@article{allili_automatic_2005,

title = {Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach},

author = {M. S. Allili and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-28444481044&partnerID=40&md5=e5d147401a402b14084292a2eeb6a792},

doi = {10.1109/CRV.2005.14},

issn = {11951036 (ISSN)},

year  = {2005},

date = {2005-01-01},

booktitle = {Proceedings - 2nd Canadian Conference on Computer and Robot Vision, CRV 2005},

journal = {Geomatica},

volume = {59},

number = {3},

pages = {275–281},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In order to keep up-to-date geospatial data in topographic databases, automatic change detection and data updating is required. In the present paper, we investigate the automatic change detection of geospatial data by using level set active contours. We propose an approach that is based on region comparison between two multi-temporal datasets. Firstly, the regions are extracted from two co-registered images taken apart in time by using level set based active contours segmentation. Then, the change detection is performed by spatially comparing the resulting region segments from the two images. The approach is validated by experiments relating to the change detection of lake surfaces by using Landsat7 multi-spectral imagery.},

keywords = {Adaptive control systems, Adaptive segmentation, Color image processing, Computer Simulation, Image analysis, Image segmentation, Level sets, Mixture analysis, Polarity smoothing, Probability density function},

pubstate = {published},

tppubtype = {article}

}