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Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution / Zhenfeng Shao in Geo-spatial Information Science, vol 25 n° 4 (December 2022)  

Public [article]  inGeo-spatial Information Science > vol 25 n° 4 (December 2022) . - pp 550 - 567 Titre : Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution Type de document : Article/Communication Auteurs : Zhenfeng Shao, Auteur ; Yuan Zhang, Auteur ; Cheng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 550 - 567 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de mélange spectral d’extrémités multiples [Termes IGN] approche hiérarchique [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] image Gaofen [Termes IGN] image Landsat-OLI [Termes IGN] scène urbaine [Termes IGN] surface imperméable Résumé : (auteur) Impervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images. For SMA, inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors. Meanwhile, the spectral-only SMA, without considering urban spatial distribution, fails to consider spectral variability in an adequate manner. The lack of endmember class diversity and their spatial variations lead to over/underestimation. To mitigate these issues, this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance, taking Wuhan and Wuzhou as two study areas. Specifically, the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarchize images into three regions, and distinct endmember combinations are independently developed in each region. Then, spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight. Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE, SE, and RMSE. Further analysis suggests that the hierarchical strategy can expand endmember class types and considerably improve the performance for the study areas in general, specifically in less developed areas. Moreover, we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas. Numéro de notice : A2022-890 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2022.2028535 Date de publication en ligne : 02/03/2022 En ligne : https://doi.org/10.1080/10095020.2022.2028535 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102237 [article]

Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)  

Public [article]  inRemote sensing of environment > vol 278 (September 2022) . - n° 113088 Titre : Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression Type de document : Article/Communication Auteurs : Haoyu Wang, Auteur ; Xiuyuan Zhang, Auteur ; Shihong Du, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113088 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] apprentissage profond [Termes IGN] carte d'occupation du sol [Termes IGN] cartographie thématique [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] croissance urbaine [Termes IGN] image Terra-MODIS [Termes IGN] modèle de régression [Termes IGN] série temporelle [Termes IGN] surface cultivée Résumé : (auteur) Global urbanization changes land cover patterns and affects the living environment of humans. However, urbanization and its evolution process, i.e., conversions among diverse land covers, are hard to measure, as existing land cover maps usually have low temporal resolutions; conversely, long-term and temporally dense land cover maps, such as vegetation-impervious-soil decomposition maps base on MODIS, ignore the important land cover of cropland in urban evolution process (UEP). To resolve the issue, this study suggests a novel model named time-extended non-crop vegetation-impervious-cropland (Time V-I-C) to represent and quantify different stages of UEP; then, a normalized multi-objective T-ConvLSTM (NMT) method is proposed to unmix cropland, non-crop vegetation, and impervious based on the intra-annual remotely-sensed time series, and obtain their fractions in each pixel for generating UEP maps. Consequently, UEP maps from 2001 to 2018 are generated for two Chinese urban agglomerations, i.e., Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations. The mapping results have high accuracies with a small standard error of regression (SER) of 13.1%, small root mean square error (RMSE) of 12.6%, and small mean absolute error (MAE) of 8.4%, and the maps reveal the different UEP in the two urban agglomerations. Therefore, this study provides a new idea for expressing UEP and contributes to a wide range of urbanization studies and sustainable city development. Numéro de notice : A2022-511 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.rse.2022.113088 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113088 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101049 [article]

Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data / Tohid Nouri in Geocarto international, Vol 37 n° 17 ([20/08/2022])  

Public [article]  inGeocarto international > Vol 37 n° 17 [20/08/2022] . - pp 5017 - 5040 Titre : Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data Type de document : Article/Communication Auteurs : Tohid Nouri, Auteur Année de publication : 2022 Article en page(s) : pp 5017 - 5040 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] altération géologique [Termes IGN] analyse des mélanges spectraux [Termes IGN] appariement d'images [Termes IGN] diffraction [Termes IGN] image multibande [Termes IGN] Iran [Termes IGN] logique floue [Termes IGN] mine d'or [Termes IGN] MNS ASTER [Termes IGN] pixel [Termes IGN] prospection minérale [Termes IGN] sédiment [Termes IGN] spectrométrie Résumé : (auteur) The northeast area of Ardabil, a city located in northwestern Iran, is one of the potential gold mineralization areas. In this study, ASTER data were used to identify the alteration events in this region. For this purpose, a novel approach was used in which the fuzzy logic was implemented to extract the co-occurrence map of the endmembers. This method revealed alterations more accurately than SID. Stream sediment samples were employed to validate the obtained results. Since these samples are alluvial, their catchment basins were determined and overlaid with the alteration maps. To the best of the authors’ knowledge, this validation approach has not been used in previous studies. The extracted alteration zones were in high conformity to the stream sediment samples. Next, X-ray diffraction (XRD) analysis and field spectrometry were used for delineation of the mineralogical phases present in the anomalous areas. Finally, the potential gold mineralization zones were identified. Numéro de notice : A2022-701 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1903575 Date de publication en ligne : 07/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1903575 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101560 [article]

