Few-Shot Aerial Image Semantic Segmentation Leveraging Pyramid Correlation Fusion

Abstract

Few-shot semantic segmentation has gained significant attention due to its ability to segment novel objects using only a limited number of labeled samples, thereby addressing the problem of overfitting caused by a lack of training data. Although this technique is widely studied in the field of computer vision, there are few methods for remote sensing images. Prevalent few-shot semantic segmentation methods can achieve remarkable results for natural images, but they are difficult to apply to remote sensing image processing because existing methods rarely take into consideration the large scale and resolution differences in remote sensing images. Consequently, it is hard for them to obtain correct semantic guidance from few annotated remote sensing images. To tackle these problems, this article proposes the pyramid correlation fusion network (PCFNet) to promote the ability to mine helpful information by calculating multi-scale pixel-wise semantic correspondence. Particularly, the dual distance correlation (DDC) module is designed to simultaneously compute the cosine similarity and Euclidean distance between query features and support features, producing adequate guidance information to determine the category of each pixel. Moreover, to improve segmentation accuracy for small objects, the scale-aware cross-entropy loss (SACELoss) is introduced to dynamically assign loss weights according to the actual sizes of objects. This enables smaller objects to be assigned larger weight values and thus receive more attention during training. Comprehensive experiments on both the iSAID-5 ⁱ and DLRSD-5 ⁱ datasets demonstrate that our method outperforms state-of-the-art few-shot semantic segmentation methods. Our code is available at https://github.com/TinyAway/PCFNet.