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Multiscale Deep Learning Network With Self-Calibrated Convolution for Hyperspectral and LiDAR Data Collaborative Classification
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AI
DOI：10.1109/TGRS.2021.3106025.png)
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Abstract
En 中文
In this article, we propose a novel multiscale deep learning network with self-calibrated convolution (MSNetSC) for hyperspectral and light detection and ranging (LiDAR) data collaborative classification. Conventional deep learning methods have limitations in extracting multiscale features at a granular level from multimodality data and fusing these features in a context-awareness way, which will severely restrict the performance of hyperspectral and LiDAR data joint classification. The proposed multiscale deep learning network utilizes a hierarchical residual structure combined with self-calibrated convolution to extract features with different receptive fields, and this can enhance the model's capability to represent the multimodality data. Besides, we employ spectral and spatial self-attention modules to adaptively calibrate weights of features with different scales, thereby enhancing the discriminative ability of extracted multiscale features. Furthermore, the attentional feature fusion module can dynamically and adaptively fuse the features from multimodality data in a contextual scale-aware way, and this attention-based feature fusion method will further improve the collaborative classification performance of hyperspectral and LiDAR data. Four benchmark multimodality data (i.e., hyperspectral and LiDAR data) sets collected by different sensors and at different acquisition times are employed for joint classification experiments. These comparative classification results and ablation study sufficiently certify the superiority of the proposed model in terms of collaborative classification accuracy when compared with other state-of-the-art methods.
Keywords:
Feature extraction
Laser radar
Hyperspectral imaging
Data mining
Convolution
Collaboration
Fuses
Attentional feature fusion (AFF)
collaborative classification
hierarchical residual network
hyperspectral image (HSI)
light detection and ranging (LiDAR)
self-calibrated convolution
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W