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Sustainability 2014, 6(8), 5300-5310; https://doi.org/10.3390/su6085300

Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation

College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
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Received: 29 May 2014 / Revised: 25 July 2014 / Accepted: 28 July 2014 / Published: 14 August 2014
(This article belongs to the Special Issue Borderland Studies and Sustainability)
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Abstract

The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of a remotely sensed image based on this principle. The target image was divided into regions based on object-oriented multiresolution segmentation and edge-detection. Furthermore, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information) were extracted by the spectral and geometrical features. The results indicate that the edge-detection has a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% by the confusion matrix. View Full-Text
Keywords: edge-detection; object-oriented; multiresolution segmentation; spectral features; geometrical features; confusion matrix edge-detection; object-oriented; multiresolution segmentation; spectral features; geometrical features; confusion matrix
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Shao, P.; Yang, G.; Niu, X.; Zhang, X.; Zhan, F.; Tang, T. Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation. Sustainability 2014, 6, 5300-5310.

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