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Hyperspectral image fusion and multitemporal image fusion by joint sparsity / Han Pan in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7887 - 7900 Titre : Hyperspectral image fusion and multitemporal image fusion by joint sparsity Type de document : Article/Communication Auteurs : Han Pan, Auteur ; Zhongliang Jing, Auteur ; Henry Leung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7887 - 7900 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] correction d'image [Termes IGN] flou [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] image multitemporelle [Termes IGN] image panchromatique [Termes IGN] pansharpening (fusion d'images) [Termes IGN] représentation parcimonieuse Résumé : (auteur) Different image fusion systems have been developed to deal with the massive amounts of image data for different applications, such as remote sensing, computer vision, and environment monitoring. However, the generalizability and versatility of these fusion systems remain unknown. This article proposes an efficient regularization framework to achieve different kinds of fusion tasks accounting for the spatiospectral and spatiotemporal variabilities of the fusion process. A joint minimization functional is developed by taking an advantage of a composite regularizer for enforcing joint sparsity in the gradient domain and the frame domain. The proposed composite regularizer is composed of the Hessian Schatten-norm regularization and contourlet-based regularization terms. The resulting problems are solved by the alternating direction method of multipliers (ADMM). The effectiveness of the proposed method is validated in a variety of image fusion experiments: 1) hyperspectral (HS) and panchromatic image fusion; 2) HS and multispectral image fusion; 3) multitemporal image fusion (MIF); and 4) multi-image deblurring. Results show promising performance compared with state-of-the-art fusion methods. Numéro de notice : A2021-649 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3039046 date de publication en ligne : 07/12/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3039046 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98360 [article]

Semantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images / Donato Amitrano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 5494 - 5514 Titre : Semantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images Type de document : Article/Communication Auteurs : Donato Amitrano, Auteur ; Raffaella Guida, Auteur ; Pasquale Lervolino, Auteur Année de publication : 2021 Article en page(s) : pp 5494 - 5514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] biomasse forestière [Termes IGN] canopée [Termes IGN] changement d'occupation du sol [Termes IGN] classification floue [Termes IGN] classification non dirigée [Termes IGN] déboisement [Termes IGN] détection de changement [Termes IGN] image multitemporelle [Termes IGN] image radar moirée [Termes IGN] image RVB [Termes IGN] image Sentinel-SAR [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] segmentation d'image [Termes IGN] seuillage d'image [Termes IGN] texture d'image Résumé : (auteur) Change detection is one of the most addressed topics in the remote sensing community. When performed on synthetic aperture radar images, the most critical issues are as follows: 1) the labeling of the identified changing patterns and 2) the scarce robustness of classic pixel-based approaches based on threshold segmentation of an appropriate change index, which tend to fail when multiple changes are present in the study area. In this work, a new methodology for unsupervised change detection in vegetation canopy is presented. It overcomes these limitations by exploiting multitemporal geographical object-based image analysis with the aim to make the intrinsic semantic of data emerge and direct the processing toward the identification of precise classes of changes through dictionary-based preclassification and fuzzy combination of class-specific information layers. The proposed methodology has been tested in ten different experiments covering agriculture and clear-cut deforestation applications. The results, validated against literature methods, highlighted the superiority of the proposed approach, which was quantitatively assessed in terms of standard classification quality parameters. On agriculture experiments, it allowed for an average increase in the detection accuracy of about 11% with respect to the best performing literature method, with an increment of the false alarm rate in the order of 0.5%. In case of deforestation, the registered detection accuracy was comparable to that achieved by the literature, while the most significant benefit was the reduction, of more than one-third, of the number of detected false deforestation patterns. Overall, the main characteristics of the proposed architecture are the robustness and the lack of any supervision, which makes it very well-suited for operational scenarios. Numéro de notice : A2021-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3029841 date de publication en ligne : 22/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3029841 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97978 [article]

Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)  

