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A framework for unsupervised wildfire damage assessment using VHR satellite images with PlanetScope data / Minkyung Chung in Remote sensing, vol 12 n° 22 (December 2020)  

Public [article]  inRemote sensing > vol 12 n° 22 (December 2020) . - n° 3835 Titre : A framework for unsupervised wildfire damage assessment using VHR satellite images with PlanetScope data Type de document : Article/Communication Auteurs : Minkyung Chung, Auteur ; Youkyung Han, Auteur ; Yongil Kim, Auteur Année de publication : 2020 Article en page(s) : n° 3835 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] aide à la décision [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] Corée du sud [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] dommage [Termes descripteurs IGN] estimation par noyau [Termes descripteurs IGN] flou [Termes descripteurs IGN] gestion des risques [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image Geoeye [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image PlanetScope [Termes descripteurs IGN] incendie de forêt [Termes descripteurs IGN] Normalized Difference Vegetation Index Résumé : (auteur) The application of remote sensing techniques for disaster management often requires rapid damage assessment to support decision-making for post-treatment activities. As the on-demand acquisition of pre-event very high-resolution (VHR) images is typically limited, PlanetScope (PS) offers daily images of global coverage, thereby providing favorable opportunities to obtain high-resolution pre-event images. In this study, we propose an unsupervised change detection framework that uses post-fire VHR images with pre-fire PS data to facilitate the assessment of wildfire damage. To minimize the time and cost of human intervention, the entire process was executed in an unsupervised manner from image selection to change detection. First, to select clear pre-fire PS images, a blur kernel was adopted for the blind and automatic evaluation of local image quality. Subsequently, pseudo-training data were automatically generated from contextual features regardless of the statistical distribution of the data, whereas spectral and textural features were employed in the change detection procedure to fully exploit the properties of different features. The proposed method was validated in a case study of the 2019 Gangwon wildfire in South Korea, using post-fire GeoEye-1 (GE-1) and pre-fire PS images. The experimental results verified the effectiveness of the proposed change detection method, achieving an overall accuracy of over 99% with low false alarm rate (FAR), which is comparable to the accuracy level of the supervised approach. The proposed unsupervised framework accomplished efficient wildfire damage assessment without any prior information by utilizing the multiple features from multi-sensor bi-temporal images. Numéro de notice : A2020-793 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12223835 date de publication en ligne : 22/11/2020 En ligne : https://doi.org/10.3390/rs12223835 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96570 [article]

Unsupervised deep joint segmentation of multitemporal high-resolution images / Sudipan Saha in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8780 - 8792 Titre : Unsupervised deep joint segmentation of multitemporal high-resolution images Type de document : Article/Communication Auteurs : Sudipan Saha, Auteur ; Lichao Mou, Auteur ; Chunping Qiu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8780 - 8792 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] extraction de données [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] itération [Termes descripteurs IGN] segmentation sémantique Résumé : (auteur) High/very-high-resolution (HR/VHR) multitemporal images are important in remote sensing to monitor the dynamics of the Earth’s surface. Unsupervised object-based image analysis provides an effective solution to analyze such images. Image semantic segmentation assigns pixel labels from meaningful object groups and has been extensively studied in the context of single-image analysis, however not explored for multitemporal one. In this article, we propose to extend supervised semantic segmentation to the unsupervised joint semantic segmentation of multitemporal images. We propose a novel method that processes multitemporal images by separately feeding to a deep network comprising of trainable convolutional layers. The training process does not involve any external label, and segmentation labels are obtained from the argmax classification of the final layer. A novel loss function is used to detect object segments from individual images as well as establish a correspondence between distinct multitemporal segments. Multitemporal semantic labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on three different HR/VHR data sets from Munich, Paris, and Trento, which shows the method to be effective. We further extended the proposed joint segmentation method for change detection (CD) and tested on a VHR multisensor data set from Trento. Numéro de notice : A2020-744 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2990640 date de publication en ligne : 11/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2990640 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96375 [article]

