Multi-Scale U-Shape MLP for Hyperspectral Image Classification
Moule Lin Weipeng Jing Donglin Di Guangsheng Chen Houbing Song
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