SPLG-Mamba：用于光学遥感图像显著目标检测的结构保持型局部-全局Mamba网络

中文摘要 AI 辅助

光学遥感图像显著目标检测（ORSI-SOD）需要在复杂背景、尺度变化和不规则目标形状下，输出保留目标完整性和结构连续性的密集预测结果。现有方法虽能定位显著区域，但预测结果常出现结构退化问题，包括前景响应碎片化、不完整或局部缺失，该问题与分层特征传播密切相关：浅层细节可能引入纹理诱导的背景响应，深层语义可能过度平滑弱结构，不受控的跨尺度融合可能干扰连贯区域。为解决此问题，本文提出用于ORSI-SOD的新型结构保持型局部-全局Mamba网络（SPLG-Mamba），具体而言，SPLG-Mamba整合了平滑细节重校准（SDR）、感知层级的局部-全局Mamba以及门控跨尺度融合（GCSF）：SDR在状态空间建模前对平滑后的响应和细节残差进行重校准，局部-全局Mamba将局部建模分配给浅层特征层级、全局建模分配给深层特征层级，GCSF在解码阶段控制跨尺度细节注入。在ORSSD、EORSSD和ORSI-4199数据集上的实验表明，该方法达到了当前最优结果，且结构完整性和连续性得到提升，代码可在指定URL获取。

英文摘要

Salient object detection in optical remote sensing images (ORSI-SOD) requires dense predictions that preserve object completeness and structural continuity under complex backgrounds, scale variation, and irregular object shapes. Existing methods often localize salient regions, but their predictions may still suffer from structural degradation, including fragmented, incomplete, or locally missing foreground responses. This degradation is closely related to hierarchical feature propagation, where shallow details can introduce texture-induced background responses, deep semantics may over-smooth weak structures, and uncontrolled cross-scale fusion can disturb coherent regions. To address this issue, we propose a novel Structure-Preserving Local-Global Mamba Network, SPLG-Mamba, for ORSI-SOD. Specifically, SPLG-Mamba integrates Smooth-Detail Recalibration (SDR), hierarchy-aware Local-Global Mamba, and Gated Cross-Scale Fusion (GCSF). SDR recalibrates smoothed responses and detail residuals before state-space modeling, Local-Global Mamba assigns local modeling to shallow feature levels and global modeling to deep feature levels, and GCSF controls cross-scale detail injection during decoding. Experiments on ORSSD, EORSSD, and ORSI-4199 demonstrate state-of-the-art results and improved structural completeness and continuity. The code is available at https://github.com/yxu9910/SPLG-Mamba

发表机构

• Nanjing University（南京大学）
• China Pharmaceutical University（中国药科大学）

机构由 AI 辅助整理，请以论文原文为准。