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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 12 (December 2019) . - pp 9554 - 9574 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 points Résumé : (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 [article]

Hyperspectral image classification with squeeze multibias network / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1291 - 1301 Titre : Hyperspectral image classification with squeeze multibias network Type de document : Article/Communication Auteurs : Leyuan Fang, Auteur ; Guangyun Liu, Auteur ; Shutao Li, Auteur ; Pedram Ghamisi, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2019 Article en page(s) : pp 1291 - 1301 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] erreur systématique [Termes IGN] image hyperspectrale Résumé : (Auteur) A convolutional neural network (CNN) has recently demonstrated its outstanding capability for the classification of hyperspectral images (HSIs). Typical CNN-based methods usually adopt image patches as inputs to the network. However, a fixed-size image patch in HSI with complex spatial contexts may contain multiple ground objects of different classes, which will deteriorate the classification performance of the CNN. In addition, traditional convolutional layers adopted in the CNN have a huge amount of parameters needed to be tuned, which will cause high computational cost. To address the above-mentioned issues, a novel squeeze multibias network (SMBN) is proposed for HSI classification. Specifically, the proposed SMBN first introduces the multibias module (MBM), which incorporates multibias into the rectified linear unit layers. The MBM can decouple the feature maps of input patches into multiple response maps (corresponding to different ground objects) and adaptively select the meaningful maps for classification. Furthermore, the proposed SMBN replaces the traditional convolutional layer with a squeeze convolution module, which can greatly reduce the number of parameters in the network, thus saving the running time, while still maintaining high classification accuracy. Experimental results on three real HSIs demonstrate the superiority of the proposed SMBN method over several state-of-the-art classification approaches. Numéro de notice : A2019-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2865953 Date de publication en ligne : 13/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2865953 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92453 [article]

Adaptive spectral–spatial compression of hyperspectral image with sparse representation / Wei Fu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 2 (February 2017) . - pp 671 - 682 Titre : Adaptive spectral–spatial compression of hyperspectral image with sparse representation Type de document : Article/Communication Auteurs : Wei Fu, Auteur ; Shutao Li, Auteur ; Leyuan Fang, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2017 Article en page(s) : pp 671 - 682 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] codage [Termes IGN] compression d'image [Termes IGN] image hyperspectrale [Termes IGN] pixel [Termes IGN] représentation parcimonieuse [Termes IGN] zone homogène Résumé : (Auteur) Sparse representation (SR) can transform spectral signatures of hyperspectral pixels into sparse coefficients with very few nonzero entries, which can efficiently be used for compression. In this paper, a spectral-spatial adaptive SR (SSASR) method is proposed for hyperspectral image (HSI) compression by taking advantage of the spectral and spatial information of HSIs. First, we construct superpixels, i.e., homogeneous regions with adaptive sizes and shapes, to describe HSIs. Since homogeneous regions usually consist of similar pixels, pixels within each superpixel will be similar and share similar spectral signatures. Then, the spectral signatures of each superpixel can be simultaneously coded in the SR model to exploit their joint sparsity. Since different superpixels generally have different performances of SR, their rate-distortion performances in the sparse coding will be different. To achieve the best possible overall rate-distortion performance, an adaptive coding scheme is introduced to adaptively assign distortions to superpixels. Finally, the obtained sparse coefficients are quantized and entropy coded and constitute the final bitstream with the coded superpixel map. The experimental results over several HSIs show that the proposed SSASR method outperforms some state-of-the-art HSI compression methods in terms of the rate-distortion and spectral fidelity performances. Numéro de notice : A2017-141 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2613848 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2613848 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84629 [article]

Random-walker-based collaborative learning for hyperspectral image classification / Bin Sun in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 1 (January 2017) . - pp 212 - 222 Titre : Random-walker-based collaborative learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Bin Sun, Auteur ; Xudong Kang, Auteur ; Shutao Li, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2017 Article en page(s) : pp 212 - 222 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage semi-dirigé [Termes IGN] champ aléatoire de Markov [Termes IGN] classification [Termes IGN] image hyperspectrale Résumé : (Auteur) Active learning (AL) and semisupervised learning (SSL) are both promising solutions to hyperspectral image classification. Given a few initial labeled samples, this work combines AL and SSL in a novel manner, aiming to obtain more manually labeled and pseudolabeled samples and use them together with the initial labeled samples to improve the classification performance. First, based on a comparison of the segmentation and spectral-spatial classification results obtained by random walker (RW) and extended RW (ERW) algorithms, the unlabeled samples are separated into two different sets, i.e., low- and high-confidence unlabeled data sets. For the high-confidence unlabeled data, pseudolabeling is performed, which can ensure the correctness and informativeness of the pseudolabeled samples. For the low-confidence unlabeled data, AL is used to select samples. In this way, the samples which are more effective for improvement of classification performance can be labeled in only a few iterations. Finally, with the learned training set and the original hyperspectral image as inputs, the ERW classifier is used to obtain the final classification result. Experiments performed on three real hyperspectral data sets show that the proposed method can achieve competitive classification accuracy even with a very limited number of manually labeled samples. Numéro de notice : A2017-019 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2604290 En ligne : https://doi.org/10.1109/TGRS.2016.2604290 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83950 [article]

Quantitative quality evaluation of pansharpened imagery: consistency versus synthesis / Frosti Palsson in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1247 - 1259 Titre : Quantitative quality evaluation of pansharpened imagery: consistency versus synthesis Type de document : Article/Communication Auteurs : Frosti Palsson, Auteur ; Johannes R. Sveinsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2016 Article en page(s) : pp 1247 - 1259 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] cohérence des données [Termes IGN] évaluation [Termes IGN] fusion d'images [Termes IGN] image de synthèse [Termes IGN] image Quickbird [Termes IGN] image Worldview [Termes IGN] pansharpening (fusion d'images) [Termes IGN] problème inverse [Termes IGN] qualité des données Résumé : (Auteur) Pansharpening is the process of fusing a high-resolution panchromatic image and a low-spatial-resolution multispectral image to yield a high-spatial-resolution multispectral image. This is a typical ill-posed inverse problem, and in the past two decades, many methods have been proposed to solve it. Still, there is no general consensus on the best way to quantitatively evaluate the spectral and spatial quality of the fused image. In this paper, we compare the two most widely used and accepted methods for quality evaluation. The first method is the verification of the synthesis property which states that the fused image should be as identical as possible to the multispectral image that the sensor would observe at a higher resolution. This is impossible to verify unless the observed images are spatially degraded so that the original observed multispectral image can be used as reference. The second method is to use metrics that do not use a reference, such as the quality no reference (QNR) metrics. However, there is another property, i.e., the consistency property, which states that the fused image reduced to the resolution of the original multispectral image should be as identical to the original image as possible. This has generally been considered a necessary condition that does not have to imply correct fusion. Using real WorldView-2 and QuickBird data and a total of 18 component substitution and multiresolution analysis methods, we demonstrate that the consistency property can indeed be used to give reliable assessment of the relative performance of pansharpening methods and is superior to using the QNR metrics. Numéro de notice : A2016-126 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2476513 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2476513 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80007 [article]

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