A fractal projection and Markovian segmentation-based approach for multimodal change detection / Max Mignotte in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 8046 - 8058 Titre : A fractal projection and Markovian segmentation-based approach for multimodal change detection Type de document : Article/Communication Auteurs : Max Mignotte, Auteur Année de publication : 2020 Article en page(s) : pp 8046 - 8058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] champ aléatoire de Markov [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] décomposition d'image [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] géométrie fractale [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] projection [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) Change detection in heterogeneous bitemporal satellite images has become an emerging, important, and challenging research topic in remote sensing for rapid damage assessment. In this article, we explore a new parametric mapping strategy based on a modified geometric fractal decomposition and a contractive mapping approach allowing us to project the before image on any after imaging modality type. This projection exploits the fact that any satellite image data can be approximatively encoded in terms of spatial self-similarities at different scales and this property remains quite invariant to a given imaging modality type. Once the projection is performed and that a pixelwise difference map between the two images (presented in the same imaging modality) is then binarized in the unsupervised Bayesian framework. At this stage, we will test several parameter estimation procedures combined with several segmentation strategies based on different Bayesian cost functions. The experiments for change detection, with real images showing different multimodalities and changed events, indicate that this new fractal-based projection method, which is entirely based on a series of structural and spatial information, is an interesting alternative to classical regression-based projection methods (based only on luminance transformation). Besides, the experiments also show that the difference map, resulting in this novel projection strategy, is also particularly amenable for an unsupervised Markovian binarization approach. Numéro de notice : A2020-682 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2986239 date de publication en ligne : 30/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2986239 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96207 [article]

Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones / Xun Liang in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 1930 - 1952 Titre : Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones Type de document : Article/Communication Auteurs : Xun Liang, Auteur ; Xiaoping Liu, Auteur ; Guangliang Chen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1930 - 1952 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] aide à la décision [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] zone d'activité économique Résumé : (auteur) Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (CA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified CA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, China from 2013 to 2019. The proposed model is applied to future simulation in 2019–2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs. Numéro de notice : A2020-513 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1741591 date de publication en ligne : 23/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1741591 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95668 [article]

A novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images / David Pirrone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 7 (July 2020) . - pp 4780 - 4795 Titre : A novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images Type de document : Article/Communication Auteurs : David Pirrone, Auteur ; Francesca Bovolo, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2020 Article en page(s) : pp 4780 - 4795 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] classification automatique [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] coordonnées polaires [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] méthode des vecteurs de changement [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] radar à antenne synthétique Résumé : (auteur) Change detection (CD) is a crucial topic in many remote sensing applications. In the recent years, satellite polarimetric synthetic aperture radar (PolSAR) systems (e.g., the Sentinel-1 constellation) became a suitable tool for multitemporal monitoring due to the regular acquisitions with a short revisit time in different polarimetric channels. Methods for CD in PolSAR data mainly focus on binary CD (i.e., they provide information about the presence/absence of change only), whereas the polarimetric enhanced information provides multiple features that can be exploited for performing multiclass CD. In this article, we introduce a novel framework for the characterization of multitemporal changes in dual-polarimetric data. The framework is based on the definition of polarimetric change vectors (PCVs) and their representation in a polar coordinate system. PCVs allow characterizing and, thus, to separate multiclass changes in terms of target properties of the single-time scenes and the scattering theory. The proposed model is used to: 1) derive the statistical behaviors of change and no change classes in PolSAR multitemporal images; 2) design an automatic and unsupervised strategy to estimate the optimal number of changes; and 3) distinguish no change from change classes and the kinds of change from each other. An experimental analysis has been conducted on three multitemporal PolSAR data sets having different complexities in terms of number and kinds of change classes. The results confirm the effectiveness of the proposed approach and the better performance with respect to both specific techniques for CD in dual-pol SAR data and a general multiclass CD method, not designed for PolSAR data. Numéro de notice : A2020-390 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2966865 date de publication en ligne : 04/02/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2966865 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95373 [article]

Unsupervised semantic and instance segmentation of forest point clouds / Di Wang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

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Coastline change modelling induced by climate change using geospatial techniques in Togo (West Africa) / Yawo Konko in Advances in Remote Sensing, vol 9 n° 2 (June 2020)  

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Unsupervised change detection between SAR images based on hypergraphs / Jun Wang in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps / Alper Sen in Survey review, vol 52 n° 371 (March 2020)  

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Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering / Liyuan Ma in European journal of remote sensing, vol 53 n°1 (2020)  

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Unsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters / Ting Mao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

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Saliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

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Satellite altimetry for earth sciences / Frédéric Frappart (2019)  

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Le vandalisme dans l’information géographique volontaire, détection de l’IG volontaire vandalisée : du concept à la détection non supervisée d’anomalie / Quy Thy Truong in Revue internationale de géomatique, vol 29 n° 1 (janvier - mars 2019)  

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Contextual classification using photometry and elevation data for damage detection after an earthquake event / Ewelina Rupnik in European journal of remote sensing, vol 51 n° 1 (2018)  

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