Abstract and Applied Analysis

A Study on Coastline Extraction and Its Trend Based on Remote Sensing Image Data Mining

Yun Zhang, Xueming Li, Jianli Zhang, and Derui Song

Full-text: Open access

Abstract

In this paper, data mining theory is applied to carry out the field of the pretreatment of remote sensing images. These results show that it is an effective method for carrying out the pretreatment of low-precision remote sensing images by multisource image matching algorithm with SIFT operator, geometric correction on satellite images at scarce control points, and other techniques; the result of the coastline extracted by the edge detection method based on a chromatic aberration Canny operator has a height coincident with the actual measured result; we found that the coastline length of China is predicted to increase in the future by using the grey prediction method, with the total length reaching up to 19,471,983 m by 2015.

Article information

Source
Abstr. Appl. Anal., Volume 2013, Special Issue (2013), Article ID 693194, 6 pages.

Dates
First available in Project Euclid: 26 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1393449789

Digital Object Identifier
doi:10.1155/2013/693194

Citation

Zhang, Yun; Li, Xueming; Zhang, Jianli; Song, Derui. A Study on Coastline Extraction and Its Trend Based on Remote Sensing Image Data Mining. Abstr. Appl. Anal. 2013, Special Issue (2013), Article ID 693194, 6 pages. doi:10.1155/2013/693194. https://projecteuclid.org/euclid.aaa/1393449789


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