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Dual-Path Prototype Feature Decoupling Alignment Network for Panchromatic and Multispectral Classification
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AI
DOI：10.1109/TGRS.2025.3553857.png)
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Abstract
En 中文
In recent years, with the rapid advancements and widespread application of satellite photography technology, it has become increasingly possible to obtain high-quality panchromatic (PAN) and multispectral (MS) data, which has provided new opportunities and challenges for multisource information fusion and classification research. Remote sensing data have the characteristics of small interclass differences and large intraclass differences, which easily leads to category confusion in network learning. In addition, how to fully tap the advantages of multisource data, better align multisource features, improve classification accuracy, and achieve collaborative classification are key issues that need to be solved urgently. In this article, a dual-path prototype feature decoupling alignment network (DPFDA-Net) is designed to solve the above issues. The network consists of two components: a prototype feature embedding (PFE) module and a feature alignment module (FAM) based on prototype decoupling. In the feature extraction stage, the PFE module uses the prototype concept to learn the discriminative prototype features of each category of the dual-source data separately, making the boundaries between categories more obvious. The FAM operates at the dual-source prototype feature level and achieves feature alignment by decoupling single-source prototype features and performing feature transformation to supplement the missing information of another data source. Finally, we use the aligned features for classification. The results of the experiment demonstrate that our approach has made significant progress in improving classification precision. The code is available at https://github.com/Xidian-AIGroup190726/DPFDANet.
Keywords:
Prototypes
Feature extraction
Remote sensing
Sensors
Data mining
Data integration
Contrastive learning
Vectors
Training
Spatial resolution
Adaptive instance normalization (AdaIN)
deep learning
dual-source RS data
feature alignment
feature decoupling
prototype learning
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W