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

我们提出了一个名为多尺度 U 形多层感知器（MUMLP）的模型，它由设计的多尺度通道（MSC）模块和 U 形多层感知器（UMLP）结构组成，用于解决高光谱图像像素识别中的关键问题。通过详尽的实验验证，我们的模型在三个广泛应用的公开数据集上表现优于现有方法。