Hyperspectral Image Classification by Nonlocal Joint Collaborative Representation With a Locally Adaptive Dictionary

Li, Jiayi; Zhang, Hongyan; Huang, Yuancheng; Zhang, Liangpei

doi:10.1109/tgrs.2013.2274875

Cited by 180 publications

(67 citation statements)

References 33 publications

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“…Moreover, if the dimensionality and the discrimination capacity of a sample is high, other regularization terms such as 2 -norm can play the same role as the sparsity 1 -norm. Several approaches have demonstrated the effectiveness of the classification method using 2 -norm in many applications [14,16], including hyperspectral image classification [17,18]. For the sake of (1) The first category can be treated as pre-processing methods.…”

Section: Introductionmentioning

confidence: 99%

Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares

Liu

Xiao

et al. 2017

IJGI

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Abstract:As a widely used classifier, sparse representation classification (SRC) has shown its good performance for hyperspectral image classification. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classification purposes. If the dimensionality and the discrimination capacity of a test pixel is high, other norms (e.g., 2 -norm) can be used to regularize the coding coefficients, except for the sparsity 1 -norm. In this paper, we show that in the kernel space the nonnegative constraint can also play the same role, and thus suggest the investigation of kernel fully constrained least squares (KFCLS) for hyperspectral image classification. Furthermore, in order to improve the classification performance of KFCLS by incorporating spatial-spectral information, we investigate two kinds of spatial-spectral methods using two regularization strategies: (1) the coefficient-level regularization strategy, and (2) the class-level regularization strategy. Experimental results conducted on four real hyperspectral images demonstrate the effectiveness of the proposed KFCLS, and show which way to incorporate spatial-spectral information efficiently in the regularization framework.

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Section: Introductionmentioning

confidence: 99%

Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares

Liu

Xiao

et al. 2017

IJGI

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“…Experimental results have demonstrated the effectiveness of such a framework [33,34]. The MTJSRC framework was motivated by the success of multi-task joint sparse linear regression and the Sparse Representation Classification (SRC) [35] approaches, that have been applied in HRS satellite image classification and achieve excellent performances [36,37]. Based on the knowledge transferring mechanism in MTL [38] and the collaborative representation mechanism in SRC [39], MTJSRC can deal with the "lack of samples" problem for high-dimensional signal recognition [36].…”

Section: Introductionmentioning

confidence: 99%

“…The MTJSRC framework was motivated by the success of multi-task joint sparse linear regression and the Sparse Representation Classification (SRC) [35] approaches, that have been applied in HRS satellite image classification and achieve excellent performances [36,37]. Based on the knowledge transferring mechanism in MTL [38] and the collaborative representation mechanism in SRC [39], MTJSRC can deal with the "lack of samples" problem for high-dimensional signal recognition [36]. The MTJSRC method can learn a common subset of features for all tasks through joint sparsity regularization [40] by penalizing the sum of l 2 norms of the blocks of coefficients associated with each covariate group across different classification problems.…”

Section: Introductionmentioning

confidence: 99%

“…From the perspective of linear regression, MTJSRC was inspired by Multi-Task Joint Covariate Selection (MTJCS) which can be regarded as a combination model of the group Least Absolute Shrinkage and Selection Operator (LASSO) [41] and multi-task LASSO [42]. Li et al [36] introduced the MTJSRC paradigm for hyperspectral image classification and achieved competitive performance. However, the multiple learning tasks in MTJSRC can be coupled using a set of shared factors possessing low-rank structure [43].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Task Joint Sparse and Low-Rank Representation for the Scene Classification of High-Resolution Remote Sensing Image

Liu

Yang

et al. 2016

Remote Sensing

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Scene classification plays an important role in the intelligent processing of High-Resolution Satellite (HRS) remotely sensed images. In HRS image classification, multiple features, e.g., shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS images. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structures across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

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“…Furthermore, K-NN Euclidean distance and the spatial neighboring information of test pixels are introduced into the CR classifiers. In [28], a nonlocal joint CR with a locally-adaptive dictionary is developed. In [29], spatially multiscale adaptive sparse representation in a pixel-wise manner is utilized to construct a structural dictionary and outperforms its counterparts.…”

Section: Introductionmentioning

confidence: 99%

Robust Hyperspectral Image Classification by Multi-Layer Spatial-Spectral Sparse Representations

Bian

Chen

et al. 2016

Remote Sensing

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Sparse representation (SR)-driven classifiers have been widely adopted for hyperspectral image (HSI) classification, and many algorithms have been presented recently. However, most of the existing methods exploit the single layer hard assignment based on class-wise reconstruction errors on the subspace assumption; moreover, the single-layer SR is biased and less stable due to the high coherence of the training samples. In this paper, motivated by category sparsity, a novel multi-layer spatial-spectral sparse representation (mlSR) framework for HSI classification is proposed. The mlSR assignment framework effectively classifies the test samples based on the adaptive dictionary assembling in a multi-layer manner and intrinsic class-dependent distribution. In the proposed framework, three algorithms, multi-layer SR classification (mlSRC), multi-layer collaborative representation classification (mlCRC) and multi-layer elastic net representation-based classification (mlENRC) for HSI, are developed. All three algorithms can achieve a better SR for the test samples, which benefits HSI classification. Experiments are conducted on three real HSI image datasets. Compared with several state-of-the-art approaches, the increases of overall accuracy (OA), kappa and average accuracy (AA) on the Indian Pines image range from 3.02% to 17.13%, 0.034 to 0.178 and 1.51% to 11.56%, respectively. The improvements in OA, kappa and AA for the University of Pavia are from 1.4% to 21.93%, 0.016 to 0.251 and 0.12% to 22.49%, respectively. Furthermore, the OA, kappa and AA for the Salinas image can be improved from 2.35% to 6.91%, 0.026 to 0.074 and 0.88% to 5.19%, respectively. This demonstrates that the proposed mlSR framework can achieve comparable or better performance than the state-of-the-art classification methods.

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Hyperspectral Image Classification by Nonlocal Joint Collaborative Representation With a Locally Adaptive Dictionary

Cited by 180 publications

References 33 publications

Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares

Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares

Multi-Task Joint Sparse and Low-Rank Representation for the Scene Classification of High-Resolution Remote Sensing Image

Robust Hyperspectral Image Classification by Multi-Layer Spatial-Spectral Sparse Representations

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