Abstract
Cloud detection is an essential and important process in satellite remote sensing. Researchers proposed various methods for cloud detection. This paper reviews recent literature (2004–2018) on cloud detection. Literature reported various techniques to detect the cloud using remote-sensing satellite imagery. Researchers explored various forms of Cloud detection like Cloud/No cloud, Snow/Cloud, and Thin Cloud/Thick Cloud using various approaches of machine learning and classical algorithms. Machine learning methods learn from training data and classical algorithm approaches are implemented using a threshold of different image parameters. Threshold-based methods have poor universality as the values change as per the location. Validation on ground-based estimates is not included in many models. The hybrid approach using machine learning, physical parameter retrieval, and ground-based validation is recommended for model improvement.
Highlights
Cloud is a visible mass of condensed water vapor in the atmosphere typically floating high above the general level of the ground
Cloud/shadow-detection algorithm based on spectral indices (CSD-SI) [39] proposed the cloud index (CI) and cloud shadow index (CSI) to indicate the potential clouds and cloud shadows based on their physical reflective characteristics
Deep learning Deep learning Decision tree ANN Probabilistic latent semantic analysis Classical algorithm Based on the green channel of total-sky visible images Adaptive thresholding approach Fog Stability Index (FSI) Color models (HSV and RGB color model) Ground-based ceilometer
Summary
Cloud is a visible mass of condensed water vapor in the atmosphere typically floating high above the general level of the ground. Authors of [27] proposed a cloud detection method for multi-spectral remote-sensing images from Landsat 8 They applied the Simple Linear Iterative Cluster (SLIC) method, and two-step super pixel classification strategy is used to predict each pixel as cloud or noncloud. Authors of [30] proposed multiple convolutional neural networks designed for highresolution remote-sensing imagery They applied the adaptive simple linear iterative clustering (A-SCLI) algorithm to the segmentation of the satellite images. The cloud detection method on polarization satellite images [23] has used the multi-spectrum and polarization characteristics and the concept of a dynamic threshold for underlying surfaces in different time and areas This proposed method can discriminate clouds from snow/ ice. Authors of [35] presented a new test for the detection of snow which uses an image gradient to detect regions of snow.
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