# LSKSANet: A Novel Architecture for Remote Sensing Image Semantic Segmentation Leveraging Large Selective Kernel and Sparse Attention Mechanism Source: [LSKSANet: A Novel Architecture for Remote Sensing Image Semantic Segmentation Leveraging Large Selective Kernel and Sparse Attention Mechanism]() Collection index: [llms.txt]() Data updated: 2025-10-26T06:56:30.471Z Authors: Miao Fu, Feng Gao, Ruzhuang Hua, Yanhai Gan, Xiaowei Zhou, Yang Zhou Published: 2024-06-03T11:50:40.000Z ## Abstract In this paper, we proposed large selective kernel and sparse attention network (LSKSANet) for remote sensing image semantic segmentation. The LSKSANet is a lightweight network that effectively combines convolution with sparse attention mechanisms. Specifically, we design large selective kernel module to decomposing the large kernel into a series of depth-wise convolutions with progressively increasing dilation rates, thereby expanding the receptive field without significantly increasing the computational burden. In addition, we introduce the sparse attention to keep the most useful self-attention values for better feature aggregation. Experimental results on the Vaihingen and Postdam datasets demonstrate the superior performance of the proposed LSKSANet over state-of-the-art methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()