提出SPLG-Mamba网络，提升遥感图像显著目标检测的结构完整性。
SPLG-Mamba: Structure-Preserving Local-Global Mamba Network for Salient Object Detection in Optical Remote Sensing Images
- 分层设计局部-全局Mamba模块，分别处理浅层细节与深层语义
- 通过门控跨尺度融合控制细节注入，减少结构断裂
- 在三个遥感数据集上达到领先性能，尤其改善不规则目标连续性
光学遥感图像显著目标检测（ORSI-SOD）需在复杂背景、尺度变化和不规则形状下实现密集预测，并保持目标完整性和结构连续性。现有方法常导致预测出现碎片化、不完整或局部缺失等结构退化问题，根源在于层级特征传播：浅层细节易引入纹理干扰背景响应，深层语义可能过度平滑弱结构，跨尺度融合失控会破坏连贯区域。为此，本文提出新型结构保全型局部-全局Mamba网络SPLG-Mamba，集成平滑-细节重校准（SDR）、层次感知局部-全局Mamba及门控跨尺度融合（GCSF）。SDR在状态空间建模前重校准平滑响应与细节残差；局部-全局Mamba将局部建模分配至浅层特征，全局建模用于深层特征；GCSF在解码阶段控制跨尺度细节注入。在ORSSD、EORSSD和ORSI-4199上的实验表明，该方法达到当前最优性能，显著提升结构完整性和连续性。代码已开源。
原文摘要 · Abstract (English)
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
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