A Coupled Co-Occurrence Matrix/Multi-Scale Segmentation Method to Extract Water from High Resolution Remote Sensing Image

This study developed a coupled co-occurrence matrix/multi-scale segmentation method to improve extraction precision of water from high-resolution remote sensing images. Two images of Kunming city (subject A & B) were obtained from Quick Bird image gallery, pre-processed by co-occurrence matrix,...

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Bibliographic Details
Published in2018 26th International Conference on Geoinformatics pp. 1 - 4
Main Authors Liang, Xiao, Hu, Wenying
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Birds
co-occurrence matrix
Data mining
high-resolution remote sensing image
Image resolution
Image segmentation
Information analysis
Multi-scale segmentation
Object-oriented
Remote sensing
Roads
Urban areas
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ISSN2161-0258
DOI10.1109/GEOINFORMATICS.2018.8557177

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Summary:This study developed a coupled co-occurrence matrix/multi-scale segmentation method to improve extraction precision of water from high-resolution remote sensing images. Two images of Kunming city (subject A & B) were obtained from Quick Bird image gallery, pre-processed by co-occurrence matrix, and then multi-scale segmented based on inherent geometrical and geographical attributes. Water encompassed by the ring roads of the city was extracted via object-oriented information analysis with successfully removal of all shadows. Results showed that water extraction precisions had significantly increased for both subject A (68.6% → 95.2%) and B (63.0% → 92.3%), indicating superior performance of the proposed method in extracting water from complex urban environment.
ISSN:2161-0258
DOI:10.1109/GEOINFORMATICS.2018.8557177