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A novel sparse tensor dictionary learning algorithm and a convolutional neural network (CNN) classification method based on this algorithm are proposed for hyperspectral image (HSI) in this paper. The HSI is intuitively represented as a three-dimensional (3-D) cube, and utilizing the joint spatial-spectral information can significantly improve the accuracy of HSI classification. Therefore, the proposed atom-substituted tensor dictionary learning (ASTDL) algorithm utilizes tensor techniques to extract 3-D joint spatial-spectral features from HSI cubes directly. Sparsity constraint is enforced on the coefficient tensors, which obeys the sparsity attribute of HSI. The proposed ASTDL enhanced CNN (ASTDL-CNN) classification method utilizes a two-dimensional (2-D) CNN to extract deep features from the feature tensors obtained by the ASTDL, and to perform the pixel-wise classification. The use of ASTDL, which extracts intrinsic tensor features before CNN, alleviates within-class spatial-spectral variation and reduces the requirement of CNN for the labeled data. Whereas, the CNN works as a 3-D classifier, and provides the final classification results. Besides, we perform the majority vote on the classification map obtained by CNN to refine the classification. The performance of the proposed method is evaluated on three real HSI data sets. The competitive results to the compared state-of-the-art methods demonstrate that the ASTDL-CNN can provide accurate and robust classification results. [ABSTRACT FROM AUTHOR]

Sparse representation and dictionary learning have been successfully applied in hyperspectral image classification. Generally, it is more effective to learn the sub-dictionary for each class and utilize multiple scale strategy. However, the sub-dictionary may only consider the within-class information and ignore the discriminative information. For multiscale sparse representation, the shape of the regions would not adaptively change according to context structure. This paper proposes a discriminative sub-dictionary learning algorithm and an adaptive multiscale superpixel classification strategy under sparse representation framework for hyperspectral image classification. In dictionary learning stage, the global constraint term can ensure the learnt sub-dictionaries contain more discriminative information. In sparse representation stage, the adaptive multiscale superpixel strategy can exploit the spatial context according to the structural information. The combination of the two strategies can further explore the spatial and spectral information in hyperspectral image. Experiments on four hyperspectral image datasets prove that the proposed method has a significant improvement in both quantitative and qualitative analysis comparing with state-of-the-art algorithms. Moreover, the proposed algorithm can produce satisfactory results for the imbalanced data problem involved in hyperspectral image. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel supervised feature extraction and classification fusion algorithm based on neighborhood preserving embedding (NPE) and sparse representation is proposed. Specifically, an optimal dictionary is adaptively learned to bate the trivial information of the original training data; then, in order to obtain the sparse representation coefficients, a sparse preserving embedding map is sought to reduce the dimensionality of high-dimensional data, and the test data is classified by the corresponding sparse representation coefficients. Finally, the novel supervised fusion algorithm is applied to the land cover recognition of the off-land scenario. Experimental results show that the proposed method leads to promising results in fusing feature extraction and classification. [Copyright &y& Elsevier]