Deep neural networks have achieved great success in the field of image processing. The feature representation of RGB image can be easily obtained in spatial domain. Different from this, hyperspectral image (HSI) is a kind of high-dimensional data that contains rich spectral information. To explore the manifold structure in HSI, a new deep learning model termed deep manifold learning network (DMLN) was proposed in this paper. In DMLN, a graph based loss function is designed to combine the exploration of manifold structure and the extraction of deep abstract information, which can obtain the discriminant features by iteratively enhancing the compactness of intraclass samples and the separation of interclass samples. Experimental results on two real-world HSI data sets demonstrate the proposed DMLN outperformed some the state-of-the-art methods.
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Deep Manifold Learning Network for Hyperspectral Image Classification
Semantic Scholar · Environmental Science · 2020
Abstract
Deep neural networks have achieved great success in the field of image processing. The feature representation of RGB image can be easily obtained in spatial domain. Different from this, hyperspectral image (HSI) is a kind of high-dimensional data that contains rich spectral information. To explore the manifold structure in HSI, a new deep learning model termed deep manifold learning network (DMLN) was proposed in this paper. In DMLN, a graph based loss function is designed to combine the exploration of manifold structure and the extraction of deep abstract information, which can obtain the discriminant features by iteratively enhancing the compactness of intraclass samples and the separation of interclass samples. Experimental results on two real-world HSI data sets demonstrate the proposed DMLN outperformed some the state-of-the-art methods.