Work

Hyperspectral Image Classification Network Based on 3D Octave Convolution and Multiscale Depthwise Separable Convolution

Year: 2023

Type: article

Abstract: Hyperspectral images (HSIs) are pivotal in various fields due to their rich spectral–spatial information. While convolutional neural networks (CNNs) have notably enhanced HSI classification, they often generate redundant spatial features. To address this, we introduce a novel HSI classification method, OMDSC, employing 3D Octave convolution combined with multiscale depthwise separable convolutional networks. This method initially utilizes 3D Octave convolution for efficient spectral–spatial feature extraction from HSIs, thereby reducing spatial redundancy. Subsequently, multiscale depthwise separable convolution is used to further improve the extraction of spatial features. Finally, the HSI classification results are output by softmax classifier. This work compares the method with other methods on three publicly available datasets in order to confirm its efficacy. The outcomes show that the method performs better in terms of classification.

Source: ISPRS International Journal of Geo-Information

DOI: 10.3390/ijgi12120505

Authors: Qingqing Hong, Xinyi Zhong, Weitong Chen, Zhenghua Zhang, Bin Li

Institution: Yangzhou University

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,891

Funder: Yangzhou University