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Proceedings Paper

Automated change feature extraction systems in remote sensing
Author(s): Xiaolong Dai
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Paper Abstract

To enhance the ability of remote sensing system to provide accurate, timely, and complete geospatial information at regional and/or global scale, an automated change detection system has been and will continue to be one of the important yet challenging problems in remote sensing. This research was designed to evaluate the requirements and develop the techniques for an automated change detection system at landscape level using various geospatial data including multisensor remotely sensed imagery and ancillary data layers. These techniques are included in three subsystems: automated computer image understanding, multisource data fusion, and database updating and visualization. This paper summarizes what has been achieved so far in this research. The experiments have been focusing on three major interrelated components. In the first component, the impact of misregistration on the accuracy of remotely sensed land cover change detection was quantitatively investigated under Landsat Thematic Mapper images. In the second components, a new feature-based approach to automated multitemporal and multisensor image registration was developed. Feature matching was done in both feature space and image space based on moment invariant distance and chain code correlation. The characteristic of this technique is that it combines moment invariant shape descriptors with chain code correlation to establish the correspondences between regions in two images. In the third component, the algorithms for an automated change detection system utilizing neural networks were developed and implemented. This work has implications on improving the efficiency and accuracy of the change feature extraction and quantification at all levels of applications ranging from local to global in scale.

Paper Details

Date Published: 1 October 1998
PDF: 11 pages
Proc. SPIE 3460, Applications of Digital Image Processing XXI, (1 October 1998); doi: 10.1117/12.323212
Show Author Affiliations
Xiaolong Dai, North Carolina State Univ. (United States)

Published in SPIE Proceedings Vol. 3460:
Applications of Digital Image Processing XXI
Andrew G. Tescher, Editor(s)

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