilities and other man-made features. Based on geographic information, the map knowledge extracted from topographic maps has been widely used in many research fields, such as landscape ecology, land and resources management and urbanization.Traditional topographic maps are generally in paper-format. It is difficult to use them for the spatial or multi-temporal analysis. Thus many research work focus on the extraction of geographic information based on scanned topographic maps (STM).Most of the existing studies developed many methods and algorithms to extract the geographical information from scanned topographic maps. However, these proposed methods usually only can extract a certain kind of feature, and parameters used in these methods are needed to set manually. However, for map knowledge, e.g. spatial distribution and spatial relationship among different map features, it is difficult to effectively combine different methods to extract map knowledge. Therefore, this paper proposes a method of extracting geographic knowledge based on deep-learning, which can be object-oriented and efficiently extract geographic knowledge. This method contains three steps: 1) establishing samples for different map features; 2) using the Convolutional Neural Networks (CNN), which is suited to the image recognition (Karpathy A et al. 2014), to classify the scanned topographic map; 3) estimating the proportion of different map features on maps and describing the spatial distribution based on a grid.The method proposed in this study has been evaluated by some scale topographic maps. The results indicate that the extraction precise of this method can reach more than 70% for water and mountain areas and can also describe the spatial distribution for the features with larger map areas.

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Bai, Xiao Lan, and Yu Zhang. "Research on Information Extraction in Pipe-Routing Layout Space for Complex Products." Applied Mechanics and Materials 155-156 (February 2012): 250–54. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.250.

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The extraction of information in the layout space is a basis and key to realize automatic pipe-routing layout for complex products such as aero-engines. This paper proposes the filling-surrounding spatial information extraction method. The layout space was first divided into several regions according to the location of accessories, which resulted in the parallel extraction of information in the layout space. Then by means of the filling idea, accessories in each region were filled one by one. After accessories’ rough containing boxes were determined, each accessory and free space was represented with discrete points by using grids to divide the rough containing box. Finally, the outside-in surrounding scanning was used for the rough containing box to identify the state of spatial points. In addition, the flow of information extraction and the storage mode of the extracted information were given. The case study illustrates its feasibility and effectiveness.

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Song, Meiping, Ying Li, Tingting Yang, and Dayong Xu. "Spatial Potential Energy Weighted Maximum Simplex Algorithm for Hyperspectral Endmember Extraction." Remote Sensing 14, no. 5 (February 28, 2022): 1192. http://dx.doi.org/10.3390/rs14051192.

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Most traditional endmember extraction algorithms focus on spectral information, which limits the effectiveness of endmembers. This paper develops a spatial potential energy weighted maximum simplex algorithm (SPEW) for hyperspectral endmember extraction, combining the relevance of hyperspectral spatial context with spectral information to effectively extract endmembers. Specifically, for pixels in a uniform spatial area, SPEW assigns a high weight to pixels with higher spatial potential energy. For pixels scattered in a spatial area, the high weights are assigned to the representative pixels with a smaller spectral angle distance. Then, the optimal endmember collection is determined by the simplex with maximum volume in the space of representative pixels. SPEW not only reduces the complexity of searching for the maximum simplex volume but also improves the performance of endmember extraction. In particular, compared with other newly proposed spatial-spectral hyperspectral endmember extraction methods, SPEW can effectively extract the hidden endmembers in a spatial area without adjusting any parameters. Experiments on synthetic and real data show that the SPEW algorithm has also provides better results than the traditional algorithms.

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Yang, Jiannan, Hong Jia, and Hanbing Liu. "Spatial Relationship Extraction of Geographic Entities Based on BERT Model." Journal of Physics: Conference Series 2363, no. 1 (November 1, 2022): 012031. http://dx.doi.org/10.1088/1742-6596/2363/1/012031.

