A hyperspectral image classification method based on feature enhancement and a hybrid deformable convolution network

In recent years, some hyperspectral image (HSI) classification methods based on deep models have shown excellent performance. Most deep models receive three-dimensional (3D) block structures as input to extract spectral-spatial features from HSI data. However, the existence of background pixel information in 3D blocks affects the spectral-spatial feature representation ability. In addition, directly performing overall dimension reduction on high-dimensional spectral information lacks analysis of the original data, which weakens the constraint relationship between spectral data and increases the difficulty of feature extraction. Therefore, an HSI classification method based on feature enhancement and hybrid deformable convolution network is proposed. Firstly, a novel 3D block is constructed to enhance spectral-spatial feature representation ability by replacing background pixel information with non-background pixel information. Secondly, based on the analysis results of the original spectral data, this paper proposes a regional dimension reduction mechanism to enhance the constraint relationship between HSI data. Finally, the proposed method is applied to hybrid deformable convolution network for experimental verification. The experimental results of three standard datasets show that the classification effect of the proposed method is better than that of several classification methods, which proves the effectiveness of the proposed method.