Public [article]  inApplied geomatics > vol 13 n° 2 (June 2021) . - pp 261 - 275 Titre : Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery Type de document : Article/Communication Auteurs : Mahmoud Salah, Auteur Année de publication : 2021 Article en page(s) : pp 261 - 275 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] appariement d'histogramme [Termes IGN] champ aléatoire de Markov [Termes IGN] classification dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection de changement [Termes IGN] géoréférencement [Termes IGN] Giza [Termes IGN] image à très haute résolution [Termes IGN] image Geoeye [Termes IGN] image multitemporelle [Termes IGN] incertitude des données [Termes IGN] méthode robuste [Termes IGN] modèle de Markov caché [Termes IGN] occupation du sol [Termes IGN] réseau neuronal artificiel [Termes IGN] utilisation du sol Résumé : (auteur) Robust approaches for image change detection (ICD) are essential for a range of large-scale applications. However, the uncertainties involved in such approaches have not been fully addressed. To investigate this problem, this paper proposes a new approach for change detection from multi-temporal very high resolution (VHR) satellite imagery based on uncertainty detection and management. First, two GeoEye-1 images of Giza urban area (Egypt), acquired in 2009 and 2019, have been geographically co-registered and their histograms have been matched. Second, a set of feature attributes have been generated from the co-registered images. Third, the support vector machine (SVM) algorithm has been adopted to classify the data into four classes: building, tree, road, and ground. In this regard, the co-registered images along with the generated attributes have been applied as input data for the SVM to calculate the probability of each pixel belonging to each class. After that, the probability images for both epochs have been compared to model the uncertainty of changes. The uncertainty places are then evaluated to estimate their likelihood of being change or no change. Finally, the obtained results have been compared with manually digitized change detection map. Compared with using the widely used post-classification comparison (PCC) approach, the results suggest that (1) the proposed method has improved the overall accuracy of change detection by 13%; (2) the class-accuracies have been improved by 35.63%; and (3) the achieved accuracies for the proposed approach are less variable. Whereas the standard deviation (SD) of the accuracies obtained for the proposed approach is 6.80, the SD of those obtained for the PCC approach is 35.50. Numéro de notice : A2021-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00346-z date de publication en ligne : 28/10/2020 En ligne : https://doi.org/10.1007/s12518-020-00346-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97737 [article]

Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery / Rajeev Bhattarai in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 28 - 40 Titre : Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery Type de document : Article/Communication Auteurs : Rajeev Bhattarai, Auteur ; Parinaz Rahimzadeh-Bajgiran, Auteur ; Aaron R. Weiskittel, Auteur Année de publication : 2021 Article en page(s) : pp 28 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Abies balsamea [Termes IGN] carte de la végétation [Termes IGN] classification par forêts aléatoires [Termes IGN] défoliation [Termes IGN] dégradation de la flore [Termes IGN] image multibande [Termes IGN] image multitemporelle [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] indice de végétation [Termes IGN] insecte phyllophage [Termes IGN] Nouveau-Brunswick (Canada) [Termes IGN] Picea abies Résumé : (auteur) Spruce budworm (Choristoneura fumiferana; SBW) is the most destructive forest pest of northeastern Canada and United States. SBW occurrence as well as the extent and severity of its damage are highly dependent on the characteristics of the forests and the availability of host species namely, spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Remote sensing satellite imagery represents a valuable data source for seamless regional-scale mapping of forest composition. This study developed and evaluated new models to map the distribution and abundance of SBW host species at 20 m spatial resolution using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery in combination with several site variables for a total of 191 variables in northern New Brunswick, Canada using the Random Forest (RF) algorithm. We found Sentinel-2 multi-temporal single spectral bands and numerous spectral vegetation indices (SVIs) yielded the classification of SBW host species with an overall accuracy (OA) of 72.6% and kappa coefficient (K) of 0.65. Incorporating Sentinel-1 SAR data with Sentinel-2 variables coupled with elevation, only marginally improved the performance of the model (OA: 73.0% and K: 0.66). The use of Sentinel-1 SAR data with elevation resulted in a reasonable OA of 57.5% and K of 0.47. These spatially explicit up-to-date SBW host species maps are essential for identifying susceptible forests, monitoring SBW defoliation, and minimizing forest losses from insect impacts at landscape scale in the current SBW outbreak in the region. Numéro de notice : A2021-085 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.023 date de publication en ligne : 15/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.023 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96845 [article]

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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 IGN] analyse d'image orientée objet [Termes IGN] apprentissage profond [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction de données [Termes IGN] image à haute résolution [Termes IGN] image à très haute résolution [Termes IGN] image multitemporelle [Termes IGN] itération [Termes 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]

Object-based classification of mixed forest types in Mongolia / E. Nyamjargal in Geocarto international, vol 35 n° 14 ([15/10/2020])  

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Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

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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)  

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Reducing shadow effects on the co-registration of aerial image pairs / Matthew Plummer in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

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A novel fire index-based burned area change detection approach using Landsat-8 OLI data / Sicong Liu in European journal of remote sensing, vol 53 n° 1 (2020)  

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Analyse, structuration et sémantisation des images aériennes [diaporama] / Valérie Gouet-Brunet (2020) 

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Data Acquisition and Processing in Cultural Heritage / Gabriele Bitelli (2020)  

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Deep learning for semantic feature extraction in aerial imagery / Ananya Gupta (2020)  

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National scale identification and characterization of braided rivers in New Zealand using Google Earth Engine / Alexis Jean (2020) 

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Recherche multimodale d'images aériennes multi-date à l'aide d'un réseau siamois / Margarita Khokhlova (2020)    

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