Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification

Deep learning (DL) has been widely applied to 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...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-13
Main Authors: Han, Zhu, Yang, Jin, Gao, Lianru, Zeng, Zhiqiang, Zhang, Bing, Chanussot, Jocelyn
Format: Article
Language:English
Subjects:
Autoencoder network
Classification
Data integration
Decoding
Deep learning
deep learning (DL)
Feature extraction
hyperspectral image classification
Hyperspectral imaging
hyperspectral unmixing (HU)
Image classification
Information retrieval
Machine learning
Pixels
Remote sensing
Spatial data
Spatial discrimination learning
Spatial resolution
subpixel feature
Task analysis
Training
Vectors
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Summary:Deep learning (DL) has been widely applied to 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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3418583