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Cloud Automatic Detection in High-resolution Satellite Images Based on Morphological Features

EasyChair Preprint no. 160

10 pagesDate: May 24, 2018


Cloud cover is one of the main factors affecting the quality of remote sensing images, and cloud detection is the first problem to be solved in remote sensing data production. Therefore, cloud judgment becomes one of the primary tasks and key technologies of remote sensing image processing. The main detection objects include thin cloud, thick cloud and cloud shadow. The main interference factors include snow, gobi, desert, reflective buildings and other highlights. Firstly, the high reflectance characteristics of clouds are used to analyze the distribution of near-infrared and red bands in multispectral remote sensing images to realize preliminary cloud detection. And then using the detected suspected cloud region, different morphological features to distinguish between the cloud and other highlight features, achieved higher accuracy of cloud detection in high resolution satellite images.

Keyphrases: Cloud Automatic Detection, fractal geometry, morphological features, satellite images

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Xiang Liu and Junping Shen and Hao Ding},
  title = {Cloud Automatic Detection in High-resolution Satellite Images Based on Morphological Features},
  howpublished = {EasyChair Preprint no. 160},
  doi = {10.29007/c38f},
  year = {EasyChair, 2018}}
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