Self-Adaptive Cloud Detection Approach For Gaofen-1 Optical Remote Sensing Data

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.