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Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
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Titre : Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data Type de document : Article/Communication Auteurs : Xiao-Ming Li, Auteur ; Yan Sun, Auteur ; Qiang Zhang, Auteur Année de publication : 2021 Article en page(s) : pp 3040 - 3053 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Arctique, océan
[Termes IGN] classification non dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] entropie
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] glace de mer
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] polarisation croisée
[Termes IGN] rétrodiffusion
[Termes IGN] texture d'imageRésumé : (auteur) In this article, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization [vertical–horizontal (VH) or horizontal–vertical (HV)] data in extra-wide (EW) swath mode based on the support vector machine (SVM) method. The classification basis includes the S1 radar backscatter and texture features, which are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e., entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparisons based on a few cases show good agreements between the synthetic aperture radar (SAR)-derived sea ice cover using the proposed method and visual inspections, of which the accuracy reaches approximately 90%–95%. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of the extracted sea ice cover by using S1 data is more than 80%. Numéro de notice : A2021-284 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3007789 Date de publication en ligne : 20/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3007789 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97392
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3040 - 3053[article]A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection / Xi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)
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Titre : A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection Type de document : Article/Communication Auteurs : Xi Wu, Auteur ; Zhenwei Shi, Auteur ; Zhengxia Zou, Auteur Année de publication : 2021 Article en page(s) : pp 87 - 104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] altitude
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection des nuages
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données
[Termes IGN] image Gaofen
[Termes IGN] information géographique
[Termes IGN] latitude
[Termes IGN] longitude
[Termes IGN] modèle statistique
[Termes IGN] neige
[Termes IGN] Normalized Difference Snow IndexRésumé : (auteur) Geographic information such as the altitude, latitude, and longitude are common but fundamental meta-records in remote sensing image products. In this paper, it is shown that such a group of records provides important priors for cloud and snow detection in remote sensing imagery. The intuition comes from some common geographical knowledge, where many of them are important but are often overlooked. For example, it is generally known that snow is less likely to exist in low-latitude or low-altitude areas, and clouds in different geographic may have various visual appearances. Previous cloud and snow detection methods simply ignore the use of such information, and perform detection solely based on the image data (band reflectance). Due to the neglect of such priors, most of these methods are difficult to obtain satisfactory performance in complex scenarios (e.g., cloud-snow coexistence). In this paper, a novel neural network called “Geographic Information-driven Network (GeoInfoNet)” is proposed for cloud and snow detection. In addition to the use of the image data, the model integrates the geographic information at both training and detection phases. A “geographic information encoder” is specially designed, which encodes the altitude, latitude, and longitude of imagery to a set of auxiliary maps and then feeds them to the detection network. The proposed network can be trained in an end-to-end fashion with dense robust features extracted and fused. A new dataset called “Levir_CS” for cloud and snow detection is built, which contains 4,168 Gaofen-1 satellite images and corresponding geographical records, and is over 20× larger than other datasets in this field. On “Levir_CS”, experiments show that the method achieves 90.74% intersection over union of cloud and 78.26% intersection over union of snow. It outperforms other state of the art cloud and snow detection methods with a large margin. Feature visualizations also show that the method learns some important priors which is close to the common sense. The proposed dataset and the code of GeoInfoNet are available in https://github.com/permanentCH5/GeoInfoNet. Numéro de notice : A2021-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.023 Date de publication en ligne : 22/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.023 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97187
in ISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 87 - 104[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021041 SL Revue Centre de documentation Revues en salle Disponible 081-2021043 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021042 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Graph convolutional networks by architecture search for PolSAR image classification / Hongying Liu in Remote sensing, vol 13 n° 7 (April-1 2021)
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Titre : Graph convolutional networks by architecture search for PolSAR image classification Type de document : Article/Communication Auteurs : Hongying Liu, Auteur ; Derong Xu, Auteur ; Tianwen Zhu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 1404 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] bande L
