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Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM / Jiehua Cai in Engineering Geology, vol 305 (August 2022)
[article]
Titre : Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM Type de document : Article/Communication Auteurs : Jiehua Cai, Auteur ; Lu Zhang, Auteur ; Jie Dong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 106730 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] cartographie des risques
[Termes IGN] déformation de surface
[Termes IGN] données lidar
[Termes IGN] données multisources
[Termes IGN] effondrement de terrain
[Termes IGN] géomorphologie
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image optique
[Termes IGN] image Sentinel-SAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] MNS lidar
[Termes IGN] MNS SRTM
[Termes IGN] séisme
[Termes IGN] Setchouan (Chine)
[Termes IGN] surveillance géologiqueRésumé : (auteur) On 8th August 2017, a catastrophic Ms. 7.0 earthquake with a focal depth of 20 km struck the Jiuzhaigou County in Sichuan Province, China. It exerted a strong influence on the slope stability within the surrounding areas and triggered numerous secondary geohazards including rockfalls and other co-seismic landslides, which incurred drastic surface changes, and thus can be easily identified from cloud-free high-resolution optical imagery. Most of such landslides became stabilized shortly after the earthquake while others moving very slowly for years. In contrast, some slopes were destabilized without significant surface change into slow-moving landslides, which may pose long-term potential threats to people's life and property. Therefore, it is crucial to accurately identify these slow-moving landslides and regularly monitor their post-seismic activity. In this study, we employed the synthetic aperture radar interferometry (InSAR) techniques to detect and monitor slow-moving landslides after the earthquake in the Jiuzhaigou area, and analyzed the impacts of the earthquake on these landslides through integration of multi-source data (InSAR, Lidar, optical image, and field survey). As a result, 16 slow-moving landslides were detected by InSAR in the Jiuzhaigou area, including several historical landslides. The results of time-series InSAR analyses enabled identification of three kinds of landslide evolution modes affected by the earthquake, i.e. acceleration of deformation of pre-existing landslides, reactivation of dormant landslide, and remobilization of earthquake-triggered landslide. Each mode is supported by detailed analyses of multi-source data. The results demonstrated that satellite InSAR combined with high-resolution Lidar and optical data can provide a cost-effective approach of post-earthquake geohazards detection and monitoring. Numéro de notice : A2022-469 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.enggeo.2022.106730 Date de publication en ligne : 28/05/2022 En ligne : https://doi.org/10.1016/j.enggeo.2022.106730 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100811
in Engineering Geology > vol 305 (August 2022) . - n° 106730[article]Hyperspectral unmixing using transformer network / Preetam Ghosh in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)
[article]
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 hyperspectraleRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 8 (August 2022) . - n° 5535116[article]Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network / Alex David Singleton in Computers, Environment and Urban Systems, vol 95 (July 2022)
[article]
Titre : Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network Type de document : Article/Communication Auteurs : Alex David Singleton, Auteur ; Dani Arribas-Bel, Auteur ; John Murray, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101802 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] analyse en composantes principales
[Termes IGN] apprentissage automatique
[Termes IGN] bâtiment
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Grande-Bretagne
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] morphologie urbaine
[Termes IGN] pondération
[Termes IGN] processeur graphiqueRésumé : (auteur) The increased availability of high-resolution multispectral imagery captured by remote sensing platforms provides new opportunities for the characterisation and differentiation of urban context. The discovery of generalized latent representations from such data are however under researched within the social sciences. As such, this paper exploits advances in machine learning to implement a new method of capturing measures of urban context from multispectral satellite imagery at a very small area level through the application of a convolutional autoencoder (CAE). The utility of outputs from the CAE is enhanced through the application of spatial weighting, and the smoothed outputs are then summarised using cluster analysis to generate a typology comprising seven groups describing salient patterns of differentiated urban context. The limits of the technique are discussed with reference to the resolution of the satellite data utilised within the study and the interaction between the geography of the input data and the learned structure. The method is implemented within the context of Great Britain, however, is applicable to any location where similar high resolution multispectral imagery are available. Numéro de notice : A2022-370 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101802 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101802 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100606
in Computers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101802[article]Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery / Qian Shen in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)
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Titre : Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Qian Shen, Auteur ; Jiru Huang, Auteur ; Min Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 78 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de changement
[Termes IGN] détection du bâti
[Termes IGN] données qualitatives
[Termes IGN] estimation quantitative
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image multibande
[Termes IGN] jeu de données
