Key points are not available for this paper at this time.
Semantic segmentation is a valuable task in practical applications for aerial images. Nevertheless, the segmentation performance is unsatisfactory due to aerial images’ huge intra-class variance and inter-class similarity. To solve this problem, we propose an approach to increase the distinction between classes and compact the features of the same class. Specifically, since a single aerial image contains only a small number of categories, which is fatal for previous contrastive learning, we discard InfoNCE loss in contrastive learning and use the simple Mean Square Error (MSE) loss that does not require negative samples to decouple the dispersion and compaction operations. Besides, we set up more representative prototypes for classes and extend the prototypes to the whole dataset level, which we call image- and dataset-level prototypes. Based on the calculated prototypes, we propose Multi-level intra-class Feature Compaction (MFC) and Multi-level inter-class Feature Dispersion (MFD) to compact the features of the same class and disperse the features of different classes in the latent feature space. More importantly, some measures are proposed to ensure the two do not conflict. MFC and MFD can be applied to any existing segmentation network to improve performance significantly without increasing computational complexity during inference. Moreover, we feed the calculated multi-level prototypes directly into the classifier, thus keeping the feature extraction and classifier consistent. Results on four challenging datasets, Deepglobe, iSAID, Potsdam, and Vaihingen, demonstrate the significant effect of our method, and sufficient ablation studies verify the role of each module.
Shan et al. (Sun,) studied this question.