Multi-Scale U-Shape MLP for Hyperspectral Image Classification

Moule Lin

doi:10.1109/LGRS.2022.3141547

Multi-Scale U-Shape MLP for Hyperspectral Image Classification

2022-01-01

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AI

DOI：10.1109/LGRS.2022.3141547

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Hyperspectral images (HSIs) 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 HSI 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 multi-scale channel (MSC) block and the U-shape multi-layer perceptron (UMLP) 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 that our model can outperform state-of-the-art methods across the board on three wide-adopted public datasets, namely Pavia University (PaviaU), Houston 2013, and Houston 2018.

Keywords:

Image coding

Shape

Unified modeling language

Semantics

Geoscience and remote sensing

Transforms

Registers

Compression model

hyperspectral image (HSI)

multi-layer perceptron

multi-scale shape

Multi-Scale U-Shape MLP for Hyperspectral Image Classification

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