Centre dedocumentation
scientifique

Learning-based hyperspectral imagery compression through generative neural networks / Chubo Deng in Remote sensing, vol 12 n° 21 (November 2020)  

Public [article]  inRemote sensing > vol 12 n° 21 (November 2020) . - n° 3657 Titre : Learning-based hyperspectral imagery compression through generative neural networks Type de document : Article/Communication Auteurs : Chubo Deng, Auteur ; Yi Cen, Auteur ; Lifu Zhang, Auteur Année de publication : 2020 Article en page(s) : n° 3657 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] compression d'image [Termes descripteurs IGN] compression par ondelettes [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] réseau neuronal artificiel Résumé : (auteur) Hyperspectral images (HSIs), which obtain abundant spectral information for narrow spectral bands (no wider than 10 nm), have greatly improved our ability to qualitatively and quantitatively sense the Earth. Since HSIs are collected by high-resolution instruments over a very large number of wavelengths, the data generated by such sensors is enormous, and the amount of data continues to grow, HSI compression technique will play more crucial role in this trend. The classical method for HSI compression is through compression and reconstruction methods such as three-dimensional wavelet-based techniques or the principle component analysis (PCA) transform. In this paper, we provide an alternative approach for HSI compression via a generative neural network (GNN), which learns the probability distribution of the real data from a random latent code. This is achieved by defining a family of densities and finding the one minimizing the distance between this family and the real data distribution. Then, the well-trained neural network is a representation of the HSI, and the compression ratio is determined by the complexity of the GNN. Moreover, the latent code can be encrypted by embedding a digit with a random distribution, which makes the code confidential. Experimental examples are presented to demonstrate the potential of the GNN to solve image compression problems in the field of HSI. Compared with other algorithms, it has better performance at high compression ratio, and there is still much room left for improvements along with the fast development of deep-learning techniques. Numéro de notice : A2020-720 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12213657 date de publication en ligne : 08/11/2020 En ligne : https://doi.org/10.3390/rs12213657 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96310 [article]

fusionImage: An R package for pan‐sharpening images in open source software / Fulgencio Cánovas‐García in Transactions in GIS, Vol 24 n° 5 (October 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1185-1207 Titre : fusionImage: An R package for pan‐sharpening images in open source software Type de document : Article/Communication Auteurs : Fulgencio Cánovas‐García, Auteur ; Paúl Pesántez‐Cobos, Auteur ; Francisco Alonso‐Sarría, Auteur Année de publication : 2020 Article en page(s) : pp 1185-1207 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de Gram-Schmidt [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] filtre passe-haut [Termes descripteurs IGN] logiciel libre [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] pouvoir de résolution géométrique [Termes descripteurs IGN] R (langage) Résumé : (Auteur) The objective of this article is to evaluate the performance of three pan‐sharpening algorithms (high‐pass filter, principal component analysis and Gram–Schmidt) to increase the spatial resolution of five types of multispectral images and to evaluate the results in terms of color, coherence and spatial sharpness, both qualitatively and quantitatively. A secondary objective is to present an implementation of the aforementioned pan‐sharpening techniques within the open source software R. From a qualitative point of view, pan‐sharpening of images with a high spatial resolution ratio give better results than those whose spatial resolution ratio is 2. According to the quantitative evaluation, there is no pan‐sharpening methodology that obtains optimal results simultaneously for all types of images used. The results of the spectral and spatial ERGAS index vary for four out of the five types of images analyzed. The results show that none of the methods implemented in this work can be considered a priori better than the others. At the same time, this work indicates the importance of both qualitative and quantitative assessment. Numéro de notice : A2020-499 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12676 date de publication en ligne : 15/09/2020 En ligne : https://doi.org/10.1111/tgis.12676 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96206 [article]

Multiscale supervised kernel dictionary learning for SAR target recognition / Lei Tao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6281 - 6297 Titre : Multiscale supervised kernel dictionary learning for SAR target recognition Type de document : Article/Communication Auteurs : Lei Tao, Auteur ; Xue Jiang, Auteur ; Xingzhao Liu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6281 - 6297 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] analyse non linéaire [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] erreur de classification [Termes descripteurs IGN] filter de Frost [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] méthode fondée sur le noyau [Termes descripteurs IGN] reconstruction d'image Résumé : (auteur) In this article, a supervised nonlinear dictionary learning (DL) method, called multiscale supervised kernel DL (MSK-DL), is proposed for target recognition in synthetic aperture radar (SAR) images. We use Frost filters with different parameters to extract an SAR image’s multiscale features for data augmentation and noise suppression. In order to reduce the computation cost, the dimension of each scale feature is reduced by principal component analysis (PCA). Instead of the widely used linear DL, we learn multiple nonlinear dictionaries to capture the nonlinear structure of data by introducing the dimension-reduced features into the nonlinear reconstruction error terms. A classification model, which is defined as a discriminative classification error term, is learned simultaneously. Hence, the objective function contains the nonlinear reconstruction error terms and a classification error term. Two optimization algorithms, called multiscale supervised kernel K-singular value decomposition (MSK-KSVD) and multiscale supervised incremental kernel DL (MSIK-DL), are proposed to compute the multidictionary and the classifier. Experiments on the moving and stationary target automatic recognition (MSTAR) data set are performed to evaluate the effectiveness of the two proposed algorithms. And the experimental results demonstrate that the proposed scheme outperforms some representative common machine learning strategies, state-of-the-art convolutional neural network (CNN) models and some representative DL methods, especially in terms of its robustness against training set size and noise. Numéro de notice : A2020-529 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2976203 date de publication en ligne : 03/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2976203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95709 [article]

