A Two-stage Method With Lightweight Network and Active Contour Model for Remote Sensing Image Segmentation

Active contour models (ACMs) have shown effectiveness on remote sensing (RS) image segmentation tasks. However, this type of method still faces two important problems when segmenting RS images. First, not using high-level semantic information makes it difficult for ACM to distinguish targets and backgrounds with similar textures. Second, the manual contour initialization required by ACMs is inconvenient and inefficient. To address the above problems, we propose a two-stage segmentation method that consists of an improved U-Net using a mixed pooling attention and an ACM (UMPA-ACM). In the first stage, a lightweight network based on U-Net structure is developed to extract semantic features while reducing computational cost. A mixed pooling attention (MPA) module is designed to enhance the ability of our proposed network to extract high-level semantic information from RS images. In the second stage, an adaptive feature enhancement (AFE) module computes grayscale information from original images and feature maps produced by the lightweight network and then fuses them to improve the intensity of target edges; a morphological-threshold process (MTP) module automatically generates appropriate initial contours for targets from the semantic feature maps instead of manual contour initialization. Then, a new ACM, proposed based on pre-fitting foreground and background in local regions, uses the statistical characteristics of local intensities and the bias field correction to suppress the interference of non-target regions, thereby improving segmentation accuracy. Experimental results show that the mean Dice Similarity Coefficient (mDSC) and the mean Intersection over Union (mIoU) of our method are higher than those of the suboptimal method by 1.21% and 1.60%, respectively, on average for segmenting images from six RS datasets, which verify the advantage of our proposed method.