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Learning-based hyperspectral imagery compression through generative neural networks / Chubo Deng in Remote sensing, vol 12 n° 21 (November 2020)
[article]
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 IGN] analyse en composantes principales
[Termes IGN] apprentissage profond
[Termes IGN] compression d'image
[Termes IGN] compression par ondelettes
[Termes IGN] image hyperspectrale
[Termes IGN] réseau neuronal artificielRésumé : (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
in Remote sensing > vol 12 n° 21 (November 2020) . - n° 3657[article]VNIR-SWIR superspectral mineral mapping: An example from Cuprite, Nevada / Kathleen E. Johnson in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 11 (November 2020)
[article]
Titre : VNIR-SWIR superspectral mineral mapping: An example from Cuprite, Nevada Type de document : Article/Communication Auteurs : Kathleen E. Johnson, Auteur ; Krzysztof Koperski, Auteur Année de publication : 2020 Article en page(s) : pp 695 - 700 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] cartographie géologique
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] image proche infrarouge
[Termes IGN] image Worldview
[Termes IGN] minéralogie
[Termes IGN] Nevada (Etats-Unis)
[Termes IGN] réalité de terrain
[Termes IGN] Short Waves InfraRedRésumé : (Auteur) Cuprite, Nevada, is a location well known for numerous studies of its hydrothermal mineralogy. This region has been used to validate geological interpretations of airborne hyperspectral imagery (AVIRIS HSI ), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ) imagery, and most recently eight-band WorldView-3 shortwave infrared (SWIR ) imagery. WorldView-3 is a high-spatial-resolution commercial multispectral satellite sensor with eight visible-to-near-infrared (VNIR ) bands (0.42–1.04 μm) and eight SWIR bands (1.2–2.33 μm). We have applied mineral mapping techniques to all 16 bands to perform a geological analysis of the Cuprite, Nevada, location. Ground truth for the training and validation was derived from AVIRIS hyperspectral data and United States Geological Survey mineral spectral data for this location. We present the results of a supervised mineral-mapping classification applying a random-forest classifier. Our results show that with good ground truth, WorldView-3 SWIR + VNIR imagery produces an accurate geological assessment. Numéro de notice : A2020-709 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.11.695 Date de publication en ligne : 01/11/2020 En ligne : https://doi.org/10.14358/PERS.86.11.695 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96395
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 11 (November 2020) . - pp 695 - 700[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2020111 SL Revue Centre de documentation Revues en salle Disponible 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-2 2020)
[article]
Titre : Drought stress detection in juvenile oilseed rape using hyperspectral imaging with a focus on spectra variability Type de document : Article/Communication Auteurs : Wiktor R. Żelazny, Auteur ; Jan Lukáš, Auteur Année de publication : 2020 Article en page(s) : 27 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Brassica napus subsp. napus
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification pixellaire
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] modèle linéaire
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] sécheresse
[Termes IGN] segmentation d'image
[Termes IGN] stress hydriqueRésumé : (auteur) Hyperspectral imaging (HSI) has been gaining recognition as a promising proximal and remote sensing technique for crop drought stress detection. A modelling approach accounting for the treatment effects on the stress indicators’ standard deviations was applied to proximal images of oilseed rape—a crop subjected to various HSI studies, with the exception of drought. The aim of the present study was to determine the spectral responses of two cultivars, `Cadeli` and `Viking’, representing distinctive water management strategies, to three types of watering regimes. Hyperspectral data cubes were acquired at the leaf level using a 2D frame camera. The influence of the experimental factors on the extent of leaf discolorations, vegetation index values, and principal component scores was investigated using Bayesian linear models. Clear treatment effects were obtained primarily for the vegetation indexes with respect to the watering regimes. The mean values of RGI, MTCI, RNDVI, and GI responded to the difference between the well-watered and water-deprived plants. The RGI index excelled among them in terms of effect strengths, which amounted to −0.96[−2.21,0.21] and −0.71[−1.97,0.49] units for each cultivar. A consistent increase in the multiple index standard deviations, especially RGI, PSRI, TCARI, and TCARI/OSAVI, was associated with worsening of the hydric regime. These increases were captured not only for the dry treatment but also for the plants subjected to regeneration after a drought episode, particularly by PSRI (a multiplicative effect of 0.33[0.16,0.68] for `Cadeli’). This result suggests a higher sensitivity of the vegetation index variability measures relative to the means in the context of the oilseed rape drought stress diagnosis and justifies the application of HSI to capture these effects. RGI is an index deserving additional scrutiny in future studies, as both its mean and standard deviation were affected by the watering regimes. Numéro de notice : A2020-656 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs12203462 Date de publication en ligne : 21/10/2020 En ligne : https://doi.org/10.3390/rs12203462 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96123
