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A deep learning framework for matching of SAR and optical imagery / Lloyd Haydn Hughes in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 166 - 179 Titre : A deep learning framework for matching of SAR and optical imagery Type de document : Article/Communication Auteurs : Lloyd Haydn Hughes, Auteur ; Diego Marcos, Auteur ; Sylvain Lobry, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 166 - 179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] données clairsemées [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] géoréférencement [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] superposition d'images Résumé : (auteur) SAR and optical imagery provide highly complementary information about observed scenes. A combined use of these two modalities is thus desirable in many data fusion scenarios. However, any data fusion task requires measurements to be accurately aligned. While for both data sources images are usually provided in a georeferenced manner, the geo-localization of optical images is often inaccurate due to propagation of angular measurement errors. Many methods for the matching of homologous image regions exist for both SAR and optical imagery, however, these methods are unsuitable for SAR-optical image matching due to significant geometric and radiometric differences between the two modalities. In this paper, we present a three-step framework for sparse image matching of SAR and optical imagery, whereby each step is encoded by a deep neural network. We first predict regions in each image which are deemed most suitable for matching. A correspondence heatmap is then generated through a multi-scale, feature-space cross-correlation operator. Finally, outliers are removed by classifying the correspondence surface as a positive or negative match. Our experiments show that the proposed approach provides a substantial improvement over previous methods for SAR-optical image matching and can be used to register even large-scale scenes. This opens up the possibility of using both types of data jointly, for example for the improvement of the geo-localization of optical satellite imagery or multi-sensor stereogrammetry. Numéro de notice : A2020-639 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.012 date de publication en ligne : 03/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96062 [article]

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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]

High-resolution remote sensing image scene classification via key filter bank based on convolutional neural network / Fengpeng Li 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 8077 - 8092 Titre : High-resolution remote sensing image scene classification via key filter bank based on convolutional neural network Type de document : Article/Communication Auteurs : Fengpeng Li, Auteur ; Ruyi Feng, Auteur ; Wei Han, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8077 - 8092 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] filtrage numérique d'image [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] test statistique Résumé : (auteur) High-resolution remote sensing (HRRS) image scene classification has attracted an enormous amount of attention due to its wide application in a range of tasks. Due to the rapid development of deep learning (DL), models based on convolutional neural network (CNN) have made competitive achievements on HRRS image scene classification because of the excellent representation capacity of DL. The scene labels of HRRS images extremely depend on the combination of global information and information from key regions or locations. However, most existing models based on CNN tend only to represent the global features of images or overstate local information capturing from key regions or locations, which may confuse different categories. To address this issue, a key region or location capturing method called key filter bank (KFB) is proposed in this article, and KFB can retain global information at the same time. This method can combine with different CNN models to improve the performance of HRRS imagery scene classification. Moreover, for the convenience of practical tasks, an end-to-end model called KFBNet where KFB combined with DenseNet-121 is proposed to compare the performance with existing models. This model is evaluated on public benchmark data sets, and the proposed model makes better performance on benchmarks than the state-of-the-art methods. Numéro de notice : A2020-683 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2987060 date de publication en ligne : 23/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2987060 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96208 [article]

Mapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery / Astrid Helena Huechacona-Ruiz in Forests, vol 11 n°11 (November 2020)  

Public [article]  inForests > vol 11 n°11 (November 2020) . - n° 1234 Titre : Mapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery Type de document : Article/Communication Auteurs : Astrid Helena Huechacona-Ruiz, Auteur ; Juan Manuel Dupuy, Auteur ; Naomi B. Schwartz, Auteur Année de publication : 2020 Article en page(s) : n° 1234 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] texture d'image [Termes descripteurs IGN] Yucatan Résumé : (auteur) In tropical dry forests, deciduousness (i.e., leaf shedding during the dry season) is an important adaptation of plants to cope with water limitation, which helps trees adjust to seasonal drought. Deciduousness is also a critical factor determining the timing and duration of carbon fixation rates, and affecting energy, water, and carbon balance. Therefore, quantifying deciduousness is vital to understand important ecosystem processes in tropical dry forests. The aim of this study was to map tree species deciduousness in three types of tropical dry forests along a precipitation gradient in the Yucatan Peninsula using Sentinel-2 imagery. We propose an approach that combines reflectance of visible and near-infrared bands, normalized difference vegetation index (NDVI), spectral unmixing deciduous fraction, and several texture metrics to estimate the spatial distribution of tree species deciduousness. Deciduousness in the study area was highly variable and decreased along the precipitation gradient, while the spatial variation in deciduousness among sites followed an inverse pattern, ranging from 91.5 to 43.3% and from 3.4 to 9.4% respectively from the northwest to the southeast of the peninsula. Most of the variation in deciduousness was predicted jointly by spectral variables and texture metrics, but texture metrics had a higher exclusive contribution. Moreover, including texture metrics as independent variables increased the variance of deciduousness explained by the models from R2 = 0.56 to R2 = 0.60 and the root mean square error (RMSE) was reduced from 16.9% to 16.2%. We present the first spatially continuous deciduousness map of the three most important vegetation types in the Yucatan Peninsula using high-resolution imagery. Numéro de notice : A2020-756 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11111234 date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.3390/f11111234 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96468 [article]

River ice segmentation with deep learning / Abhineet Singh 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 7570 - 7579 Titre : River ice segmentation with deep learning Type de document : Article/Communication Auteurs : Abhineet Singh, Auteur ; Hayden Kalke, Auteur ; Mark Loewen, Auteur Année de publication : 2020 Article en page(s) : pp 7570 - 7579 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage non-dirigé [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Canada [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] glace [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] rivière [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation sémantique Résumé : (auteur) This article deals with the problem of computing surface concentrations for two types of river ice from digital images acquired during freeze-up. It presents the results of attempting to solve this problem using several state-of-the-art semantic segmentation methods based on deep convolutional neural networks (CNNs). This task presents two main challenges—very limited availability of labeled training data and presence of noisy labels due to the great difficulty of visually distinguishing between the two types of ice, even for human experts. The results are used to analyze the extent to which some of the best deep learning methods currently in existence can handle these challenges. The code and data used in the experiments are made publicly available to facilitate further work in this domain. Numéro de notice : A2020-674 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2981082 date de publication en ligne : 13/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2981082 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96165 [article]

Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city / Azam Raha Bahrehdar in Computers, Environment and Urban Systems, vol 84 (November 2020)  

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Topographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México / Nelly L. Ramirez Serrato in Geocarto international, vol 35 n° 15 ([01/11/2020])  

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Urban tree species identification and carbon stock mapping for urban green planning and management / MD Abdul Choudhury in Forests, vol 11 n°11 (November 2020)  

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Drought stress detection in juvenile oilseed rape using hyperspectral imaging with a focus on spectra variability / Wiktor R. Żelazny in Remote sensing, vol 12 n° 20 (October 2020)  

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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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Textural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])  

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Application of convolutional and recurrent neural networks for buried threat detection using ground penetrating radar data / Mahdi Moalla in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)  

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Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution / Vitor Martins in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

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A graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)  

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Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data / Yaotong Cai in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)  

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