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Morphologically decoupled structured sparsity for rotation-invariant hyperspectral image analysis / Saurabh Prasad in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4355 - 4366 Titre : Morphologically decoupled structured sparsity for rotation-invariant hyperspectral image analysis Type de document : Article/Communication Auteurs : Saurabh Prasad, Auteur ; Demetrio Labate, Auteur ; Mishan Cui, Auteur ; Yuhang Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 4355 - 4366 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classificateur paramétrique [Termes descripteurs IGN] classification spectrale [Termes descripteurs IGN] décomposition empirique d'image [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] morphologie mathématique [Termes descripteurs IGN] primitive géométrique [Termes descripteurs IGN] réflectance spectrale Résumé : (Auteur) Hyperspectral imagery has emerged as a popular sensing modality for a variety of applications, and sparsity-based methods were shown to be very effective to deal with challenges coming from high dimensionality in most hyperspectral classification problems. In this paper, we challenge the conventional approach to hyperspectral classification that typically builds sparsity-based classifiers directly on spectral reflectance features or features derived directly from the data. We assert that hyperspectral image (HSI) processing can benefit very significantly by decoupling data into geometrically distinct components since the resulting decoupled components are much more suitable for sparse representation-based classifiers. Specifically, we apply morphological separation to decouple data into texture and cartoon-like components, which are sparsely represented using local discrete cosine bases and multiscale shearlets, respectively. In addition to providing a structured sparse representation, this approach allows us to build classifiers with invariance properties specific to each geometrically distinct component of the data. The experimental results using real-world HSI data sets demonstrate the efficacy of the proposed framework for classifying multichannel imagery under a variety of adverse conditions - in particular, small training sample size, additive noise, and rotational variabilities between training and test samples. Numéro de notice : A2017-496 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2691607 En ligne : http://dx.doi.org./10.1109/TGRS.2017.2691607 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86437 [article]

Hyperspectral band selection from statistical wavelet models / Siwei Feng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2111 - 2123 Titre : Hyperspectral band selection from statistical wavelet models Type de document : Article/Communication Auteurs : Siwei Feng, Auteur ; Yuki Itoh, Auteur ; Mario Parente, Auteur ; Marco F. Duarte, Auteur Année de publication : 2017 Article en page(s) : pp 2111 - 2123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification spectrale [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] pouvoir de résolution spectrale [Termes descripteurs IGN] redondance de données [Termes descripteurs IGN] signature spectrale Résumé : (Auteur) High spectral resolution brings hyperspectral images with large amounts of information, which makes these images more useful in many applications than images obtained from traditional multispectral scanners with low spectral resolution. However, the high data dimensionality of hyperspectral images increases the burden on data computation, storage, and transmission; fortunately, the high redundancy in the spectral domain allows for significant dimensionality reduction. Band selection provides a simple dimensionality reduction scheme by discarding bands that are highly redundant, thereby preserving the structure of the data set. This paper proposes a new criterion for pointwise-ranking-based band selection that uses a nonhomogeneous hidden Markov chain (NHMC) model for redundant wavelet coefficients of each hyperspectral signature. The model provides a binary multiscale label that encodes semantic features that are useful to discriminate spectral types. A band ranking score considers the average correlation among the average NHMC labels for each band. We also test richer discrete-valued label vectors that provide a more finely grained quantization of spectral fluctuations. In addition, since band selection methods based on band ranking often ignore correlations in selected bands, we study the effect of redundancy elimination, applied on the selected features, on the performance of an example classification problem. Our experimental results also include an optional redundancy elimination step and test their effect on classification performance that is based on the selected bands. The experimental results also include a comparison with several relevant supervised band selection techniques. Numéro de notice : A2017-172 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2016.2636850 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84717 [article]

Extracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary / Yubin Niu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1604 - 1617 Titre : Extracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary Type de document : Article/Communication Auteurs : Yubin Niu, Auteur ; Bin Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1604 - 1617 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bibliothèque de signatures spectrales [Termes descripteurs IGN] classification spectrale [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] image hyperspectrale Résumé : (Auteur) The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. Numéro de notice : A2017-158 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/ 10.1109/TGRS.2016.2628085 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84695 [article]

