PPMamba: Enhancing Semantic Segmentation in Remote Sensing Imagery by SS2D

Juwei Mu;Shangbo Zhou;Xingjie Sun
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Abstract

Remote sensing semantic segmentation is a critical technology in the field of remote sensing image processing, with broad applications in environmental monitoring, urban planning, disaster assessment, and resource exploration. Despite the transformative impact of convolutional neural networks (CNNs) on this domain, CNN-based methods often encounter limitations due to their localized receptive fields, which struggle to capture the global context necessary for accurate segmentation in complex remote sensing imagery. In this letter, a novel approach is presented for remote sensing semantic segmentation using a mamba-based model named PPmamba. The PPmamba model integrates Resblock and PPmamba within an encoder-decoder framework to effectively capture both local and global contextual information from high-resolution remote sensing images. Leveraging the strengths of the Mamba architecture, our model employs selective scanning to efficiently process long sequences, overcoming the limitations of traditional CNNs and transformers in handling large-scale images with complex scenes. Extensive experiments on two benchmark datasets (Potsdam and Vaihingen) demonstrate the superiority of our PPmamba model against state-of-the-art models, achieving significant improvements in segmentation results. The codes will be available at https://github.com/Jerrymo59/PPMambaSeg .
PPMamba:利用SS2D增强遥感图像的语义分割
遥感语义分割是遥感图像处理领域的一项关键技术,在环境监测、城市规划、灾害评估、资源勘探等领域有着广泛的应用。尽管卷积神经网络(cnn)在这一领域产生了变革性的影响,但基于cnn的方法由于其局部接受域而经常遇到局限性,难以捕捉复杂遥感图像准确分割所需的全局背景。本文提出了一种基于曼巴模型的遥感语义分割新方法——PPmamba。PPmamba模型将Resblock和PPmamba集成在一个编码器-解码器框架中,可以有效地从高分辨率遥感图像中捕获本地和全局上下文信息。利用曼巴架构的优势,我们的模型采用选择性扫描来有效地处理长序列,克服了传统cnn和变压器在处理复杂场景的大规模图像时的局限性。在两个基准数据集(Potsdam和Vaihingen)上进行的大量实验表明,我们的PPmamba模型与最先进的模型相比具有优势,在分割结果上取得了显着改善。这些代码可在https://github.com/Jerrymo59/PPMambaSeg上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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