Multiple Spectral Resolution 3D Convolutional Neural Network for Hyperspectral Image Classification

Remote Sensing

Hao Xu¹  Li Cheng¹  Bo Li¹  Wei Yao¹ 

[1] College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China;

关键词: hyperspectral image classification;    convolutional neural network;    dilated convolution;    3D convolution;    residual connection;   

DOI  :  10.3390/rs13071248

来源: DOAJ

【 摘 要 】

In recent years, benefiting from the rapid development of deep learning technology in the field of computer vision, the study of hyperspectral image (HSI) classification has also made great progress. However, compared with ordinary RGB images, HSIs are more like 3D cubes; therefore, it is necessary and beneficial to explore classification methods suitable for the very special data structure of HSIs. In this paper, we propose Multiple Spectral Resolution 3D Convolutional Neural Network (MSR-3DCNN) for HSI classification tasks. In MSR-3DCNN, we expand the idea of multi-scale feature fusion and dilated convolution from the spatial dimension to the spectral dimension, and combine 3D convolution and residual connection; therefore, it can better adapt to the 3D cubic form of hyperspectral data and make efficient use of spectral information in different bands. Experimental results on four benchmark datasets show the effectiveness of the proposed approach and its superiority as compared with some state-of-the-art (SOTA) HSI classification methods.

【 授权许可】

Unknown