Coastline extraction from remote sensing image based on improved minimum filter

In this paper, coastline has been extracted from remote sensing image using the supervised classification method through the research of remote sensing image features. The coastline extraction can be very accurate when the seawater features are obviously consistent in image. For the calm sea image,...

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Bibliographic Details
Published in2010 Second IITA International Conference on Geoscience and Remote Sensing Vol. 2; pp. 44 - 47
Main Authors Xianfeng Zhang, Zhiyong Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2010
Subjects
coastline extraction
Computer languages
Correlation
correlation coefficient
Image resolution
improved minimum filter
PSNR
PSNR (Peak Signal to Noise Ratio)
remote sensing image
supervision classification
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Summary:In this paper, coastline has been extracted from remote sensing image using the supervised classification method through the research of remote sensing image features. The coastline extraction can be very accurate when the seawater features are obviously consistent in image. For the calm sea image, the coastline can be extracted accurately based on supervised classification. But for the images that have large variation and obvious reflective waves, it will be under the influence of sea surface texture obviously. In order to solve this problem, it put forward an improved minimum filter method to avoid the impact of sea surface texture. The method added two thresholds, which were the PSNR (peak signal-to-noise ratio) and the correlation coefficient. It limited the filter range in sea area. At last, semiautomatic extracted the accurate coastline in ArcGIS. This method can extract most of the coastline in the high-resolution image.
ISBN:9781424485147
1424485142
DOI:10.1109/IITA-GRS.2010.5603235