Nonlinear spatial decoupling strategy and deformable convnets v2 for hyperspectral image classification

Abstract

Classification of hyperspectral image (HSI) based on deep learning has attracted extensive attention. However, there is a strong spatial nonlinear coupling phenomenon among data in HSI, which leads to low spatial separability of different types of data and affects classification accuracy. In order to solve this problem, this paper proposes a HSI classification method based on nonlinear spatial decoupling strategy and deformable convnets v2 (DCNv2). Specifically, this paper introduces the classic color names (CN) algorithm with strong nonlinear projection ability in the field of target tracking. Through the projection matrix of CN algorithm, the spatial constraint features of the original spectral data extracted by bilinear local binary pattern and the original spectral data are projected to realize the spatial decoupling of features and improve the spatial separability of data. Then, the DCNv2, which has strong adaptability of geometric transformation, is used to extract the deep features of the decoupled data, so as to achieve effective classification. The experiment was carried out on four standard hyperspectral data sets and the overall accuracy of the proposed method on these data sets reached 99.73%, 99.93%, 99.98% and 99.99%, respectively. Compared with several classical classification methods, it has better classification effect.