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Geographic entity relationship extraction from text is an important way to acquire geographic knowledge. Entity relations in Chinese text are difficult to discover because of implicit representations of relations between entities in Chinese text. Therefore, using existing pattern matching and machine learning methods to extract entity relations from Chinese text often has problems such as insufficient artificial features, poor generality, inability to resolve word polysemy, and difficulty in making full use of contextual information. However, deep learning methods can better solve the above problems. This paper takes the spatial relation between geographic entities as the main research object to build a geographic entity spatial relation corpus, and propose a geographic entity relationship extraction method that combines BERT and attention mechanism. The method is trained and tested on the corpus. The results show that the F1 value of the BERT-BiGRU-Attention model proposed in this paper reaches 85.2% on the test set, which is a great improvement in relation extraction ability compared with other baseline models. The geographic entity relationship extraction method based on the BERT model can effectively learn the context information in Chinese text sentences, improve the accuracy of relationship extraction, and provide support for geographic knowledge graph construction and geographic entity information retrieval.

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Zhang, Zhenxing, Feng Gao, Bin Ma, and Zhiqiang Zhang. "Extraction of Earth Surface Texture Features from Multispectral Remote Sensing Data." Journal of Electrical and Computer Engineering 2018 (October 25, 2018): 1–9. http://dx.doi.org/10.1155/2018/9684629.

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Earth surface texture features referring to as visual features of homogeneity in remote sensing images are very important to understand the relationship between surface information and surrounding environment. Remote sensing data contain rich information of earth surface texture features (image gray reflecting the spatial distribution information of texture features, for instance). Here, we propose an efficient and accurate approach to extract earth surface texture features from remote sensing data, called gray level difference frequency spatial (GLDFS). The gray level difference frequency spatial approach is designed to extract multiband remote sensing data, utilizing principle component analysis conversion to compress the multispectral information, and it establishes the gray level difference frequency spatial of principle components. In the end, the texture features are extracted using the gray level difference frequency spatial. To verify the effectiveness of this approach, several experiments are conducted and indicate that it could retain the coordination relationship among multispectral remote sensing data, and compared with the traditional single-band texture analysis method that is based on gray level co-occurrence matrix, the proposed approach has higher classification precision and efficiency.

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Zhang, Tianyu, Cuiping Shi, Diling Liao, and Liguo Wang. "Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification." Remote Sensing 13, no. 21 (November 7, 2021): 4472. http://dx.doi.org/10.3390/rs13214472.

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Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images.

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Xie, Yan, Fang Miao, Kai Zhou, and Jing Peng. "HsgNet: A Road Extraction Network Based on Global Perception of High-Order Spatial Information." ISPRS International Journal of Geo-Information 8, no. 12 (December 10, 2019): 571. http://dx.doi.org/10.3390/ijgi8120571.

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Road extraction is a unique and difficult problem in the field of semantic segmentation because roads have attributes such as slenderness, long span, complexity, and topological connectivity, etc. Therefore, we propose a novel road extraction network, abbreviated HsgNet, based on high-order spatial information global perception network using bilinear pooling. HsgNet, taking the efficient LinkNet as its basic architecture, embeds a Middle Block between the Encoder and Decoder. The Middle Block learns to preserve global-context semantic information, long-distance spatial information and relationships, and different feature channels’ information and dependencies. It is different from other road segmentation methods which lose spatial information, such as those using dilated convolution and multiscale feature fusion to record local-context semantic information. The Middle Block consists of three important steps: (1) forming a feature resource pool to gather high-order global spatial information; (2) selecting a feature weight distribution, enabling each pixel position to obtain complementary features according to its own needs; and (3) inversely mapping the intermediate output feature encoding to the size of the input image by expanding the number of channels of the intermediate output feature. We compared multiple road extraction methods on two open datasets, SpaceNet and DeepGlobe. The results show that compared to the efficient road extraction model D-LinkNet, our model has fewer parameters and better performance: we achieved higher mean intersection over union (71.1%), and the model parameters were reduced in number by about 1/4.

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Wang, Zhengyang, and Shufang Tian. "Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network." PLOS ONE 16, no. 10 (October 14, 2021): e0254542. http://dx.doi.org/10.1371/journal.pone.0254542.

