双流差异建模与深度引导的多尺度融合用于红树林变化检测
Xin Wang1,2, Shuai Tang1, Qin Qin3
1School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究介绍了DSDGMNet,用于准确检测红树林变化,克服潮效应等挑战. 新方法提高了复杂的沿海环境中的检测精度.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 沿海生态 沿海生态
背景情况:
- 准确的红树林变化检测对于沿海生态系统监测至关重要.
- 潮干扰和不稳定的陆地水界对现有方法构成重大挑战.
- 深度学习模型很难区分真正的变化和潮引起的光谱变化.
研究的目的:
- 开发一种新的深度学习模型,用于准确检测红树林变化.
- 解决现有方法在处理潮干扰和边界复杂性的局限性.
- 在变化检测中,改善语义一致性和边界精度之间的平衡.
主要方法:
- 拟议的DSDGMNet (双流差异建模和深度引导的多尺度融合).
- 双流差异驱动的战略,以减少潮干扰,提高对结构变化的敏感性.
- 深度引导的多尺度融合模块,用于整合全球背景与细节的边界细节.
主要成果:
- 在GBCNR数据集上,DSDGMNet获得了71.36%的F1得分,超过了SNUNet (68.87%) 和ChangeFormer (66.39%).
- 在WHU-CD数据集中,DSDGMNet获得了91.38%的F1得分,超过了DDLNet (89.85%) 和ChangeFormer (88.82%).
- 在复杂的潮区内检测红树林变化的表现卓越.
结论:
- DSDGMNet有效地解决了在潮间环境中探测红树林变化的挑战.
- 拟议的方法显示了与现有的深度学习方法相比的显著改进.
- 强调了通过先进的遥感技术加强沿海生态系统监测的潜力.
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