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Channel-Layer-Oriented Lightweight Spectral-Spatial Network for Hyperspectral Image Classification
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DOI：10.1109/TGRS.2024.3350055.png)
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摘要
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Hyperspectral image (HSI) classification is commonly influenced by convolution neural networks (CNNs). However, the large number of parameters and computational complexity associated with CNNs can limit their practical application, particularly when computing and storage resources are limited. To address this challenge, we propose a channel-layer-oriented lightweight network for HSI classification. Motivated by existing structures that typically set large channels and stack multiple layers, we give more optimal solutions strategically to further compress the model. For intralayer feature extraction, we develop a channel-oriented spectral-spatial module (COS2M), which introduces a dual-single-channel (DSC) 3-D convolution that works in conjunction with depthwise convolution to fully extract spectral-spatial information. For interlayer information transmission, we propose a novel neighbor-pixel-aware activation function (NPAF), where the activation of a single pixel is determined by the learnable interaction with its neighbor range that enhances information transmission and improves the network's fitting ability through the single activation layer. By implementing these strategies, we aim to overcome the limitations of traditional CNNs and enable efficient HSI classification within resource-constrained environments. The whole network is designed to be a compact end-to-end structure. It achieves better classification performance than other deep learning methods and lightweight models, even with limited training samples. The network parameters, model complexity, and inference time also demonstrate significant superiority, as confirmed by experiments on three benchmark datasets. The source codes are available publicly at: https://github.com/AchunLee/CLOLN_TGRS
Keyword:
Activation function
convolution channel
efficient model
hyperspectral image (HSI) classification
lightweight module
neural network
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
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