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Hyperspectral and LiDAR Data Classification Based on Structural Optimization Transmission
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DOI：10.1109/TCYB.2022.3169773.png)
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摘要
En 中文
With the development of the sensor technology, complementary data of different sources can be easily obtained for various applications. Despite the availability of adequate multisource observation data, for example, hyperspectral image (HSI) and light detection and ranging (LiDAR) data, existing methods may lack effective processing on structural information transmission and physical properties alignment, weakening the complementary ability of multiple sources in the collaborative classification task. The complementary information collaboration manner and the redundancy exclusion operator need to be redesigned for strengthening the semantic relatedness of multisources. As a remedy, we propose a structural optimization transmission framework, namely, structural optimization transmission network (SOT-Net), for collaborative land-cover classification of HSI and LiDAR data. Specifically, the SOT-Net is developed with three key modules: 1) cross-attention module; 2) dual-modes propagation module; and 3) dynamic structure optimization module. Based on above designs, SOT-Net can take full advantage of the reflectance-specific information of HSI and the detailed edge (structure) representations of multisource data. The inferred transmission plan, which integrates a self-alignment regularizer into the classification task, enhances the robustness of the feature extraction and classification process. Experiments show consistent outperformance of SOT-Net over baselines across three benchmark remote sensing datasets, and the results also demonstrate that the proposed framework can yield satisfying classification result even with small-size training samples.
Keyword:
Laser radar
Feature extraction
Optimization
Indexes
Hyperspectral imaging
Collaboration
Task analysis
Collaborative classification
convolutional neural network (CNN)
deep learning
hyperspectral image (HSI)
light detection and ranging (LiDAR) data
pattern recognition remote sensing
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
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