Automatic and high-precise extraction for water information from multispectral images with the step-by-step iterative transformation mechanism

LUO Jian-cheng1; SHENG Yong-wei2; SHEN Zhan-feng1; LI Jun-li3; GAO Li-jing1 1. Institute of Remote Sensing Applications; Chinese Academy of Sciences; Beijing 100101; China. Department of Geography; University of California; Los Angeles CA90095-1524; USA. Xinjiang Institute of Ecology and Geography; Urumqi 830011

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Automatic and high-precise extraction for water information from multispectral images with the step-by-step iterative transformation mechanism
Automatic and high-precise extraction for water information from multispectral images with the step-by-step iterative transformation mechanism
2009年13卷第4期页码：604-609
纸质出版：2009
DOI：
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[1].Automatic and high-precise extraction for water information from multispectral images with the step-by-step iterative transformation mechanism[J].遥感学报,2009,13(04):604-609. DOI：

Automatic and high-precise extraction for water information from multispectral images with the step-by-step iterative transformation mechanism[J]. Journal of Remote Sensing, 2009, 13(4): 604-609. DOI：

摘要

High-precision and automation are very important objectives of remote sensing information extraction research. This paper takes LANDSAT ETM as remote sensing data source

and uses spatial scaling （scale transformation） mechanism of "whole-local" based on Normalized Difference Water Index （NDWI）. Combining the segmentation and classification at whole and local scales respectively

this study fuses the hierarchical knowledge of water extraction and sets up an iterative algorithm to achieve the gradually approach of the water body’s optimal margin

so that we can extract the high-precise water body information. The experiment of plateau lake information extraction demonstrated that the complex and various information of water body could be extracted accurately

and the confusion between water body and shadow information could also be effectively avoided.

Abstract

High-precision and automation are very important objectives of remote sensing information extraction research. This paper takes LANDSAT ETM as remote sensing data source

and uses spatial scaling （scale transformation） mechanism of "whole—local" based on Normalized Difference Water Index （NDWI）. Combining the segmentation and classification at whole and local scales respectively

this study fuses the hierarchical knowledge of water extraction and sets up an iterative algorithm to achieve the gradually approach of the water body’s optimal margin

and the confusion between water body and shadow information could also be effectively avoided.

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