Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification

TL;DR

This paper introduces DSNet, a dual-branch deep learning model that effectively integrates subpixel and spectral features for hyperspectral image classification, improving accuracy by addressing mixed pixels and nonlinear properties.

Contribution

The novel DSNet architecture combines subpixel unmixing with deep autoencoder features, enabling unsupervised abundance estimation and enhanced classification performance.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively handles mixed pixels and nonlinear subpixel properties

Provides reliable class boundary delineation

Abstract

Deep learning (DL) has been widely applied into hyperspectral image (HSI) classification owing to its promising feature learning and representation capabilities. However, limited by the spatial resolution of sensors, existing DL-based classification approaches mainly focus on pixel-level spectral and spatial information extraction through complex network architecture design, while ignoring the existence of mixed pixels in actual scenarios. To tackle this difficulty, we propose a novel dual-branch subpixel-guided network for HSI classification, called DSNet, which automatically integrates subpixel information and convolutional class features by introducing a deep autoencoder unmixing architecture to enhance classification performance. DSNet is capable of fully considering physically nonlinear properties within subpixels and adaptively generating diagnostic abundances in an unsupervised manner to achieve more reliable decision boundaries for class label distributions. The subpixel fusion module is designed to ensure high-quality information fusion across pixel and subpixel features, further promoting stable joint classification. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of DSNet compared with state-of-the-art DL-based HSI classification approaches. The codes will be available at https://github.com/hanzhu97702/DSNet, contributing to the remote sensing community.