Light-ResKAN: A Parameter-Sharing Lightweight KAN with Gram Polynomials for Efficient SAR Image Recognition

Abstract

Synthetic Aperture Radar (SAR) image recognition is vital for disaster monitoring, military reconnaissance, and ocean observation. However, large SAR image sizes hinder deep learning deployment on resource-constrained edge devices, and existing lightweight models struggle to balance high-precision feature extraction with low computational requirements. The emerging Kolmogorov-Arnold Network (KAN) enhances fitting by replacing fixed activations with learnable ones, reducing parameters and computation. Inspired by KAN, we propose Light-ResKAN to achieve a better balance between precision and efficiency. First, Light-ResKAN modifies ResNet by replacing convolutions with KAN convolutions, enabling adaptive feature extraction for SAR images. Second, we use Gram Polynomials as activations, which are well-suited for SAR data to capture complex non-linear relationships. Third, we employ a parameter-sharing strategy: each kernel shares parameters per channel, preserving unique features while reducing parameters and FLOPs. Our model achieves 99.09%, 93.01%, and 97.26% accuracy on MSTAR, FUSAR-Ship, and SAR-ACD datasets, respectively. Experiments on MSTAR resized to $1024 \times 1024$ show that compared to VGG16, our model reduces FLOPs by $82.90 \times$ and parameters by $163.78 \times$. This work establishes an efficient solution for edge SAR image recognition.

Figures (8)

• Figure 1: Comparison of our proposed method with existing methods in terms of FLOPs and accuracy. The size of the circle represents the parameter size of the method. The graph shows the excellent balance between accuracy and computational resources of our method evaluated on MSTAR dataset resized to $1024 \times 1024$. Compared to the traditional VGG16 architecture, Light-ResKAN achieves a 0.12% increase in accuracy while demonstrating significant computational and parameter efficiency, evidenced by an 82.90 times reduction in FLOPs and a 163.78 times reduction in parameter count.
• Figure 2: Model architecture diagram of Light-ResKAN. (a) Provides its framework with three main modules: the shared activation function convolution $\phi_{n \times n}$, Bottleneck ResKAGN 1 and Bottleneck ResKAGN 2. (b) Details the internal structure of the shared activation function convolution $\phi_{n \times n}$. (c) Illustrates the architecture of Bottleneck ResKAGN 1 and Bottleneck ResKAGN 2. (d) Provides a symbol List.
• Figure 3: An example of $\phi_{2 \times 2}$ convolution in one channel. (a) The operation process and result of initial KAN convolution. (b) The operation process and result of the proposed $\phi_{2 \times 2}$ convolution with shared weights. Among them, ${y}_{1} = {\phi }_{11}\left( {a}_{1}\right) + {\phi }_{12}\left( {a}_{2}\right) + {\phi }_{21}\left( {a}_{3}\right) + {\phi }_{22}\left( {a}_{4}\right)$ and ${f}_{1} = \phi \left( {a}_{1}\right) + \phi \left( {a}_{2}\right) + \phi \left( {a}_{3}\right) + \phi \left( {a}_{4}\right)$
• Figure 4: Three SAR datasets. (a) Ten types of SAR images in MSTAR. (b) Ten types of SAR images in FUSAR-Ship. (c) Six types of SAR images in SAR-ACD.
• Figure 5: t-SNE visualization results of different methods on MSTAR dataset. (a) ASANet. (b) ConvnNeXt. (c) MACN. (d) VGG16. (e) Cross-HL. (f) Light-ResKAN (Ours). Colors represent different categories, and the distribution density of points and the compactness of clusters indicate the discriminative ability of the model for this task. The results indicate that Light-ResKAN can achieve the most compact intra class clusters and the most obvious inter class separation under the same data distribution, demonstrating stronger feature expression ability and higher classification performance.