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ECWAY TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE
BANGALORE / HYDRABAD
CELL: +91 98949 17187, +91 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 / 8111
VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com

INTERACTIVE SEGMENTATION FOR CHANGE DETECTION IN
MULTISPECTRAL REMOTE-SENSING IMAGES

ABSTRACT:

We propose to solve the change detection (CD) problem in multitemporal remote-sensing images
using interactive segmentation methods. The user needs to input markers related to change and
no-change classes in the difference image. Then, the pixels under these markers are used by the
support vector machine classifier to generate a spectral-change map. To enhance further the
result, we include the spatial contextual information in the decision process using two different
solutions based on Markov random field and level-set methods. While the former is a regiondriven method, the latter exploits both region and contour for performing the segmentation task.
Experiments conducted on a set of four real remote-sensing images acquired by low as well as
very high spatial resolution sensors and referring to different kinds of changes confirm the
attractive capabilities of the proposed methods in generating accurate CD maps with simple and
minimal interaction.

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Interactive segmentation for change detection in multispectral remote sensing images

  • 1. ECWAY TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE BANGALORE / HYDRABAD CELL: +91 98949 17187, +91 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 / 8111 VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com INTERACTIVE SEGMENTATION FOR CHANGE DETECTION IN MULTISPECTRAL REMOTE-SENSING IMAGES ABSTRACT: We propose to solve the change detection (CD) problem in multitemporal remote-sensing images using interactive segmentation methods. The user needs to input markers related to change and no-change classes in the difference image. Then, the pixels under these markers are used by the support vector machine classifier to generate a spectral-change map. To enhance further the result, we include the spatial contextual information in the decision process using two different solutions based on Markov random field and level-set methods. While the former is a regiondriven method, the latter exploits both region and contour for performing the segmentation task. Experiments conducted on a set of four real remote-sensing images acquired by low as well as very high spatial resolution sensors and referring to different kinds of changes confirm the attractive capabilities of the proposed methods in generating accurate CD maps with simple and minimal interaction.