Hyperspectral unmixing using transformer network / Preetam Ghosh in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 8 (August 2022) . - n° 5535116 Titre : Hyperspectral unmixing using transformer network Type de document : Article/Communication Auteurs : Preetam Ghosh, Auteur ; Swalpa Kumar Roy, Auteur ; Bikram Koirala, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5535116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image hyperspectrale Résumé : (auteur) Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way into the field of hyperspectral image classification and achieved promising results. In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep neural network-based unmixing model with transformers. A transformer network captures nonlocal feature dependencies by interactions between image patches, which are not employed in convolutional neural network (CNN) models, and hereby has the ability to enhance the quality of the endmember spectra and the abundance maps. The proposed model is a combination of a convolutional autoencoder and a transformer. The hyperspectral data is encoded by the convolutional encoder. The transformer captures long-range dependencies between the representations derived from the encoder. The data are reconstructed using a convolutional decoder. We applied the proposed unmixing model to three widely used unmixing datasets, that is, Samson, Apex, and Washington DC Mall, and compared it with the state-of-the-art in terms of root mean squared error and spectral angle distance. The source code for the proposed model will be made publicly available at https://github.com/preetam22n/DeepTrans-HSU . Numéro de notice : A2022-662 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3196057 Date de publication en ligne : 03/08/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3196057 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101518 [article]

A new method to detect targets in hyperspectral images based on principal component analysis / Shahram Sharifi Hashjin in Geocarto international, vol 37 n° 9 ([15/05/2022])  

Public [article]  inGeocarto international > vol 37 n° 9 [15/05/2022] . - pp 2679 - 2697 Titre : A new method to detect targets in hyperspectral images based on principal component analysis Type de document : Article/Communication Auteurs : Shahram Sharifi Hashjin, Auteur ; Safa Khazai, Auteur Année de publication : 2022 Article en page(s) : pp 2679 - 2697 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse en composantes principales [Termes IGN] détection de cible [Termes IGN] estimation de cohérence [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale Résumé : (auteur) Target detection (TD) is a major task in hyperspectral image (HSI) processing which, due to the high spectral resolution, requires dealing with the curse of dimensionality. The integrated feature extraction and selection is a well-known solution for dimensionality reduction of HSIs. In this study, a new method is presented to improve the performance of TD algorithms based on principal component analysis (PCA) feature space. In this method, using the implantation of the target spectrum (TS) in the HSI and following the simulated targets in the PCA feature space, the best principal components (PCs) are selected. Then, using the mixing and unmixing coefficients of the PCs, a new TS and a new image in the PCA feature space are created. Afterwards, using the new spectrum of the target, the TD algorithm is run on the new HSI. The performance of the proposed method is compared to nine counterpart algorithms on Hymap and Hyperion HSI. All the comparisons are performed using adaptive coherence estimator (ACE) TD algorithm. Experimental results illustrate that the proposed method, compared to its counterparts, yields superior performance based on the false alarm rate (FAR) measure. It gives an average FAR value of about 16, which is approximately 9% better than that of its best counterparts. Numéro de notice : A2022-568 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1831625 Date de publication en ligne : 01/12/2020 En ligne : https://doi.org/10.1080/10106049.2020.1831625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101251 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2022091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Unmixing-based spatiotemporal image fusion accounting for complex land cover changes / Xiaolu Jiang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 5 (May 2022)  

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Deep generative model for spatial–spectral unmixing with multiple endmember priors / Shuaikai Shi in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)  

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Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)  

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Characteristics of taiga and tundra snowpack in development and validation of remote sensing of snow / Henna-Reetta Hannula (2022)  

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Improving LSMA for impervious surface estimation in an urban area / Jin Wang in European journal of remote sensing, vol 55 n° 1 (2022)  

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A novel unmixing-based hypersharpening method via convolutional neural network / Xiaochen Lu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

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Phase unmixing of TerraSAR-X staring spotlight interferograms in building scale for PS height and deformation / Peng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)  

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Fluvial gravel bar mapping with spectral signal mixture analysis / Liza Stančič in European journal of remote sensing, vol 54 sup 1 (2021)  

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Cloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 / Dimitris Poursanidis in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)  

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Correntropy-based spatial-spectral robust sparsity-regularized hyperspectral unmixing / Xiaorun Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

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