Correction of systematic radiometric inhomogeneity in scanned aerial campaigns using principal component analysis / Lâmân Lelégard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 871 - 876 Titre : Correction of systematic radiometric inhomogeneity in scanned aerial campaigns using principal component analysis Type de document : Article/Communication Auteurs : Lâmân Lelégard , Auteur ; Arnaud Le Bris , Auteur ; Sébastien Giordano , Auteur Année de publication : 2020 Projets : HIATUS / Giordano, Sébastien Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 871 - 876 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme d'homogénéisation [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] correction radiométrique [Termes descripteurs IGN] image numérisée Résumé : (auteur) Orthophotomosaic is defined as a single image that can be layered on a map. The term “mosaic” implies that it is produced from a set of images, usually aerial images. Even if these images are taken during cloudless period, they are impaired by radiometric inhomogeneity mostly due to atmospheric phenomena, like hotspot, haze or high altitude clouds shadows as well as imaging device systematisms, like lens vignetting. These create some unsightly radiometric inhomogeneity in the orthophotomosaic that could be corrected by using a Wallis filter. Yet this solution leads to a significant loss of contrast at small scales. This work introduces an alternative to Wallis filter by considering some systematic radiometric behaviours in the images through a principal component analysis process. Numéro de notice : A2020-501 Affiliation des auteurs : LaSTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-871-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-871-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95642 [article]

A convolutional neural network with mapping layers for hyperspectral image classification / Rui Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3136 - 3147 Titre : A convolutional neural network with mapping layers for hyperspectral image classification Type de document : Article/Communication Auteurs : Rui Li, Auteur ; Zhibin Pan, Auteur ; Yang Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3136 - 3147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algèbre linéaire [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] analyse multidimensionnelle [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] couche thématique [Termes descripteurs IGN] dispersion [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] réduction Résumé : (auteur) In this article, we propose a convolutional neural network with mapping layers (MCNN) for hyperspectral image (HSI) classification. The proposed mapping layers map the input patch into a low-dimensional subspace by multilinear algebra. We use our mapping layers to reduce the spectral and spatial redundancies and maintain most energy of the input. The feature extracted by our mapping layers can also reduce the number of following convolutional layers for feature extraction. Our MCNN architecture avoids the declining accuracy with increasing layers phenomenon of deep learning models for HSI classification and also saves the training time for its effective mapping layers. Furthermore, we impose the 3-D convolutional kernel on the convolutional layer to extract the spectral–spatial features for HSI. We tested our MCNN on three data sets of Indian Pines, University of Pavia, and Salinas, and we achieved the classification accuracy of 98.3%, 99.5%, and 99.3%, respectively. Experimental results demonstrate that the proposed MCNN can significantly improve classification accuracy and save much time consumption. Numéro de notice : A2020-234 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2948865 date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2948865 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94980 [article]

Dimension reduction methods applied to coastline extraction on hyperspectral imagery / Ozan Arslan in Geocarto international, vol 35 n° 4 ([15/03/2020])  

Permalink

Plant survival monitoring with UAVs and multispectral data in difficult access afforested areas / Maria Luz Gil-Docampo in Geocarto international, vol 35 n° 2 ([01/02/2020])  

Permalink

Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])  

Permalink

Synergetic efficiency of Lidar and WorldView-2 for 3D urban cartography in Northeast Mexico / Fabiola D. Yepez-Rincon in Geocarto international, vol 34 n° 2 ([01/02/2019])  

Permalink

Application of Landsat-8 and ASTER satellite remote sensing data for porphyry copper exploration: a case study from Shahr-e-Babak, Kerman, south of Iran / Morteza Safari in Geocarto international, vol 33 n° 11 (November 2018)  

Permalink

On the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)  

Permalink

Influences of environmental loading corrections on the nonlinear variations and velocity uncertainties for the reprocessed global positioning system height time series of the crustal movement observation network of China / Peng Yuan in Remote sensing, vol 10 n° 6 (June 2018)    

Permalink

Long-term prediction of polar motion using a combined SSA and ARMA model / Y. Shen in Journal of geodesy, vol 92 n° 3 (March 2018)  

Permalink

Sensitivity analysis of pansharpening in hyperspectral change detection / Seyd Teymoor Seydi in Applied geomatics, vol 10 n° 1 (March 2018)  

Permalink

Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

Permalink