Diversity Consistency Learning for Remote-Sensing Object Recognition With Limited Labels

Zhao, Wenda; Tong, Tingting; Wang, Haipeng; Zhao, Fan; He, You; Lu, Huchuan

doi:10.1109/tgrs.2022.3210980

Cited by 4 publications

(1 citation statement)

References 46 publications

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“…Inception v3 [32], DenseNet121 [33], MobileNet [35], and Xception [37] are general CNNs and classification is carried out by extracting high-level features from images; their main limitation is that only global features are available, while detailed features are ignored. To address the problem of limited samples, FDN [5], DCL [31], and B-CNN [34] improve the recognition accuracy of remote sensing targets through multi-feature fusion and pseudotag training; however, they do not consider the fusion of different receptive field features, resulting in a low utilization rate of local information. ME-CNN [6] combines the CNN, Gabor filter, LBP operator, and other means, in extracting multiple features, providing more information than the FDN.…”

Section: Comparisons With Other Methodsmentioning

confidence: 99%

Contrastive Learning Network Based on Causal Attention for Fine-Grained Ship Classification in Remote Sensing Scenarios

Pan

et al. 2023

Remote Sensing

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Fine-grained classification of ship targets is an important task in remote sensing, having numerous applications in military reconnaissance and sea surveillance. Due to the influence of various imaging factors, ship targets in remote sensing images have considerable inter-class similarity and intra-class difference, which brings significant challenges to fine-grained classification. In response, we developed a contrastive learning network based on causal attention (C2Net) to improve the model’s fine-grained identification ability from local details. The asynchronous feature learning mode of “decoupling + aggregation” is adopted to reduce the mutual influence between local features and improve the quality of local features. In the decoupling stage, the feature vectors of each part of the ship targets are de-correlated using a decoupling function to prevent feature adhesion. Considering the possibility of false associations between results and features, the decoupled part is designed based on the counterfactual causal attention network to enhance the model’s predictive logic. In the aggregation stage, the local attention weight learned in the decoupling stage is used to carry out feature fusion on the trunk feature weight. Then, the proposed feature re-association module is used to re-associate and integrate the target local information contained in the fusion feature to obtain the target feature vector. Finally, the aggregation function is used to complete the clustering process of the target feature vectors and fine-grained classification is realized. Using two large-scale datasets, the experimental results show that the proposed C2Net method had better fine-grained classification than other methods.

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