Kolmogorov-Arnold Network for Remote Sensing Image Semantic Segmentation

TL;DR

This paper introduces DeepKANSeg, a novel semantic segmentation network for remote sensing images that leverages Kolmogorov Arnold Networks to better utilize high-dimensional features and improve decoding detail, outperforming existing methods.

Contribution

The paper proposes a new KAN-based architecture with a feature refinement module and a KAN-enhanced decoder, offering improved accuracy and interpretability over traditional models.

Findings

Superior accuracy on ISPRS Vaihingen and Potsdam datasets

Enhanced ability to capture fine-grained details

Improved interpretability of the segmentation model

Abstract

Semantic segmentation plays a crucial role in remote sensing applications, where the accurate extraction and representation of features are essential for high-quality results. Despite the widespread use of encoder-decoder architectures, existing methods often struggle with fully utilizing the high-dimensional features extracted by the encoder and efficiently recovering detailed information during decoding. To address these problems, we propose a novel semantic segmentation network, namely DeepKANSeg, including two key innovations based on the emerging Kolmogorov Arnold Network (KAN). Notably, the advantage of KAN lies in its ability to decompose high-dimensional complex functions into univariate transformations, enabling efficient and flexible representation of intricate relationships in data. First, we introduce a KAN-based deep feature refinement module, namely DeepKAN to effectively capture complex spatial and rich semantic relationships from high-dimensional features. Second, we replace the traditional multi-layer perceptron (MLP) layers in the global-local combined decoder with KAN-based linear layers, namely GLKAN. This module enhances the decoder's ability to capture fine-grained details during decoding. To evaluate the effectiveness of the proposed method, 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. They highlight the potential of KANs as a powerful alternative to traditional architectures in semantic segmentation tasks. Moreover, the explicit univariate decomposition provides improved interpretability, which is particularly beneficial for applications requiring explainable learning in remote sensing.