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Novel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images / Zhi Yong Lv in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)
[article]
Titre : Novel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images Type de document : Article/Communication Auteurs : Zhi Yong Lv, Auteur ; Tong Fei Liu, Auteur ; Zhang Penglin, Auteur ; Jon Atli Benediktsson, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 9554 - 9574 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] changement d'occupation du sol
[Termes IGN] Chine
[Termes IGN] classification pixellaire
[Termes IGN] détection de changement
[Termes IGN] histogramme
[Termes IGN] Hong-Kong
[Termes IGN] image à très haute résolution
[Termes IGN] phénologie
[Termes IGN] seuillage de pointsRésumé : (auteur) Detecting land cover change through very-high-resolution (VHR) remote sensing images is helpful in supporting urban sustainable development, natural disaster evaluation, and environmental assessment. However, the intraclass spectral variance in VHR remote sensing images is usually larger than that of median-low remote sensing images. Furthermore, the bitemporal images are usually acquired under different atmospheric conditions, sun height, soil moisture, and other factors. Consequently, in practical applications, many pseudo changes are presented in the detected map. In this paper, an adaptive histogram trend (AHT) similarity approach is promoted to quantitatively measure the magnitude between the corresponding pixels in bitemporal images in terms of change semantic. In the proposed approach, to reduce the phenological effect on the bitemporal images of land cover change detection (LCCD), we first define the quantitative description of AHT. Second, the change magnitudes between pairwise pixels are quantitatively measured by an improved bin-to-bin (B2B) distance between the corresponding AHTs. Then, the change magnitudes between two entire bitemporal images are measured AHT-by-AHT. Finally, binary threshold methods, such as the Otsu method or the double-window flexible pace search (DFPS) method, are used to divide the change magnitude image into binary change detection maps and obtain the final change detection map. The performance of the AHT-based LCCD approach is verified by four pairs of VHR remote-sensing images that correspond to two types of real land cover change cases. The detected results based on the four pairs of bitemporal VHR images outperformed the compared state-of-the-art LCCD methods. Numéro de notice : A2019-599 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2927659 Date de publication en ligne : 01/08/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2927659 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94593
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 12 (December 2019) . - pp 9554 - 9574[article]Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images / Cheolhee Yoo in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)
[article]
Titre : Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images Type de document : Article/Communication Auteurs : Cheolhee Yoo, Auteur ; Daehyeon Han, Auteur ; Jungho Im, Auteur ; Benjamin Bechtel, Auteur Année de publication : 2019 Article en page(s) : pp 155 - 170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] Chicago (Illinois)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] climat urbain
[Termes IGN] Hong-Kong
[Termes IGN] ilot thermique urbain
[Termes IGN] image Landsat-8
[Termes IGN] Madrid (Espagne)
[Termes IGN] Rome
[Termes IGN] World Urban Database and Access Portal Tools
[Termes IGN] zone urbaine denseRésumé : (Auteur) The Local Climate Zone (LCZ) scheme is a classification system providing a standardization framework to present the characteristics of urban forms and functions, especially for urban heat island (UHI) research. Landsat-based 100 m resolution LCZ maps have been classified by the World Urban Database and Portal Tool (WUDAPT) method using a random forest (RF) machine learning classifier. Some studies have proposed modified RF and convolutional neural network (CNN) approaches. This study aims to compare CNN with an RF classifier for LCZ mapping in great detail. We designed five schemes (three RF-based schemes (S1–S3) and two CNN-based ones (S4–S5)), which consist of various combinations of input features from bitemporal Landsat 8 data over four global mega cities: Rome, Hong Kong, Madrid, and Chicago. Among the five schemes, the CNN-based one with the incorporation of a larger neighborhood information showed the best classification performance. When compared to the WUDAPT workflow, the overall accuracies for entire land cover classes (OA) and for urban LCZ types (i.e., LCZ1-10; OAurb) increased by about 6–8% and 10–13%, respectively, for the four cities. The transferability of LCZ models for the four cities were evaluated, showing that CNN consistently resulted in higher accuracy (increased by about 7–18% and 18–29% for OA and OAurb, respectively) than RF. This study revealed that the CNN classifier classified particularly well for the specific LCZ classes in which buildings were mixed with trees or buildings or plants were sparsely distributed. The research findings can provide a basis for guidance of future LCZ classification using deep learning. Numéro de notice : A2019-495 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.009 Date de publication en ligne : 19/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93728
in ISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 155 - 170[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019113 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019112 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Context pyramidal network for stereo matching regularized by disparity gradients / Junhua Kang in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)
[article]
Titre : Context pyramidal network for stereo matching regularized by disparity gradients Type de document : Article/Communication Auteurs : Junhua Kang, Auteur ; Lin Chen, Auteur ; Fei Deng, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 201 - 215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] appariement de formes
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] gradient
[Termes IGN] vision par ordinateur
[Termes IGN] vision stéréoscopiqueRésumé : (Auteur) Also after many years of research, stereo matching remains to be a challenging task in photogrammetry and computer vision. Recent work has achieved great progress by formulating dense stereo matching as a pixel-wise learning task to be resolved with a deep convolutional neural network (CNN). However, most estimation methods, including traditional and deep learning approaches, still have difficulty to handle real-world challenging scenarios, especially those including large depth discontinuity and low texture areas.
