Abstract
Recently, sparse representation has yielded successful results in hyperspectral image (HSI) classification. In the sparse representation-based classifiers (SRCs), a more discriminative representation that preserves the spectral-spatial information can be exploited by treating the HSI as a whole entity. Based on this observation, a tensor block-sparsity based representation method is proposed for spectral-spatial classification of HSI in this paper. Unlike traditional vector/matrix-based SRCs, the proposed method consists of tensor block-sparsity based dictionary learning and class-dependent block sparse representation. By naturally regarding the HSI cube as a third-order tensor, small local patches centered at the training samples are extracted from the HSI to maintain the structural information. All the patches are then partitioned into a number of groups, on which a dictionary learning model is constructed with a tensor block-sparsity constraint. A test sample is also expressed as a small local patch and the block sparse representation is then performed in a class-wise manner to take advantage of the class label information. Finally, the category of the test sample is determined by using the minimal residual. Experimental results of two real-world HSIs show that our proposed method greatly improves the classification performance of SRC.
Highlights
Hyperspectral image (HSI) recorded by sensors simultaneously contains hundreds of continuous narrow spectral bands from the visible to infrared electromagnetic spectrum, providing detailed spectral information about the physical nature of distinct materials
support vector machine (SVM): the classical SVM [10] with a single radial basis function (RBF) kernel; SVM with composite kernel (SVMCK): the SVM [19] with a composite of spectral kernel and spatial kernel, and the spatial feature is extracted by extended morphological profile (EMP) [56]; collaborative representation based classification (CRC): the test sample is approximated by the linear combination of the training samples in a least squares sense [33]
90% with 5% of labeled samples as training samples; tbSRC is demonstrated to be superior to other methods with a small number of labeled samples, while spatial-aware dictionary learning (SADL), 3-D discrete wavelet transform (3D-DWT), local tensor discriminative analysis technique (LTDA), class-dependent SRC (cdSRC) and patch-based learning SRC with spatial smooth (pLSRC-S) trail marginally behind tbSRC
Summary
Hyperspectral image (HSI) recorded by sensors simultaneously contains hundreds of continuous narrow spectral bands from the visible to infrared electromagnetic spectrum, providing detailed spectral information about the physical nature of distinct materials. In [32], an online dictionary is designed for patch-based SRC by learning vector quantization (LVQ), while spatial-aware dictionary learning (SADL) [29] is proposed by partitioning the pixels of HSI into a number of square patches called contextual groups Another limitation of SRC is that the class labels of training samples are only utilized to calculate the residuals for each class but are ignored in the process of determining the sparse codes. We propose a tensor block-sparsity based representation method [43,47,48] for spectral-spatial classification of HSI. This method consists of two important steps, tensor block-sparsity based dictionary learning and class-dependent block sparse representation.
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