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The purposes are to solve the isomorphism encountered while processing hyperspectral remote sensing data and improve the accuracy of hyperspectral remote sensing data in extracting and classifying lithological information. Taking rocks as the research object, Backpropagation Neural Network (BPNN) is introduced. After the hyperspectral image data are normalized, the lithological spectrum and spatial information are the feature extraction targets to construct a deep learning-based lithological information extraction model. The performance of the model is analyzed using specific instance data. Results demonstrate that the overall accuracy and the Kappa coefficient of the lithological information extraction and classification model based on deep learning were 90.58% and 0.8676, respectively. This model can precisely distinguish the properties of rock masses and provide better performance compared with the state of other analysis models. After introducing deep learning, the recognition accuracy and the Kappa coefficient of the proposed BPNN model increased by 8.5% and 0.12, respectively, compared with the traditional BPNN. The proposed extraction and classification model can provide some research values and practical significances for the hyperspectral rock and mineral classification.

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Wang, Zhengyang, and Shufang Tian. "Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network." PLOS ONE 16, no. 10 (October 14, 2021): e0254542. http://dx.doi.org/10.1371/journal.pone.0254542.

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The purposes are to solve the isomorphism encountered while processing hyperspectral remote sensing data and improve the accuracy of hyperspectral remote sensing data in extracting and classifying lithological information. Taking rocks as the research object, Backpropagation Neural Network (BPNN) is introduced. After the hyperspectral image data are normalized, the lithological spectrum and spatial information are the feature extraction targets to construct a deep learning-based lithological information extraction model. The performance of the model is analyzed using specific instance data. Results demonstrate that the overall accuracy and the Kappa coefficient of the lithological information extraction and classification model based on deep learning were 90.58% and 0.8676, respectively. This model can precisely distinguish the properties of rock masses and provide better performance compared with the state of other analysis models. After introducing deep learning, the recognition accuracy and the Kappa coefficient of the proposed BPNN model increased by 8.5% and 0.12, respectively, compared with the traditional BPNN. The proposed extraction and classification model can provide some research values and practical significances for the hyperspectral rock and mineral classification.

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Sun, Bin, Zhihai Gao, Longcai Zhao, Hongyan Wang, Wentao Gao, and Yuanyuan Zhang. "Extraction of Information on Trees outside Forests Based on Very High Spatial Resolution Remote Sensing Images." Forests 10, no. 10 (September 23, 2019): 835. http://dx.doi.org/10.3390/f10100835.

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The sparse Ulmus pumila L. woodland in the Otingdag Sandy Land of China is indispensable in maintaining the ecosystem stability of the desertified grasslands. Many studies of this region have focused on community structure and analysis of species composition, but without consideration of spatial distribution. Based on a combination of spectral and multiscale spatial variation features, we present a method for automated extraction of information on the U. pumila trees of the Otingdag Sandy Land using very high spatial resolution remote sensing imagery. In this method, feature images were constructed using fused 1-m spatial resolution GF-2 images through analysis of the characteristics of the natural geographical environment and the spatial distribution of the U. pumila trees. Then, a multiscale Laplace transform was performed on the feature images to generate multiscale Laplacian feature spaces. Next, local maxima and minima were obtained by iteration over the multiscale feature spaces. Finally, repeated values were removed and vector data (point data) were generated for automatic extraction of the spatial distribution and crown contours of the U. pumila trees. Results showed that the proposed method could overcome the lack of universality common to image classification methods. Validation indicated the accuracy of information extracted from U. pumila test data reached 82.7%. Further analysis determined the parameter values of the algorithm applicable to the study area. Extraction accuracy was improved considerably with a gradual increase of the Sigma parameter; however, the probability of missing data also increased markedly after the parameter reached a certain level. Therefore, we recommend the Sigma value of the algorithm be set to 90 (±5). The proposed method could provide a reference for information extraction, spatial distribution mapping, and forest protection in relation to the U. pumila woodland of the Otingdag Sandy Land, which could also support improved ecological protection across much of northern China.

