Cloud Detection Method for High Resolution Remote Sensing Imagery Based on the Spectrum and Texture of Superpixels
Image cloud detection is an important part of high spatial resolution remote sensing imagery (HSRI) information processing and analysis. The spectral threshold selection of image cloud detection and the influence of cloud-like ground objects are two vital factors in determining cloud detection results of HSRI. With respect to these two issues, a novel cloud detection method for HSRI based on the spectrum and texture of superpixels is proposed in the paper. First, the adaptive image cloud detection spectral threshold is obtained according to the image equalization histogram. Second, the initial cloud detection result is obtained based on spectral threshold of the cloud detection and spectral attributes of superpixels. Third, the initial cloud detection result is refined based on the gray value and angular second moment of the superpixels local binary patterns texture to eliminate the influence of cloud-like ground objects. Finally, the cloud detection result is processed using the region growing algorithm and expansion algorithm to obtain an accurate cloud detection result. The experimental results show that the proposed method can obtain good cloud detection results.
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Document Type: Research Article
Publication date: April 1, 2019
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- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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