Cascade Residual Capsule Network for Hyperspectral Image Classification

Abstract

Theconvolution neural network (CNN) has recently shown the good performance in hyperspectral image (HSI) classification tasks. Many CNN-based methods crop image patches from original HSI as inputs. However, the input HSI cubes usually contain background and many hyperspectral pixels with different land-cover labels. Therefore, the spatial context information on objects of the same category is diverse in HSI cubes, which will weaken the discrimination of spectral–spatial features. In addition, CNN-based methods still face challenges in dealing with the spectral similarity between HSI cubes of spatially adjacent categories, which will limit the classification accuracy. To address the aforementioned issues, we propose a cascade residual capsule network (CRCN) for HSI classification. First, a residual module is designed to learn high-level spectral features of input HSI cubes in the spectral dimension. The residual module is employed to solve the problem of the spectral similarity between HSI cubes of spatially adjacent categories. And then two 3-D convolution layers are exploited to extract high-level spatial–spectral features. Finally, a capsule structure is developed to characterize spatial context orientation representations of objects, which can effectively deal with the diverse spatial context information on objects of the same category in HSI cubes. The capsule module is composed of two 3-D convolution layers and the capsule structure, which is connected to the residual module in series to construct the proposed CRCN. Experimental results on four public HSI datasets demonstrate the superiority of the proposed CRCN over six state-of-the-art models.

Keywords

• Cascade residual capsule network (CRCN)
• convolution neural network (CNN)
• high-level spatial–spectral feature extraction
• hyperspectral image (HSI) classification
• spatial context orientation representations