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Duan, Yueming, Wenyi Zhang, Peng Huang, Guojin He, and Hongxiang Guo. "A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images." Remote Sensing 13, no. 22 (November 14, 2021): 4576. http://dx.doi.org/10.3390/rs13224576.

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Mapping land surface water automatically and accurately is closely related to human activity, biological reproduction, and the ecological environment. High spatial resolution remote sensing image (HSRRSI) data provide extensive details for land surface water and gives reliable data support for the accurate extraction of land surface water information. The convolutional neural network (CNN), widely applied in semantic segmentation, provides an automatic extraction method in land surface water information. This paper proposes a new lightweight CNN named Lightweight Multi-Scale Land Surface Water Extraction Network (LMSWENet) to extract the land surface water information based on GaoFen-1D satellite data of Wuhan, Hubei Province, China. To verify the superiority of LMSWENet, we compared the efficiency and water extraction accuracy with four mainstream CNNs (DeeplabV3+, FCN, PSPNet, and UNet) using quantitative comparison and visual comparison. Furthermore, we used LMSWENet to extract land surface water information of Wuhan on a large scale and produced the land surface water map of Wuhan for 2020 (LSWMWH-2020) with 2m spatial resolution. Random and equidistant validation points verified the mapping accuracy of LSWMWH-2020. The results are summarized as follows: (1) Compared with the other four CNNs, LMSWENet has a lightweight structure, significantly reducing the algorithm complexity and training time. (2) LMSWENet has a good performance in extracting various types of water bodies and suppressing noises because it introduces channel and spatial attention mechanisms and combines features from multiple scales. The result of land surface water extraction demonstrates that the performance of LMSWENet exceeds that of the other four CNNs. (3) LMSWENet can meet the requirement of high-precision mapping on a large scale. LSWMWH-2020 can clearly show the significant lakes, river networks, and small ponds in Wuhan with high mapping accuracy.

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Borhaninejad, S., F. Hakimpour, and E. Hamzei. "TAGS EXTARCTION FROM SPATIAL DOCUMENTS IN SEARCH ENGINES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 10, 2015): 111–13. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-111-2015.

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Nowadays the selective access to information on the Web is provided by search engines, but in the cases which the data includes spatial information the search task becomes more complex and search engines require special capabilities. The purpose of this study is to extract the information which lies in spatial documents. To that end, we implement and evaluate information extraction from GML documents and a retrieval method in an integrated approach. Our proposed system consists of three components: crawler, database and user interface. In crawler component, GML documents are discovered and their text is parsed for information extraction; storage. The database component is responsible for indexing of information which is collected by crawlers. Finally the user interface component provides the interaction between system and user. We have implemented this system as a pilot system on an Application Server as a simulation of Web. Our system as a spatial search engine provided searching capability throughout the GML documents and thus an important step to improve the efficiency of search engines has been taken.

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Zheng, Quansheng. "Information Extraction and Spatial Pattern Analysis of Sports Events Based on Deep Learning." Scientific Programming 2022 (April 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/7729769.

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The analysis of sports events information greatly promotes the formulation of scientific development strategy for sports events, the optimization of the spatial distribution of these events, and the popularization of sports culture. Most of the previous studies focused on the positive effects on sports events, but few quantified the effects. Besides, the broad environment of new media information communication was not considered. Therefore, this study carries out information extraction and spatial pattern analysis of sports events based on deep learning. Firstly, the texts of sports events information were classified by convolutional neural network (CNN), and the most valuable news or cases of sports events were screened. On this basis, a named entity recognition model was constructed to extract the most effective information from the data of sports events information. Next, a spatial pattern analysis approach was provided for the diffusion of sports event information flow. Finally, experiments were carried out to demonstrate the effectiveness of the proposed model and provide the spatial pattern analysis results on the diffusion of sports events information flow.

