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Extraction of spectral channels from hyperspectral images for classification purposes / S.B. Serpico in IEEE Transactions on geoscience and remote sensing, vol 45 n° 2 (February 2007)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 2 (February 2007) . - pp 484 - 495 Titre : Extraction of spectral channels from hyperspectral images for classification purposes Type de document : Article/Communication Auteurs : S.B. Serpico, Auteur ; G. Moser, Auteur Année de publication : 2007 Article en page(s) : pp 484 - 495 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] classification dirigée [Termes IGN] extraction [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) This paper proposes a procedure to extract spectral channels of variable bandwidths and spectral positions from the hyperspectral image in such a way as to optimize the accuracy for a specific classification problem. In particular, each spectral channel ("s-band") is obtained by averaging a group of contiguous channels of the hyperspectral image ("h-bands"). Therefore, if one wants to define m s-bands, the problem can be formulated as the optimization of the related m starting and m ending h-bands. Toward this end, we propose to adopt, as an optimization criterion, an interclass distance computed on a training set and to generate a sequence of possible solutions by one of three possible search strategies. As the proposed formalization of the problem makes it analogous to a feature-selection problem, the proposed three strategies have been derived by modifying three feature-selection strategies, namely: 1) the "sequential forward selection", 2) the "steepest ascent," and 3) the "fast constrained search". Experimental results on a well-known hyperspectral data set confirm the effectiveness of the approach, which yields better results than other widely used methods. The importance of this kind of procedure lies in feature reduction for hyperspectral image classification or in the case-based design of the spectral bands of a programmable sensor. It represents a special case of feature extraction that is expected to be more powerful than feature selection. The kind of transformation used allows the interpretability of the new features (i.e., the spectral bands) to be saved. Copyright IEEE Numéro de notice : A2007-081 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.886177 En ligne : https://doi.org/10.1109/TGRS.2006.886177 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28446 [article]

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Partially supervised classification of remote sensing images through SVM-based probability density estimation / P. Mantero in IEEE Transactions on geoscience and remote sensing, vol 43 n° 3 (March 2005)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 3 (March 2005) . - pp 559 - 570 Titre : Partially supervised classification of remote sensing images through SVM-based probability density estimation Type de document : Article/Communication Auteurs : P. Mantero, Auteur ; G. Moser, Auteur ; S.B. Serpico, Auteur Année de publication : 2005 Article en page(s) : pp 559 - 570 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification semi-dirigée [Termes IGN] estimation statistique [Termes IGN] probabilités [Termes IGN] réalité de terrain Résumé : (Auteur) A general problem of supervised remotely sensed image classification assumes prior knowledge to be available for all the thematic classes that are present in the considered dataset. However, the ground-truth map representing that prior knowledge usually does not really describe all the land-cover typologies in the image, and the generation of a complete training set often represents a time-consuming, difficult and expensive task. This problem affects the performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is described that allows the identification of samples drawn from unknown classes through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density functions and on a recursive procedure to generate prior probability estimates for known and unknown classes. In the experiments, both a synthetic dataset and two real datasets were used. Numéro de notice : A2005-168 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.842022 En ligne : https://doi.org/10.1109/TGRS.2004.842022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27306 [article]

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A Markov random field approach to spatio-temporal contextual image classification / F. Melgani in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 11 (November 2003) . - pp 2478 - 2487 Titre : A Markov random field approach to spatio-temporal contextual image classification Type de document : Article/Communication Auteurs : F. Melgani, Auteur ; S.B. Serpico, Auteur Année de publication : 2003 Article en page(s) : pp 2478 - 2487 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] champ aléatoire de Markov [Termes IGN] classificateur paramétrique [Termes IGN] fusion d'images [Termes IGN] image ERS-SAR [Termes IGN] image Landsat-TM [Termes IGN] jeu de données localisées [Termes IGN] méthode robuste [Termes IGN] précision de la classification Résumé : (Auteur) Markov random fields (MRFs) provide a useful and theoretically well-established tool for integrating temporal contextual information into the classification process. In particular, when dealing with a sequence of temporal images, the usual MRF-based approach consists in adopting a "cascade" scheme, i.e., in propagating the temporal information from the current image to the next one of the sequence. The simplicity of the cascade scheme makes it attractive ; on the other hand, it does not fully exploit the temporal information available in a sequence of temporal images. In this paper, a "mutual" MRF approach is proposed that aims at improving both the accuracy and the reliability of the classification process by means of a better exploitation of the temporal information. It involves carrying out a bidirectional exchange of the temporal information between the defined single-time MRF models of consecutive images. A difficult issue related to MRFs is the determination of the MRF model parameters that weight the energy terms related to the available information sources. To solve this problem, we propose a simple and fast method based on the concept of "minimum perturbation" and implemented with the pseudo-inverse technique for the minimization of the sum of squared errors. Experimental results on a multitemporal dataset made up of two multisensor (Landsat Thematic Mapper and European Remote Sensing 1 synthetic aperture radar) images are reported. The results obtained by the proposed "mutual" approach show a clear improvement in terms of classification accuracy over those yielded by a reference MRF-based classifier. The presented method to automatically estimate the MRF parameters yielded significant results that make it an attractive alternative to the usual trial-and-error search procedure. Numéro de notice : A2003-317 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817269 En ligne : https://doi.org/10.1109/TGRS.2003.817269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22613 [article]

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A new search algorithm for feature selection in hyperspectral remote sensing images / S.B. Serpico in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 39 n° 7 (July 2001) . - pp 1360 - 1367 Titre : A new search algorithm for feature selection in hyperspectral remote sensing images Type de document : Article/Communication Auteurs : S.B. Serpico, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2001 Article en page(s) : pp 1360 - 1367 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale Résumé : (auteur) A new suboptimal search strategy suitable for feature selection in very high-dimensional remote sensing images (e.g., those acquired by hyperspectral sensors) is proposed. Each solution of the feature selection problem is represented as a binary string that indicates which features are selected and which are disregarded. In turn, each binary string corresponds to a point of a multidimensional binary space. Given a criterion function to evaluate the effectiveness of a selected solution, the proposed strategy is based on the search for constrained local extremes of such a function in the above-defined binary space. In particular, two different algorithms are presented that explore the space of solutions in different ways. These algorithms are compared with the classical sequential forward selection and sequential forward floating selection suboptimal techniques, using hyperspectral remote sensing images (acquired by the airborne visible/infrared imaging spectrometer [AVIRIS] sensor) as a data set. Experimental results point out the effectiveness of both algorithms, which can be regarded as valid alternatives to classical methods, as they allow interesting tradeoffs between the qualities of selected feature subsets and computational cost. Numéro de notice : A2001-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.934069 En ligne : https://doi.org/10.1109/36.934069 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21890 [article]

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