红外与毫米波学报2019，Vol.38Issue(1)：103-114,12.DOI:10.11972/j.issn.1001-9014.2019.01.017
高分一号光学遥感数据自适应云区识别
Self-adaptive cloud detection approach for GaoFen-1 optical remote sensing data
摘要
Abstract
Cloud detection for remote sensing imageries is a fundamental as well as significant step due to the inevitable existence of large amount of clouds in the optical remote sensing data. A highly efficient cloud detection approach is capable of saving data collection cost and improving data utilization efficiency. Homomorphic filtering algorithm is one of the most commonly methods that based on single-scene image for detecting clouds. This algorithm has the advantage of fast computation and high accuracy in cloud areas detection. However, the detected cloud areas are heavily dependent on the cut-off frequency of the homomorphic filter. The homomorphic filtering progress usually uses cut-off frequency with empirical value which might not be applicable to large amount of intricate input data. Therefore, this paper aims to construct the relationship between the image spectra power and the filter cut-off frequency. Based on the domestic high spatial resolution optical remote sensing data GF-1, this research makes the detection of clouds could be process to achieve a bulk deal. Our approach make the cut-off frequency self-adaptive changes rather than used empirical value when compared with the traditional homomorphic filtering, thus it could be able to meet more complicated scenarios. Further, the post-processing steps including whiteness index, spectral threshold, and morphological opening and closing operators are applied to coarse cloud mask to optimize results. We have tested on 98 GF-1 high resolution multispectral imageries, results indicated that our approach is capable of detecting cloud as well as haze areas with high accuracy of 93. 81％. This novel self-adaptive method shows its great application potential for real-time and high efficient cloud detection, meanwhile reduced the error detection rates caused by high reflectance ground objects.关键词
云区识别/自适应/同态滤波/GF-1遥感数据/截止频率Key words
cloud detection/self-adaptive/Homomorphic filtering/GaoFen-1 remote sensing data/cutoff frequency分类
数理科学引用本文复制引用
蒙诗栎,庞勇,张钟军,李增元..高分一号光学遥感数据自适应云区识别[J].红外与毫米波学报,2019,38(1):103-114,12.基金项目
十三五"国家重点研发计划"多尺度落叶松人工林生长预测"(2017YFD0600404) （2017YFD0600404）
国家自然科学基金"基于高分辨率遥感数据的森林生物多样性监测"(31570546) （31570546）
中央高校基本科研业务费专项资金项目"L波段森林的微波辐射与传输特性研究"(2015KJJCA12) （2015KJJCA12）
