针对国产中高分辨率卫星因缺乏红外波段导致的薄云检测精度低、阈值依赖性强、薄云漏检率高、现有算法自适应性与计算效率不足等问题, 本文提出一种基于IDL (Interactive Data Language) 的雾度优化快速自适应阈值分割方法 (Haze-Optimized Adaptive Thresholding, HOAT) 。该方法融合改进型HOT指数与动态权重OTSU算法, 通过多波段动态权重优化、矩阵运算加速及海洋场景自适应处理机制, 实现了复杂环境下云层的高效精准检测。实验表明, HOAT在薄云检测精度、耀斑误检抑制及处理效率上显著优于传统方法, 为灾害应急遥感监测提供可靠技术支持。

Addressing the challenges associated with detecting thin clouds in domestically produced mediumhigh resolution satellite imagery—such as low detection accuracy, strong threshold dependence, high thin cloud omission rates, and the insufficient adaptability and computational efficiency of existing algorithms— which primarily stem from the lack of infrared bands, this paper proposes a Haze-Optimized Adaptive Thresholding (HOAT) method based on IDL (Interactive Data Language) . The HOAT method integrates an improved Haze Optimization Transformation (HOT) index with a dynamically weighted OTSU algorithm. Through multi-band dynamic weight optimization, accelerated matrix operations, and an adaptive processing mechanism for marine scenarios, it achieves efficient and precise cloud detection in complex environments. Experimental results demonstrate that HOAT significantly outperforms traditional methods in thin cloud detection accuracy, suppression of sun glint false detection, and processing efficiency, providing reliable technical support for disaster emergency remote sensing monitoring.