Overview
- Discusses change detection methods for remote sensing applications
- Summarizes well-known methods in the literature
- Proposes novel methods to solve the problem
- Includes supplementary material: sn.pub/extras
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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About this book
Change detection using remotely sensed images has many applications, such as urban monitoring, land-cover change analysis, and disaster management. This work investigates two-dimensional change detection methods. The existing methods in the literature are grouped into four categories: pixel-based, transformation-based, texture analysis-based, and structure-based. In addition to testing existing methods, four new change detection methods are introduced: fuzzy logic-based, shadow detection-based, local feature-based, and bipartite graph matching-based. The latter two methods form the basis for a structural analysis of change detection. Three thresholding algorithms are compared, and their effects on the performance of change detection methods are measured. These tests on existing and novel change detection methods make use of a total of 35 panchromatic and multi-spectral Ikonos image sets. Quantitative test results and their interpretations are provided.
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Table of contents (8 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Two-Dimensional Change Detection Methods
Book Subtitle: Remote Sensing Applications
Authors: Murat İlsever, Cem Ünsalan
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-1-4471-4255-3
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Cem Ünsalan 2012
Softcover ISBN: 978-1-4471-4254-6Published: 24 June 2012
eBook ISBN: 978-1-4471-4255-3Published: 22 June 2012
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: X, 72
Number of Illustrations: 26 b/w illustrations, 22 illustrations in colour
Topics: Image Processing and Computer Vision, Pattern Recognition