Land Cover Information Extraction Based on High-Resolution Remote Sensing Image in Coastal Areas

doi:10.13474/j.cnki.11-2246.2017.0041

Abstract

Abstract:

Remote sensing image classification provides an important method for the extraction of land cover information in coastal areas which is essential part of the national general survey of geographic conditions. This paper establishes the land cover classification system in coastal areas and then utilizes the classification method based on object-oriented GLC decision tree developed by Chinese Academy of Surveying and Mapping to extract the land cover information in coastal areas on the bases of a GF-1 high-resolution remote sensing image. This paper conducts classification experiment by choosing an area and compares the results with the reference classification image which verifies the validity and superiority of the proposed method. Its overall accuracy and Kappa coefficient are 87.201 8%,0.840 6 separately which are both higher than SVM. At the end of this thesis, the extraction process flow of land cover information in coastal areas based on the high-resolution remote sensing image is summarized.

Key words: land cover in coastal areas, object-oriented, GLC decision tree, GF-1, SVM, extraction process flow

CLC Number:

P237

ZHOU Xingyu, ZHANG Jixian, GAO Mianxin, SANG Huiyong, ZHAI Liang. Land Cover Information Extraction Based on High-Resolution Remote Sensing Image in Coastal Areas[J]. 测绘通报, 2017, 0(2): 19-24.

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