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Multimorphological superpixel model for hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 6950 - 6963 Titre : Multimorphological superpixel model for hyperspectral image classification Type de document : Article/Communication Auteurs : Tianzhu Liu, Auteur ; Yanfeng Gu, Auteur ; Jocelyn Chanussot, Auteur ; Mauro Dalla Mura, Auteur Année de publication : 2017 Article en page(s) : pp 6950 - 6963 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] méthode fondée sur le noyau Résumé : (Auteur) With the development of hyperspectral sensors, nowadays, we can easily acquire large amount of hyperspectral images (HSIs) with very high spatial resolution, which has led to a better identification of relatively small structures. Owing to the high spatial resolution, there are much less mixed pixels in the HSIs, and the boundaries between these categories are much clearer. However, the high spatial resolution also leads to complex and fine geometrical structures and high inner-class variability, which make the classification results very “noisy.” In this paper, we propose a multimorphological superpixel (MMSP) method to extract the spectral and spatial features and address the aforementioned problems. To reduce the difference within the same class and obtain multilevel spatial information, morphological features (multistructuring element extended morphological profile or multiattribute filter extended multi-attribute profiles) are first obtained from the original HSI. After that, simple linear iterative clustering segmentation method is performed on each morphological feature to acquire the MMSPs. Then, uniformity constraint is used to merge the MMSPs belonging to the same class which can avoid introducing the information from different classes and acquire spatial structures at object level. Subsequently, mean filtering is utilized to extract the spatial features within and among MMSPs. At last, base kernels are obtained from the spatial features and original HSI, and several multiple kernel learning methods are used to obtain the optimal kernel to incorporate into the support vector machine. Experiments conducted on three widely used real HSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model. Numéro de notice : A2017-767 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2737037 En ligne : https://doi.org/10.1109/TGRS.2017.2737037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88806 [article]

Class-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7351 - 7365 Titre : Class-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification Type de document : Article/Communication Auteurs : Tianzhu Liu, Auteur ; Yanfeng Gu, Auteur ; Xiuping Jia, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7351 - 7365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] exploration de données [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] méthode fondée sur le noyau Résumé : (Auteur) In recent years, many studies on hyperspectral image classification have shown that using multiple features can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. This paper proposes a class-specific sparse MKL (CS-SMKL) framework to improve the capability of hyperspectral image classification. In terms of the features, extended multiattribute profiles are adopted because it can effectively represent the spatial and spectral information of hyperspectral images. CS-SMKL classifies the hyperspectral images, simultaneously learns class-specific significant features, and selects class-specific weights. Using an L1-norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at the group/feature level and automatically learn a compact feature set for the classification of any two classes. More precisely, our CS-SMKL determines the associated weights of optimal base kernels for any two classes and results in improved classification performances. The advantage of the proposed method is that only the features useful for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on three hyperspectral data sets. The experimental results show that the proposed method achieves better performances for hyperspectral image classification compared with several state-of-the-art algorithms, and the results confirm the capability of the method in selecting the useful features. Numéro de notice : A2016-932 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2600522 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2600522 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83346 [article]