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification semi-dirigée
[Termes IGN] échantillon
[Termes IGN] graphe
[Termes IGN] image AIRSAR
[Termes IGN] image radar moirée
[Termes IGN] noeud
[Termes IGN] polarimétrie radar
[Termes IGN] réseau neuronal de graphesRésumé : (auteur) Classification of polarimetric synthetic aperture radar (PolSAR) images has achieved good results due to the excellent fitting ability of neural networks with a large number of training samples. However, the performance of most convolutional neural networks (CNNs) degrades dramatically when only a few labeled training samples are available. As one well-known class of semi-supervised learning methods, graph convolutional networks (GCNs) have gained much attention recently to address the classification problem with only a few labeled samples. As the number of layers grows in the network, the parameters dramatically increase. It is challenging to determine an optimal architecture manually. In this paper, we propose a neural architecture search method based GCN (ASGCN) for the classification of PolSAR images. We construct a novel graph whose nodes combines both the physical features and spatial relations between pixels or samples to represent the image. Then we build a new searching space whose components are empirically selected from some graph neural networks for architecture search and develop the differentiable architecture search method to construction our ASGCN. Moreover, to address the training of large-scale images, we present a new weighted mini-batch algorithm to reduce the computing memory consumption and ensure the balance of sample distribution, and also analyze and compare with other similar training strategies. Experiments on several real-world PolSAR datasets show that our method has improved the overall accuracy as much as 3.76% than state-of-the-art methods. Numéro de notice : A2021-350 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13071404 Date de publication en ligne : 06/04/2021 En ligne : https://doi.org/10.3390/rs13071404 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97600
in Remote sensing > vol 13 n° 7 (April-1 2021) . - n° 1404[article]Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
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Titre : Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework Type de document : Article/Communication Auteurs : Peyman Azimpour, Auteur ; Tahereh Bahraini, Auteur ; Hadi Sadoghi Yazdi, Auteur Année de publication : 2021 Article en page(s) : pp 3266 - 3276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification bayesienne
[Termes IGN] classification floue
[Termes IGN] distribution de Gauss
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] filtrage du bruit
[Termes IGN] filtre de Gauss
[Termes IGN] image hyperspectrale
[Termes IGN] Matlab
[Termes IGN] processeur graphique
[Termes IGN] qualité des données
[Termes IGN] variableRésumé : (auteur) The hyperspectral-image (HSI) noise-reduction step is a very significant preprocessing phase of data-quality enhancement. It has been attracting immense research attention in the remote sensing and image processing domains. Many methods have been developed for HSI restoration, the goal of which is to remove noise from the whole HSI cube simultaneously without considering the spectral–spatial similarity. When a noise-removal algorithm is used globally to the entire data set, it would not eliminate all levels of noise, effectively. Furthermore, most of the existing methods remove independent and identically distributed (i.i.d.) Gaussian noise. The real scenarios are much more complicated than this assumption. The complexity created by natural noise that has a non-i.i.d. structure leads to inefficient methods containing underestimation and invalid performance. In this article, we calculated the spatial–spectral similarity criteria by defining a set of clustering-based latent variables (CLVs) in a Bayesian framework to improve the robustness. These criteria can be extracted using the clustering operators. Then, by applying the CLV to the variational Bayesian model, we investigated a new low-rank matrix factorization denoising approach based on the proposed clustering-based latent variable (CLV-LRMF) to remove noise with the non-i.i.d. mixture of Gaussian structures. Finally, we switched to the GPU for MATLAB implementation to reduce the runtime. The experimental results show that the performance has been improved by applying the proposed CLV and demonstrate the effectiveness of the proposed CLV-LRMF over other state-of-the-art methods. Numéro de notice : A2021-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2939512 Date de publication en ligne : 24/03/2021 En ligne : https://doi.org/10.1109/TGRS.2019.2939512 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97396
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3266 - 3276[article]A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)
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Titre : A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Ting Bai, Auteur ; Kaimin Sun, Auteur ; Wenzhuo Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 249-262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement d'occupation du sol
[Termes IGN] classe d'objets
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection de changement
[Termes IGN] détection du bâti
[Termes IGN] image à haute résolution
[Termes IGN] milieu urbain
[Termes IGN] segmentation multi-échelleRésumé : (Auteur) A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data. Numéro de notice : A2021-332 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.249 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.249 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97528
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 249-262[article]Réservation
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