LiteCCLKNet: A lightweight criss‐cross large kernel convolutional neural network for hyperspectral image classification

Author:

Zhong Chengcheng12ORCID,Gong Na3,Zhang Zitong4,Jiang Yanan5,Zhang Kai2

Affiliation:

1. School of Science China University of Geosciences Beijing China

2. College of Information and Electrical Engineering China Agricultural University Beijing China

3. Department of Continuing Education Yantai Engineering & Technology College Yantai China

4. School of Earth Sciences and Resources China University of Geosciences Beijing China

5. School of Mathematical Sciences Beijing Normal University Beijing China

Abstract

AbstractHigh‐performance convolutional neural networks (CNNs) stack many convolutional layers to obtain powerful feature extraction capability, which leads to huge storing and computational costs. The authors focus on lightweight models for hyperspectral image (HSI) classification, so a novel lightweight criss‐cross large kernel convolutional neural network (LiteCCLKNet) is proposed. Specifically, a lightweight module containing two 1D convolutions with self‐attention mechanisms in orthogonal directions is presented. By setting large kernels within the 1D convolutional layers, the proposed module can efficiently aggregate long‐range contextual features. In addition, the authors effectively obtain a global receptive field by stacking only two of the proposed modules. Compared with traditional lightweight CNNs, LiteCCLKNet reduces the number of parameters for easy deployment to resource‐limited platforms. Experimental results on three HSI datasets demonstrate that the proposed LiteCCLKNet outperforms the previous lightweight CNNs and has higher storage efficiency.

Publisher

Institution of Engineering and Technology (IET)

Subject

Computer Vision and Pattern Recognition,Software

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