in Remote sensing > vol 12 n° 20 (October-2 2020) . - 27 p.[article]Object-based classification of mixed forest types in Mongolia / E. Nyamjargal in Geocarto international, vol 35 n° 14 ([15/10/2020])
[article]
Titre : Object-based classification of mixed forest types in Mongolia Type de document : Article/Communication Auteurs : E. Nyamjargal, Auteur ; D. Amarsaikhan, Auteur ; A. Munkh-Erdene, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1615 - 1626 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] approche hiérarchique
[Termes IGN] carte forestière
[Termes IGN] classification bayesienne
[Termes IGN] classification orientée objet
[Termes IGN] classification pixellaire
[Termes IGN] forêt
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] image Sentinel-MSI
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] Mongolie
[Termes IGN] peuplement mélangéRésumé : (auteur) The aim of this study is to produce updated forest map of the Bogdkhan Mountain, Mongolia using multitemporal Sentinel-2A images. The target area has highly mixed forest types and it is very difficult to differentiate the fuzzy boundaries among different forest types. To extract the forest class information, an object-based classification technique is applied and a rule-base to separate the mixed classes is developed. The rule-base uses a hierarchy of rules describing different conditions under which the actual classification has to be performed. To compare the result of the developed method with a result of a pixel-based approach, a Bayesian maximum likelihood classification is applied. The final result indicates overall accuracy of 90.87% for the object-based classification, while for the pixel-based approach it is 79.89%. Overall, the research indicates that the object-based method that uses a thoroughly defined segmentation and a well-constructed rule-base can significantly improve the classification of mixed forest types and produce of a reliable forest map. Numéro de notice : A2020-619 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1583775 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1583775 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95995
in Geocarto international > vol 35 n° 14 [15/10/2020] . - pp 1615 - 1626[article]Textural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])
[article]
Titre : Textural classification of remotely sensed images using multiresolution techniques Type de document : Article/Communication Auteurs : Rizwan Ahmed Ansari, Auteur ; Krishna Mohan Buddhiraju, Auteur ; Avik Bhattacharya, Auteur Année de publication : 2020 Article en page(s) : pp 1580 - 1602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse multirésolution
[Termes IGN] analyse texturale
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de contours
[Termes IGN] distance euclidienne
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image RVB
[Termes IGN] image satellite
[Termes IGN] texture d'image
[Termes IGN] transformation en ondelettesRésumé : (auteur) Multiresolution analysis (MRA) methods have been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a methodology to identify different classes in satellite images using texture features from newly developed multiresolution methods. The proposed method is tested on remotely sensed optical images and a Pauli RGB decomposed version of synthetic aperture radar image. The textural information is extracted at various scales and in different directions from curvelet and contourlet transforms. The results are compared with wavelet-based features. Accuracy assessment is performed and comparative analysis is carried out using minimum distance to mean, support vector machine and random forest classifiers. It is found that the proposed method shows better class discriminating power and classification capability as compared to existing wavelet-based method. Numéro de notice : A2020-618 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581263 Date de publication en ligne : 15/04/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581263 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95994
in Geocarto international > vol 35 n° 14 [15/10/2020] . - pp 1580 - 1602[article]Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands / Bappa Das in Geocarto international, vol 35 n° 13 ([01/10/2020])PermalinkMultiview automatic target recognition for infrared imagery using collaborative sparse priors / Xuelu Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)PermalinkA novel spectral–spatial based adaptive minimum spanning forest for hyperspectral image classification / Jing Lv in Geoinformatica, vol 24 n° 4 (October 2020)PermalinkUse of visible and near-infrared reflectance spectroscopy models to determine soil erodibility factor (K) in an ecologically restored watershed / Qinghu Jiang in Remote sensing, vol 12 n° 18 (September-2 2020)PermalinkHyperspectral unmixing using orthogonal sparse prior-based autoencoder with hyper-laplacian loss and data-driven outlier detection / Zeyang Dou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkPansharpening: context-based generalized Laplacian pyramids by robust regression / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkX-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkCan ensemble techniques improve coral reef habitat classification accuracy using multispectral data? / Mohammad Shawkat Hossain in Geocarto international, vol 35 n° 11 ([01/08/2020])PermalinkExtraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkClassification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)Permalink