Multiview Transformer: Rethinking Spatial Information in Hyperspectral Image Classification

TL;DR

This paper introduces a multiview transformer model for hyperspectral image classification that effectively captures spatial-spectral features while avoiding overfitting, demonstrating superior performance on multiple datasets.

Contribution

It proposes a novel multiview transformer architecture combining MPCA, SED, and SPTT to improve hyperspectral image classification accuracy and robustness.

Findings

Outperforms state-of-the-art methods on three HSI datasets

Effectively mitigates spatial overfitting issues

Demonstrates robustness under strict experimental settings

Abstract

Identifying the land cover category for each pixel in a hyperspectral image (HSI) relies on spectral and spatial information. An HSI cuboid with a specific patch size is utilized to extract spatial-spectral feature representation for the central pixel. In this article, we investigate that scene-specific but not essential correlations may be recorded in an HSI cuboid. This additional information improves the model performance on existing HSI datasets and makes it hard to properly evaluate the ability of a model. We refer to this problem as the spatial overfitting issue and utilize strict experimental settings to avoid it. We further propose a multiview transformer for HSI classification, which consists of multiview principal component analysis (MPCA), spectral encoder-decoder (SED), and spatial-pooling tokenization transformer (SPTT). MPCA performs dimension reduction on an HSI via constructing spectral multiview observations and applying PCA on each view data to extract low-dimensional view representation. The combination of view representations, named multiview representation, is the dimension reduction output of the MPCA. To aggregate the multiview information, a fully-convolutional SED with a U-shape in spectral dimension is introduced to extract a multiview feature map. SPTT transforms the multiview features into tokens using the spatial-pooling tokenization strategy and learns robust and discriminative spatial-spectral features for land cover identification. Classification is conducted with a linear classifier. Experiments on three HSI datasets with rigid settings demonstrate the superiority of the proposed multiview transformer over the state-of-the-art methods.