Multi-Scale U-Shape MLP for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2307.10186v1
• Date: Wed, 5 Jul 2023 08:52:27 GMT
• Title: Multi-Scale U-Shape MLP for Hyperspectral Image Classification
• Authors: Moule Lin, Weipeng Jing, Donglin Di, Guangsheng Chen, Houbing Song
• Abstract summary: Two challenges in identifying pixels of the hyperspectral image are respectively representing the correlated information among the local and global, as well as the abundant parameters of the model. We propose a Multi-Scale U-shape Multi-Layer Perceptron (MUMLP) model consisting of the designed MSC (Multi-Scale Channel) block and the U-shape Multi-Layer Perceptron structure. Our model can outperform state-of-the-art methods across-the-board on three wide-adopted public datasets.
• Score: 13.85573689689951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral images have significant applications in various domains, since they register numerous semantic and spatial information in the spectral band with spatial variability of spectral signatures. Two critical challenges in identifying pixels of the hyperspectral image are respectively representing the correlated information among the local and global, as well as the abundant parameters of the model. To tackle this challenge, we propose a Multi-Scale U-shape Multi-Layer Perceptron (MUMLP) a model consisting of the designed MSC (Multi-Scale Channel) block and the UMLP (U-shape Multi-Layer Perceptron) structure. MSC transforms the channel dimension and mixes spectral band feature to embed the deep-level representation adequately. UMLP is designed by the encoder-decoder structure with multi-layer perceptron layers, which is capable of compressing the large-scale parameters. Extensive experiments are conducted to demonstrate our model can outperform state-of-the-art methods across-the-board on three wide-adopted public datasets, namely Pavia University, Houston 2013 and Houston 2018

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