Volume 6 Number 7 (Jul. 2011)
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JSW 2011 Vol.6(7): 1289-1296 ISSN: 1796-217X
doi: 10.4304/jsw.6.7.1289-1296

Automatic Cloud Detection and Removal Algorithm for MODIS Remote Sensing Imagery

Lingjia Gu, Ruizhi Ren, Shuang Zhang
College of electronic science & engineering, JiLin University, Changchun, China

Abstract—Cloud is one of the most common interferers in Moderate Resolution Imaging Spectrum-radiometer (MODIS) remote sensing imagery. Because of cloud interference, much important and useful information covered by cloud cannot be recovered well. How to detect and remove cloud from MODIS imagery is an important issue for wide application of remote sensing data. In general, cloud can be roughly divided into the two types, namely, thin cloud and thick cloud. In order to effectively detect and eliminate cloud, an automatic algorithm of cloud detection and removal is proposed in this paper. Firstly, several necessary preprocessing works need to be done for MODIS L1B data, including geometric precision correction, bowtie effect elimination and stripe noise removal. Furthermore, through analyzing the cloud spectral characters derived from the thirty-six bands of MODIS data, it can be found the spectral reflections of ground and cloud are different in various MODIS bands. Hence, cloud and ground can be respectively identified based on the analysis of multispectral characters derived from MODIS imagery. Cloud removal processing mainly aims at cloud region rather than whole image, which can improve processing efficiency. As for thin cloud and thick cloud regions, the corresponding cloud removal algorithms are proposed in this paper. Experimental results demonstrate that the proposed algorithms can effectively detect and remove cloud from MODIS imagery, which can meet the demands of postprocessing of remote sensing imagery.

Index Terms—MODIS data, Cloud detection, Thin cloud removal, Thick cloud removal, Multispectral image analysis


Cite: Lingjia Gu, Ruizhi Ren, Shuang Zhang, "Automatic Cloud Detection and Removal Algorithm for MODIS Remote Sensing Imagery," Journal of Software vol. 6, no. 7, pp. 1289-1296, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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