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Takatoo, Masao, Tadaaki Kitamura, and Yoshiki Kobayashi. "Vehicle extraction using spatial differentiation and subtraction." Systems and Computers in Japan 29, no. 7 (June 30, 1998): 21–32. http://dx.doi.org/10.1002/(sici)1520-684x(19980630)29:7<21::aid-scj3>3.0.co;2-k.

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Kim, Wookhyun, Yuzo Hirai, Takashi Furukawa, and Hidenobu Arita. "Extraction of road segments by spatial filters." Systems and Computers in Japan 25, no. 3 (1994): 57–67. http://dx.doi.org/10.1002/scj.4690250305.

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Shan, Wei. "Optimized Design of 3D Spatial Images Based on Kalman Filter Equation." Advances in Mathematical Physics 2021 (December 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/7262871.

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This paper takes the advantageous ability of Kalman filter equation as a means to jointly realize the accurate and reliable extraction of 3D spatial information and carries out the research work from the extraction of 3D spatial position information from multisource remote sensing optical stereo image pairs, recovery of 3D spatial structure information, and joint extraction of 3D spatial information with optimal topological structure constraints, respectively. Taking advantage of the stronger effect capability of Wiener recovery and shorter computation time of Kalman filter recovery, Wiener recovery is combined with Kalman filter recovery (referred to as Wiener-Kalman filter recovery method), and the mean square error and peak signal-to-noise ratio of the recovered image of this method are comparable to those of Wiener recovery, but the subjective evaluation concludes that the recovered image obtained by the Wiener-Kalman filter recovery method is clearer. To address the problem that the Kalman filter recovery method has the advantage of short computation time but the recovery effect is not as good as the Wiener recovery method, an improved Kalman filter recovery algorithm is proposed, which overcomes the fact that the Kalman filter recovery only targets the rows and columns of the image matrix for noise reduction and cannot utilize the pixel point information among the neighboring rows and columns. The algorithm takes the first row of the matrix image as the initial parameter of the Kalman filter prediction equation and then takes the first row of the recovered image as the initial parameter of the second Kalman filter prediction equation. The algorithm does not need to estimate the degradation function of the degradation system based on the degraded image, and the recovered image presents the image edge detail information more clearly, while the recovery effect is comparable to that of the Wiener recovery and Wiener-Kalman filter recovery method, and the improved Kalman filter recovery method has stronger noise reduction ability compared with the Kalman filter recovery method. The problem that the remote sensing optical images are seriously affected by shadows and complex environment detail information when 3D spatial structure information is extracted and the data extraction feature edge is not precise enough and the structure information extraction is not stable enough is addressed. A global optimal planar segmentation method with graded energy minimization is proposed, which can realize the accurate and stable extraction of the topological structure of the top surface by combining the edge information of remote sensing optical images and ensure the accuracy and stability of the final extracted 3D spatial information.

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Zhang, Qian, Yi Ping Yang, and Xin Wei Jiang. "Improved Low-Rank Matrix Approximation for Hyperspectral Image Spatial-Spectral Feature Extraction." Applied Mechanics and Materials 590 (June 2014): 716–21. http://dx.doi.org/10.4028/www.scientific.net/amm.590.716.

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It is important to take account into both the spectral domain and spatial domain information for hyperspectral image analysis. Thus, how to effectively integrate both spectral and spatial information confronts us. Motived by the least square form of PCA, we extend it to a low-rank matrix approximation form for multi-feature dimensionality redu-ction. In addition, we use the ensemble manifold regularize-ation techniques to capture the complementary information provided by spectral-spatial features of hyperspectral image. Experimental results on public hyperspectral data set demonstrate the effectiveness of our proposed method.

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Datta, Surabhi, and Kirk Roberts. "Fine-grained spatial information extraction in radiology as two-turn question answering." International Journal of Medical Informatics 158 (February 2022): 104628. http://dx.doi.org/10.1016/j.ijmedinf.2021.104628.

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Journal articles on the topic 'Spatial information extraction'

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