Cloud detection of satellite imagery is very important for quantitative remote sensing research and remote sensing applications. However, many satellite sensors don’t have enough bands for a quick, accurate, and simple detection of clouds. Particularly, the newly launched moderate to high spatial resolution satellite sensors of China, such as the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) and the wide field of view (WFV) sensor on-board the Gao Fen 1 (GF-1), only have four available bands including blue, green, red, and near infrared bands, which are far from the requirements of most could detection methods. In order to solve this problem, an improved and automated cloud detection method for Chinese satellite sensors called OCM (Object oriented Cloud and cloud-shadow Matching method) is presented in this paper. It firstly modified the Automatic Cloud Cover Assessment (ACCA) method, which was developed for Landsat-7 data, to get an initial cloud map. The modified ACCA method is mainly based on threshold and different threshold setting produces different cloud map. Subsequently, a strict threshold is used to produce a cloud map with high confidence and large amount of cloud omission and a loose threshold is used to produce a cloud map with low confidence and large amount of commission. Secondly, a corresponding cloud-shadow map is also produced using the threshold of near-infrared band. Thirdly, the cloud maps and cloud-shadow map are transferred to cloud objects and cloud-shadow objects. Cloud and cloud-shadow are usually in pairs; consequently, the final cloud and cloud-shadow maps are made based on the relationship between cloud and cloud-shadow objects. OCM method was tested using almost 200 HJ-1/CCD images across China and the overall accuracy of cloud detection is close to 90%.
KEYWORDS: Clouds, Satellites, Reflectivity, Detection and tracking algorithms, Remote sensing, Solar radiation, Meteorological satellites, Visible radiation, Temperature metrology, Algorithm development
Cloud detection is a key work for the estimation of solar radiation from remote sensing. Particularly, the
detection of thin cirrus cloud and the edges of thicker cloud is critical and difficult. To obtain accurate
estimates of cloud cover of MTSAT-1R image, we propose an effective cloud detection algorithm for
improving the detection of thin cirrus cloud and the edges of thicker cloud. Using the brightness
temperature difference (BTD) and lookup table to identify cloud-free and cloud-filled pixels is not
sufficient for MTSAT-1R data on the region of China. Therefore, a new lookup table (LUT) is made by
extending the original one. On the basis of the exiting method, in order to apply to the MTSAT-1R
satellite data in China region, we expand the scope of the latitude and extend the applicable scope of
satellite zenith angle. We change the interpolation method from linear mode to nonlinear mode. The
evaluation results indicate that our proposed method is effective for the cirrus and the edges of thicker
cloud detection of MTSAT-1R in China region.