Abstract
Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. <br><br> The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection accuracy of the proposed method is better than related methods.
Highlights
Passive remote sensing sensors used for Earth observations are primarily limited by their sensitivity to clouds and weather conditions
Irish et al (2000) proposed an automatic cloud cover assessment (ACCA) for images acquired by the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor
Oreopoulos et al (2011) proposed adaptations of ACCA to process images obtained from the MODIS sensors
Summary
Passive remote sensing sensors used for Earth observations are primarily limited by their sensitivity to clouds and weather conditions. Irish et al (2000) proposed an automatic cloud cover assessment (ACCA) for images acquired by the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. Their method uses available bands to establish a set of threshold-based filters. ACCA can effectively detect clouds, this algorithm may fail to distinguish snow from clouds in high-latitude areas (Zhu and Woodcock, 2012) To resolve this problem, Choi and Bindschadler (2004) developed a method to determine the optimal threshold of the normalized difference snow index (NDSI) by iteratively matching clouds and cloud shadow edges. To resolve this problem, Choi and Bindschadler (2004) developed a method to determine the optimal threshold of the normalized difference snow index (NDSI) by iteratively matching clouds and cloud shadow edges. Zhu and Woodcock (2012) utilized top of atmosphere (ToA) reflectance and temperatures in cloud and cloud shadow detection
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