Multiple spectral similarity metrics for surface materials identification using hyperspectral data / Rama Rao Nidamanuri in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)  

Public [article]  inGeocarto international > vol 31 n° 7 - 8 (July - August 2016) . - pp 845 - 859 Titre : Multiple spectral similarity metrics for surface materials identification using hyperspectral data Type de document : Article/Communication Auteurs : Rama Rao Nidamanuri, Auteur Année de publication : 2016 Article en page(s) : pp 845 - 859 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] classification spectrale [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] limite de résolution spectrale [Termes descripteurs IGN] pouvoir de résolution spectrale [Termes descripteurs IGN] similitude spectrale Résumé : (Auteur) Modern hyperspectral imaging and non-imaging spectroradiometer has the capability to acquire high-resolution spectral reflectance data required for surface materials identification and mapping. Spectral similarity metrics, due to their mathematical simplicity and insensitiveness to the number of reference labelled spectra, have been increasingly used for material mapping by labelling reflectance spectra in hyperspectral data labelling. For a particular hyperspectral data set, the accuracy of spectral labelling depends considerably upon the degree of unambiguous spectral matching achieved by the spectral similarity metric used. In this work, we propose a new methodology for quantifying spectral similarity for hyperspectral data labelling for surface materials identification. Developed adopting the multiple classifier system architecture, the proposed methodology unifies into a single framework the differential performances of eight different spectral similarity metrics for the quantification of spectral matching for surface materials. The proposed methodology has been implemented on two types of hyperspectral data viz. image (airborne hyperspectral images) and non-image (library spectra) for numerous surface materials identification. Further, the performance of the proposed methodology has been compared with the support vector machines (SVM) approach, and with all the base spectral similarity metrics. The results indicate that, for the hyperspectral images, the performance of the proposed methodology is comparable with that of the SVM. For the library spectra, the proposed methodology shows a consistently higher (increase of about 30% when compared to SVM) classification accuracy. The proposed methodology has the potential to serve as a general library search method for materials identification using hyperspectral data. Numéro de notice : A2016-457 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1086903 date de publication en ligne : 30/09/2015 En ligne : http://dx.doi.org/10.1080/10106049.2015.1086903 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81381 [article]

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Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 12 (December 2015) . - pp 6663 - 6674 Titre : Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels Type de document : Article/Communication Auteurs : Leyuan Fang, Auteur ; Shutao Li, Auteur ; Wuhui Duan, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 6663 - 6674 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] classification spectrale [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] pixel Résumé : (auteur) For the classification of hyperspectral images (HSIs), this paper presents a novel framework to effectively utilize the spectral-spatial information of superpixels via multiple kernels, which is termed as superpixel-based classification via multiple kernels (SC-MK). In the HSI, each superpixel can be regarded as a shape-adaptive region, which consists of a number of spatial neighboring pixels with very similar spectral characteristics. First, the proposed SC-MK method adopts an oversegmentation algorithm to cluster the HSI into many superpixels. Then, three kernels are separately employed for the utilization of the spectral information, as well as spatial information, within and among superpixels. Finally, the three kernels are combined together and incorporated into a support vector machine classifier. Experimental results on three widely used real HSIs indicate that the proposed SC-MK approach outperforms several well-known classification methods. Numéro de notice : A2015-847 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2445767 date de publication en ligne : 01/07/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2445767 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79197 [article]

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Classification of remotely sensed images using the geneSIS fuzzy segmentation algorithm / Stelios Mylonas in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

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On spectral unmixing resolution using extended support vector machines / Xiaofeng Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)  

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Spectral–spatial classification of hyperspectral images with a superpixel-based discriminative sparse model / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)

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Local binary patterns and extreme learning machine for hyperspectral imagery classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)

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Hyperspectral image classification based on three-dimensional scattering wavelet transform / Yuan Yan Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)

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Supervised spectral–spatial hyperspectral image classification with weighted markov random fields / Le Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)  

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Gabor feature-based collaborative representation for hyperspectral imagery classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)  

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Sparse unmixing of hyperspectral data using spectral a priori information / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)  

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Automatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning / Qian Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

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Extended random walker-based classification of hyperspectral images / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

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