Dictionary-Based, Clustered Sparse Representation for Hyperspectral Image Classification

Qin, Zhentao; Yang, Wunian; Yang, Ruigang; Zhao, Xiangyu; Yang, Teng-jiao

doi:10.1155/2015/678765

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Based on the similar idea, Kang et al [32] used stochastic rovers for probability majorization, which is demonstrated to be very useful when training samples are relatively restricted. In recent years, sparse representation also becomes a hot research topic in HIC field as a novel signal expression algorithm [33]- [37].…”

Section: Introductionmentioning

confidence: 99%

Ensemble EMD-Based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification

Zheng

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral images (HSIs) have fine spectral information and rich spatial information, of which the feature quality is one of the key factors that affect the classification performance. Therefore, how to extract essential features and eliminate redundant features from hyperspectral data is the main research focus of this paper. Here, we propose a spectral-spatial feature extraction method based on ensemble empirical mode decomposition (SFEEMD) for HSI classification, which contains several steps as follows: Firstly, the dimension reduction for HSI is performed by using principal component analysis method. Secondly, in order to decrease the sensitivity to noise and extract rough outline features, the adaptive total variation filtering (ATVF) is conducted on the selected principal components. Furthermore, by using the ensemble empirical mode decomposition (EEMD) to resolve each spectral band into sequence components, the features of HSIs can be better coalesced into the transform domain. Finally, the first K principal components of the input image and the outputs of the ATVF and EEMD are integrated into a stacking system to obtain the final feature image, which is then classified by a pixel-wise classifier. The experimental results of three authentic hyperspectral data sets show that the proposed algorithm obtains superior classification performance compared with other methods.

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Section: Introductionmentioning

confidence: 99%

Ensemble EMD-Based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification

Zheng

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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ADHD classification by feature space separation with sparse representation

Zhang

Tang

Chen

et al. 2018

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)

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Exploring Structural Consistency in Graph Regularized Joint Spectral-Spatial Sparse Coding for Hyperspectral Image Classification

Liu

Zhou

Liang

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In hyperspectral image classification, both spectral and spatial data distributions are important in describing and identifying different materials and objects in the image. Furthermore, consistent spatial structures across bands can be useful in capturing inherent structural information of objects. These imply that three properties should be considered when reconstructing an image using sparse coding methods. Firstly, the distribution of different ground objects leads to different coding coefficients across the spatial locations. Secondly, local spatial structures change slightly across bands due to different reflectance properties of various object materials. Lastly and more importantly, some sort of structural consistency shall be enforced across bands to reflect the fact that the same object appears at the same spatial location in all bands of an image. Based on these considerations, we propose a novel joint spectralspatial sparse coding model that explores structural consistency for hyperspectral image classification. For each band image, we adopt a sparse coding step to reconstruct the structures in the band image. This allows different dictionaries be generated to characterize the band-wise image variation. At the same time, we enforce the same coding coefficients at the same spatial location in different bands so as to maintain consistent structures across bands. To further promote the discriminating power of the model, we incorporate a graph Laplacian sparsity constraint into the model to ensure spectral consistency in the dictionary generation step. Experimental results show that the proposed method outperforms some state-of-the-art spectral-spatial sparse coding methods.

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Dictionary-Based, Clustered Sparse Representation for Hyperspectral Image Classification

Cited by 3 publications

References 15 publications

Ensemble EMD-Based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification

Ensemble EMD-Based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification

ADHD classification by feature space separation with sparse representation

Exploring Structural Consistency in Graph Regularized Joint Spectral-Spatial Sparse Coding for Hyperspectral Image Classification

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