Automated Retrieval of Cloud Masks from the HJ-1 WVC Imagery

Robust and automated cloud discrimination is regarded as an important step toward the extraction and analysis of cloud-free surface features, especially for high-resolution images only covering visible and near-infrared spectrum, like data from the wide view CCD cameras (WVC) of HJ-1 minisatellites. A cloud screening algorithm designed for the HJ-1 (China) multispectral WVC data is presented using a combination of Tasseled Cap (TC) transformation and an unsupervised classification known as the iterative self-organizing data analysis (ISODATA). Two filters, one of which is cluster-based and the other uses a pixel-based analysis that employs greenness index and wetness index both extracted from the TC transformation, are successively driven to filter out cloud-pixels. The performance of the proposed algorithm is investigated for six HJ-1 WVC scenes with typical cloud coverage. Two comparisons, respectively, with one method based on the combination of the TC transformation and linear constrains (TCLC) and the other using the maximum-likelihood classification (MLC), are given in this study. The initial results from both comparisons show an impressive agreement, especially for regions covered by thick clouds or large thin clouds. Although this agreement decreases significantly for other complex situations, such as the presence of numerous small thin clouds, overall agreements still stay on an acceptable level. A considerable contribution, therefore, can be expected by this method for those high-resolution data with less frequent observations and a narrow spectrum cover.