Robust multi-view representation for spatial-spectral domain in application of hyperspectral image classification

Spatial-spectral representation plays an important role in hyperspectral images (HSIs) classification. However, many of the existing local feature algorithms for HSIs are based on the two-dimensional image and do not take full advantage of the information hidden in HSI, such as spatial-spectral locality correlation information, thereby reducing the robustness of these algorithms. In response to these problems, this study presents a robust multi-view spatial-spectral representation method with the characteristics of HSIs. There are two key techniques in this representation method, called spatial-spectral locality constrained linear coding (SSLLC) and spatial-spectral pyramid matching model (SSPM). Firstly, SSLLC applies the locality information of the feature points and visual words and uses the discriminant information provided by the nearest-neighbouring spatial-spectral feature points in HSIs. Secondly, SSPM works by partitioning the image into increasingly fine sub-cubes and uses the cubes to match the local features of the HSIs. The multi-view representation is tolerant to illumination change, image rotation, affine distortion etc. To assess the validity of authors' algorithm, the authors compared their results with several existing approaches, including a deep learning method. The experimental results show that this representation method can effectively improve the accuracy of HSIs classification.