Mask-Guided Correlation Learning for Few-Shot Segmentation in Remote Sensing Imagery
Shuo Li, Fang Liu, Licheng Jiao, Xu Liu, Puhua Chen, Lingling Li
Abstract
Few-shot segmentation aims to segment specific objects in a query image based on a few densely annotated images and has been extensively studied in recent years. In remote sensing, image segmentation faces challenges such as less training data, large intraclass diversity, and low foreground-background contrast. In this work, we propose a novel few-shot segmentation method in remote sensing imagery based on mask-guided correlation learning (MGCL) to alleviate the above challenges. In our MGCL, a novel mask-guided feature enhancement (MGFE) module is proposed, which makes features have intramask consistency by leveraging oversegmented masks. In order to enhance the contrast between foreground and background, a novel foreground-background correlation (FBC) module is proposed, which enhances background correlation representation by learning foreground correlation and background correlation separately. Furthermore, a novel mask-guided correlation decoder (MGCD) module is proposed to guide the decoder to focus on the consistency within the mask, thereby learning how to segment complete objects and improving segmentation accuracy. Sufficient experiments on the iSAID-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5^{i}$ </tex-math></inline-formula> and DLRSD-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5^{i}$ </tex-math></inline-formula> datasets show that our MGCL outperforms all comparative methods. In particular, in the one-shot setting of the iSAID-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5^{i}$ </tex-math></inline-formula> dataset, we achieve an mIoU of 39.92 based on ResNet50, which is an improvement of 4.25 over the state-of-the-art (SOAT) method. The visualization of features before and after the MGFE module further concretely demonstrates the motivation and advantages of our MGCL. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LiShuo1001/MGCL</uri>.