Let the loss impartial: a hierarchical unbiased loss for small object segmentation in high-resolution remote sensing images
2023
Qianpeng Chong | Mengying Ni | Jianjun Huang | Guangyi Wei | Ziyi Li | Jindong Xu
ABSTRACTThe progress in optical remote sensing technology presents both a possibility and challenge for small object segmentation task. However, the gap between human vision cognition and machine behavior still poses an inherent constrains to the interpretation of small but key objects in large-scale remote sensing scenes. This paper summarizes this gap as a bias of the machine against small object segmentation task, called scale-induced bias. The scale-induced bias causes the degradation in the performance of conventional remote sensing image segmentation methods. Therefore, this paper applies a straightforward but innovative insight to mitigate the scale-induced bias. Specifically, we propose a universal impartial loss, which leverages the hierarchical approach to alleviate two sub-problems separately. The pixel-level statistical methodology is applied to remove the bias between the background and small objects, and an emendation vector is introduced to alleviate the bias between small object categories. Extensive experiments explicitly manifest that our method is fully compatible with the existing segmentation structures, armed with the hierarchical unbiased loss, these structures will achieve satisfactory improvement. The proposed method is validated on two benchmark remote sensing image datasets, where it achieved a competitive performance and could narrow the gap between the human vision cognition and machine behavior.
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