To tackle these problems, we investigate a recently proposed end-to-end disparity learning network, DispNet (Mayer et al., 2015), and improve it to yield better results in these problematic areas. The improvements consist of three major contributions. First, we use dilated convolutions to develop a context pyramidal feature extraction module. A dilated convolution expands the receptive field of view when extracting features, and aggregates more contextual information, which allows our network to be more robust in weakly textured areas. Second, we construct the matching cost volume with patch-based correlation to handle larger disparities. We also modify the basic encoder-decoder module to regress detailed disparity images with full resolution. Third, instead of using post-processing steps to impose smoothness in the presence of depth discontinuities, we incorporate disparity gradient information as a gradient regularizer into the loss function to preserve local structure details in large depth discontinuity areas.
We evaluate our model in terms of end-point-error on several challenging stereo datasets including Scene Flow, Sintel and KITTI. Experimental results demonstrate that our model decreases the estimation error compared with DispNet on most datasets (e.g. we obtain an improvement of 46% on Sintel) and estimates better structure-preserving disparity maps. Moreover, our proposal also achieves competitive performance compared to other methods.Numéro de notice : A2019-496 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.012 Date de publication en ligne : 27/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93729
in ISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 201 - 215[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019113 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019112 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery / Yuri Shendryk in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)
[article]
Titre : Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery Type de document : Article/Communication Auteurs : Yuri Shendryk, Auteur ; Yannik Rist, Auteur ; Catherine Ticehurst, Auteur ; Peter Thorburn, Auteur Année de publication : 2019 Article en page(s) : pp 124 - 136 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Amazonie
[Termes IGN] apprentissage profond
[Termes IGN] Australie
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'ombre
[Termes IGN] état de l'art
[Termes IGN] image à haute résolution
[Termes IGN] image PlanetScope
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] nuage
[Termes IGN] occupation du sol
[Termes IGN] zone tropicale humideRésumé : (Auteur) With the increasing availability of high-resolution satellite imagery it is important to improve the efficiency and accuracy of satellite image indexing, retrieval and classification. Furthermore, there is a need for utilizing all available satellite imagery in identifying general land cover types and monitoring their changes through time irrespective of their spatial, spectral, temporal and radiometric resolutions. Therefore, in this study, we developed deep learning models able to efficiently and accurately classify cloud, shadow and land cover scenes in different high-resolution ( Numéro de notice : A2019-494 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.08.018 Date de publication en ligne : 17/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.018 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93727
in ISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 124 - 136[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019113 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019112 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt A double-strategy-check active learning algorithm for hyperspectral image classification / Ying Cui in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 11 (November 2019)
[article]
Titre : A double-strategy-check active learning algorithm for hyperspectral image classification Type de document : Article/Communication Auteurs : Ying Cui, Auteur ; Xiaowei Ji, Auteur ; Kai Xu, Auteur ; Liguo Wang, Auteur Année de publication : 2019 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] classification semi-dirigée
[Termes IGN] image hyperspectraleRésumé : (Auteur) Applying limited labeled samples to improve classification results is a challenge in hyperspectral images. Active Learning (AL) and Semisupervised Learning (SSL) are two promising techniques to achieve this challenge. Combining AL with SSL is an excellent idea for hyperspectral image classification. The traditional method, such as the Collaborative Active and Semisupervised Learning algorithm (CASSL), may introduce many incorrect pseudolabels and shows premature convergence. To overcome these drawbacks, a novel framework named Double-Strategy-Check Collaborative Active and Semisupervised Learning (DSC-CASSL) is proposed in this paper. This framework combines two different AL algorithms and SSL in a collaborative mode. The double-strategy verification can gradually improve the pseudolabeling accuracy and facilitate SSL. We evaluate the performance of DSC-CASSL on four hyperspectral data sets and compare it with that of four hyperspectral image classification methods. Our results suggest that DSC-CASSL leads to consistent improvement for hyperspectral image classification. Numéro de notice : A2019-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.85.11.841 Date de publication en ligne : 01/11/2019 En ligne : https://doi.org/10.14358/PERS.85.11.841 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94067
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 11 (November 2019)[article]Réservation
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