Abstract
Abstract. This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.
Highlights
With the recent rapid developments of optical remote sensor, obtaining high resolution remote sensing images is an easy task
The image windows size directly affects the result of the cloud detection of support vector machine
Support vector machine (SVM) method will obtain a high accuracy of cloud detection, but the time consumption is higher
Summary
With the recent rapid developments of optical remote sensor, obtaining high resolution remote sensing images is an easy task now. As early as 1994, based on the difference of clouds’ spectral characteristics in the infrared and visible light, Yu Fan investigated the infrared-visible light spectra in two-dimensional feature space for about a hundred of cloud samples, and presented a method to determine the various types of clouds and the corresponding distribution of clusters using satellite cloud pictures. This algorithm used in this study greatly reduces the misjudgment and improves the detection accuracy
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