Research on the extremely small target identification in aerial remote sensing images with negative example enhancement

Fan Li; Pingping Xu

doi:10.1117/12.3029656

21 June 2024 Research on the extremely small target identification in aerial remote sensing images with negative example enhancement

Fan Li, Pingping Xu

Author Affiliations +

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131670S (2024) https://doi.org/10.1117/12.3029656
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

In response to the significant difficulty in detecting extremely small targets in aerialremote sensing images, this research proposes a data enhancement method that optimizes the negative example selection strategy, that is, to achieve the objective by optimizing the strategy of selecting negative samples for training during the training process on the basis of existing deep learning-based target detection methods. Taking the Faster-RCNN model as an example, we use the outcome of model error identification as the negative example in the next epoch of the training process and adjusts the loss function according to the negative example category. Experimental shows that the algorithm can effectively enhance the detection accuracy of extremely small targets in remote sensing images, with a strong screening ability for interference regions in complicated backgrounds.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Fan Li and Pingping Xu "Research on the extremely small target identification in aerial remote sensing images with negative example enhancement", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131670S (21 June 2024); https://doi.org/10.1117/12.3029656

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KEYWORDS

Education and training

Small targets

Target detection

Remote sensing

Photography

Data modeling

Target recognition

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