Semantic segmentation of remote sensing images is a key technology for land cover interpretation and target identification. Although convolutional neural networks (CNNs) have achieved remarkable success in this field, their inherent limitation of local receptive fields restricts their ability to model long-range dependencies and global contextual information. As a result, CNN-based methods often struggle to capture the comprehensive spatial context necessary for accurate segmentation in complex remote sensing scenes, leading to issues such as the misclassification of small objects and blurred or imprecise object boundaries. To address these problems, this paper proposes a new hybrid architecture called KPV-UNet, which integrates the Kolmogorov–Arnold Network (KAN) and the Pyramid Pooling Visual State Space Attention (PP-VSSA) block. KPV-UNet introduces a deep feature refinement module based on KAN and incorporates PP-VSSA to enable scalable long-range modeling. This design effectively captures global dependencies and abundant localized semantic content extracted from complex feature spaces, overcoming CNNs’ limitations in modeling long-range dependencies and inter-national context in large-scale complex scenes. In addition, we designed an Auxiliary Local Monitoring (ALM) block that significantly enhances KPV-UNet’s perception of local content. Experimental results demonstrate that KPV-UNet outperforms state-of-the-art methods on the Vaihingen, LoveDA Urban, and WHDLD datasets, achieving mIoU scores of 84.03%, 51.27%, and 62.87%, respectively. The proposed method not only improves segmentation accuracy but also produces clearer and more connected object boundaries in visual results.

Deep learning has advanced medical image segmentation, yet existing methods struggle with complex anatomical structures. Mainstream models, such as CNN, Transformer, and hybrid architectures, face challenges including insufficient information representation and redundant complexity, which limit their clinical deployment. Developing efficient and lightweight networks is crucial for accurate lesion localization and optimized clinical workflows. We propose the VML-UNet, a lightweight segmentation network with core innovations including the CPMamba module and the multi-scale local supervision module (MLSM). The CPMamba module integrates the visual state space (VSS) block and a channel prior attention mechanism to enable efficient modeling of spatial relationships with linear computational complexity through dynamic channel-space weight allocation, while preserving channel feature integrity. The MLSM enhances local feature perception and reduces the inference burden. Comparative experiments were conducted on three public datasets, including ISIC2017, ISIC2018, and PH2, with ablation experiments performed on ISIC2017. VML-UNet achieves 0.53 M parameters, 2.18 MB memory usage, and 1.24 GFLOPs time complexity, with its performance on the datasets outperforming comparative networks, validating its effectiveness. This study provides valuable references for developing lightweight, high-performance skin lesion segmentation networks, advancing the field of skin lesion segmentation.