Spectral–Spatial Classification of Hyperspectral Images With a Superpixel-Based Discriminative Sparse Model

Fang, Leyuan; Li, Shutao; Kang, Xudong; Benediktsson, Jón Atli

doi:10.1109/tgrs.2015.2392755

Cited by 242 publications

(104 citation statements)

References 66 publications

(100 reference statements)

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“…The performance of the proposed MSSR algorithm is compared with those of seven competing classification algorithms, i.e., SVM [8], EMP [16], SRC [36], JSRC [36], multiscale adaptive sparse representation (MASR) [63], superpixel-based classification via multiple kernels (SCMK) [46] and the superpixel-based discriminative sparse model (SBDSM) [64]. The EMP, JSRC, MASR, SCMK, SBDSM and MSSR algorithms take advantage of the spectral-spatial information for HSI classification, while the SVM and SRC algorithms only exploit the spectral information.…”

Section: Resultsmentioning

confidence: 99%

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Zhang

et al. 2017

Remote Sensing

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Abstract:Recently, superpixel segmentation has been proven to be a powerful tool for hyperspectral image (HSI) classification. Nonetheless, the selection of the optimal superpixel size is a nontrivial task. In addition, compared with single-scale superpixel segmentation, the same image segmented on a different scale can obtain different structure information. To overcome such a drawback also utilizing the structural information, a multiscale superpixel-based sparse representation (MSSR) algorithm for the HSI classification is proposed. Specifically, a modified segmentation strategy of multiscale superpixels is firstly applied on the HSI. Once the superpixels on different scales are obtained, the joint sparse representation classification is used to classify the multiscale superpixels. Furthermore, majority voting is utilized to fuse the labels of different scale superpixels and to obtain the final classification result. Two merits are realized by the MSSR. First, multiscale information fusion can more effectively explore the spatial information of HSI. Second, in the multiscale superpixel segmentation, except for the first scale, the superpixel number on a different scale for different HSI datasets can be adaptively changed based on the spatial complexity of the corresponding HSI. Experiments on four real HSI datasets demonstrate the qualitative and quantitative superiority of the proposed MSSR algorithm over several well-known classifiers.

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Section: Resultsmentioning

confidence: 99%

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Zhang

et al. 2017

Remote Sensing

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“…In [8], [9], the ERS was proven to improve the accuracy of HSI classification effectively. ERS is extended to multiple dimensions, which can be directly applied on the entire hyperspectral data, to divided an HSI into multiple non-overlapping homogeneous regions.…”

Section: The Proposed Methods Hyperspectral Image Segmentationmentioning

confidence: 99%

Super pixel-level dictionary learning for hyperspectral image classification

Zhao¹,

Zhu²,

Liao³

et al. 2017

AIP Conference Proceedings

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Abstract. This paper presents a superpixel-level dictionary learning model for hyperspectral data. The idea is to divide the hyperspectral image into a number of super-pixels by means of the super-pixel segmentation method. Each super-pixel is a spatial neighborhood called contextual group. That is, each pixel is represented using a linear combination of a few dictionary items learned from the train data, but since pixels inside a super-pixel are often consisting of the same materials, their linear combinations are constrained to use common items from the dictionary. To this end, the sparse coefficients of the context group have a common sparse pattern by using the joint sparse regularizer for dictionary learning. The sparse coefficients are then used for classification using linear support vector machines. The validity of the proposed method is experimentally verified on a real hyperspectral images.

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“…Better than patch region, shape-adaptive superpixel can provide more accurate spatial information. In [29], the superpixel was introduced to replace the patch region. Then a shape-adaptive local smooth region was generated for each test pixel by a shape-adaptive algorithm in [30].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification

Tong

Jiang

et al. 2017

Remote Sensing

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Sparse Representation has been widely applied to classification of hyperspectral images (HSIs). Besides spectral information, the spatial context in HSIs also plays an important role in the classification. The recently published Multiscale Adaptive Sparse Representation (MASR) classifier has shown good performance in exploiting spatial information for HSI classification. But the spatial information is exploited by multiscale patches with fixed sizes of square windows. The patch can include all nearest neighbor pixels but these neighbor pixels may contain some noise pixels. Then another research proposed a Multiscale Superpixel-Based Sparse Representation (MSSR) classifier. Shape-adaptive superpixels can provide more accurate representation than patches. But it is difficult to select scales for superpixels. Therefore, inspired by the merits and demerits of multiscale patches and superpixels, we propose a novel algorithm called Multiscale Union Regions Adaptive Sparse Representation (MURASR). The union region, which is the overlap of patch and superpixel, can make full use of the advantages of both and overcome the weaknesses of each one. Experiments on several HSI datasets demonstrate that the proposed MURASR is superior to MASR and union region is better than the patch in the sparse representation.

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Spectral–Spatial Classification of Hyperspectral Images With a Superpixel-Based Discriminative Sparse Model

Cited by 242 publications

References 66 publications

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Super pixel-level dictionary learning for hyperspectral image classification

Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification

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