[Termes IGN] réseau neuronal siamoisRésumé : (auteur) In the field of remote sensing applications, semantic change detection (SCD) simultaneously identifies changed areas and their change types by jointly conducting bitemporal image classification and change detection. It facilitates change reasoning and provides more application value than binary change detection (BCD), which offers only a binary map of the changed/unchanged areas. In this study, we propose a multitask Siamese network, named the semantic feature-constrained change detection (SFCCD) network, for building change detection in bitemporal high-spatial-resolution (HSR) images. SFCCD conducts feature extraction, semantic segmentation and change detection simultaneously, where change detection and semantic segmentation are the main and auxiliary tasks, respectively. For the segmentation task, ResNet50 is used to conduct image feature extraction, and the extracted semantic features are provided to execute the change detection task via a series of jump connections. For the change detection task, a global channel attention (GCA) module and a multiscale feature fusion (MSFF) module are designed, where high-level features offer training guidance to the low-level feature maps, and multiscale features are fused with multiple convolutions that possess different receptive fields. In bitemporal HSR images with different view angles, high-rise buildings have different directional height displacements, which generally cause serious false alarms for common change detection methods. However, known public building change detection datasets often lack buildings with height displacement. We thus create the Nanjing Dataset (NJDS) and design the aforementioned network structures and modules to target this issue. Experiments for method validation and comparison are conducted on the NJDS and two additional public datasets, i.e., the WHU Building Dataset (WBDS) and Google Dataset (GDS). Ablation experiments on the NJDS show that the joint utilization of the GCA and MSFF modules performs better than several classic modules, including atrous spatial pyramid pooling (ASPP), efficient spatial pyramid (ESP), channel attention block (CAB) and global attention upsampling (GAU) modules, in dealing with building height displacement. Furthermore, SFCCD achieves higher accuracy in terms of the OA, recall, F1-score and mIoU measures than several state-of-the-art change detection methods, including deeply supervised image fusion network (DSIFN), the dual-task constrained deep Siamese convolutional network (DTCDSCN), and multitask U-Net (MTU-Net). Numéro de notice : A2022-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.05.001 Date de publication en ligne : 12/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.05.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100762
in ISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 78 - 94[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2022071 SL Revue Centre de documentation Revues en salle Disponible Artificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data / Saeideh Sahebi Vayghan in Geocarto international, vol 37 n° 10 ([01/06/2022])
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Titre : Artificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data Type de document : Article/Communication Auteurs : Saeideh Sahebi Vayghan, Auteur ; Mohammad Salmani, Auteur ; Neda Ghasemkhanic, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2967 - 2995 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme génétique
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection d'arbres
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] empreinte
[Termes IGN] image aérienne
[Termes IGN] image optique
[Termes IGN] Inférence floue
[Termes IGN] morphologie mathématiqueRésumé : (auteur) One of the most important considerations in urban environments is the extraction of urban objects, with a high automation level. This study aims to present a new method which uses aerial images and LiDAR data to extract buildings and trees footprint in urban areas. In this study, high-elevation objects were extracted from the LiDAR data using the developed scan labeling method, and then the classification methods of Neural Networks (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Based K-Means algorithm (GBKMs) were used to separate buildings and trees and with the purpose of evaluating their performance. The features used for the classification were extracted from aerial images and LiDAR data, and the training data for the classification were selected automatically. Mathematical morphology functions were also used to process the classification results. The results show that NN and the ANFIS are more effective than the genetic-based K-Means algorithm in detecting small and large buildings. Numéro de notice : A2022-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1844311 En ligne : https://doi.org/10.1080/10106049.2020.1844311 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101300
in Geocarto international > vol 37 n° 10 [01/06/2022] . - pp 2967 - 2995[article]HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion / Kun Li in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)PermalinkPrecise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP / Haibin Wu in Remote sensing, vol 14 n° 11 (June-1 2022)PermalinkApplication oriented quality evaluation of Gaofen-7 optical stereo satellite imagery / Jiaojiao Tian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)PermalinkA 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])PermalinkResearch on automatic identification method of terraces on the Loess plateau based on deep transfer learning / Mingge Yu in Remote sensing, vol 14 n° 10 (May-2 2022)PermalinkSpectral-spatial classification method for hyperspectral images using stacked sparse autoencoder suitable in limited labelled samples situation / Seyyed Ali Ahmadi in Geocarto international, vol 37 n° 7 ([15/04/2022])PermalinkWood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data / Michele Dalponte in Remote sensing, vol 14 n° 8 (April-2 2022)PermalinkDeep 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)PermalinkDirect photogrammetry with multispectral imagery for UAV-based snow depth estimation / Kathrin Maier in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkGraph learning based on signal smoothness representation for homogeneous and heterogeneous change detection / David Alejandro Jimenez-Sierra in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)PermalinkMeta-learning based hyperspectral target detection using siamese network / Yulei Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)PermalinkAboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network / Chen Chen in Remote sensing of environment, vol 270 (March 2022)PermalinkDeep-learning-based multispectral image reconstruction from single natural color RGB image - Enhancing UAV-based phenotyping / Jiangsan Zhao in Remote sensing, vol 14 n° 5 (March-1 2022)PermalinkProbabilistic unsupervised classification for large-scale analysis of spectral imaging data / Emmanuel Paradis in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkDecision fusion of deep learning and shallow learning for marine oil spill detection / Junfang Yang in Remote sensing, vol 14 n° 3 (February-1 2022)PermalinkMapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery / Donato Morresi in Remote sensing of environment, vol 269 (February 2022)PermalinkSemantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network / Ekrem Saralioglu in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkAirborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape / Niva Kiran Verma in Geocarto international, vol 37 n° 1 ([01/01/2022])PermalinkCharacteristics of taiga and tundra snowpack in development and validation of remote sensing of snow / Henna-Reetta Hannula (2022)PermalinkDetection and biomass estimation of phaeocystis globosa blooms off Southern China from UAV-based hyperspectral measurements / Xue Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)Permalink