Kolmogorov–Arnold Network for Remote Sensing Image Semantic Segmentation

Abstract

Semantic segmentation is vital for remote sensing applications, where accurate feature extraction and representation are essential. Existing encoder–decoder architectures often fail to fully utilize high-dimensional features and recover fine details during decoding. To address this problem, we propose DeepKANSeg, a novel network based on the Kolmogorov–Arnold network (KAN). KAN’s ability to decompose complex functions into univariate transformations enables flexible modeling of intricate data patterns. Our approach introduces two key innovations: a KAN-based deep feature refinement (DFR) module composed of DeepKAN to capture complex spatial and semantic relationships from high-dimensional features, and a global–local KAN (GLKAN) module replacing the traditional multilayer perceptron (MLP) layers with KAN-based linear layers to enhance fine-grained decoding. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on two well-known fine-resolution remote sensing benchmark datasets, namely ISPRS Vaihingen and ISPRS Potsdam. The results demonstrate that the KAN-enhanced segmentation model achieves superior performance in terms of accuracy compared to state-of-the-art methods. Moreover, the univariate decomposition improves interpretability, making it suitable for explainable learning in remote sensing. The source code for this work will be accessible at https://github.com/sstary/SSRS