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.

It is challenging for active contour models (ACMs) to segment weak-edge and noisy images efficiently and accurately. To solve this problem, a novel ACM is proposed in this work. The proposed ACM achieves high-precision segmentation for weak-edge and noisy images using a non-local feature fitting energy function and a scalable normalization method. The non-local feature fitting energy function is constructed based on the distances calculated by Jeffreys divergence between non-local weighted fitting images and the image processed by the non-local means (NLM) algorithm. The non-local weighted fitting images include the fitting foreground and background with image edge features. The images processed by the NLM algorithm is used to reduce the influence of noise. The data-driven term, obtained by minimizing the non-local feature fitting energy function, is computed before the level set iteration, which improves the computation speed. In addition, a scalable normalization method is proposed to normalize the data-driven term. The ability to distinguish the targets from the background for different types of images is enhanced by adjusting a scaling factor, improving the robustness and accuracy of the proposed model. Experimental results demonstrate the advantages of the proposed model.

Methods combining deep neural networks (DNN) and active contour models (ACM) have shown effective performance in building segmentation tasks. However, due to the independent structural design, it suffers from a large amount of parameters and computational cost. To address the above problem and further explore the performance effect of the coupling DNN and ACM methods, we present a Parameter-efficient Differentiable Active Contour (PDAC) network for precisely segmenting buildings in remote sensing scenarios. Specifically, an adapter is applied to fine-tune the encoder, instead of initializing a new one. Based on it, a semantic segmentation model can achieve further localization improvement by introducing a minor amount of parameters. Experiments on two BE datasets show that PDAC achieves better performance with nearly half of the computational resources than the baseline.