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Open AccessFeature PaperArticle

Performance of Change Detection Algorithms Using Heterogeneous Images and Extended Multi-attribute Profiles (EMAPs)

1
Applied Research LLC, Rockville, MD 20850, USA
2
Department of Computer Architecture and Automation, Complutense University of Madrid, 28040 Madrid, Spain
3
Department of Technology of Computers and Communications, University of Extremadura, 10003 Cáceres, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(20), 2377; https://doi.org/10.3390/rs11202377
Received: 3 September 2019 / Revised: 9 October 2019 / Accepted: 10 October 2019 / Published: 14 October 2019
We present detection performance of ten change detection algorithms with and without the use of Extended Multi-Attribute Profiles (EMAPs). Heterogeneous image pairs (also known as multimodal image pairs), which are acquired by different imagers, are used as the pre-event and post-event images in the investigations. The objective of this work is to examine if the use of EMAP, which generates synthetic bands, can improve the detection performances of these change detection algorithms. Extensive experiments using five heterogeneous image pairs and ten change detection algorithms were carried out. It was observed that in 34 out of 50 cases, change detection performance was improved with EMAP. A consistent detection performance boost in all five datasets was observed with EMAP for Homogeneous Pixel Transformation (HPT), Chronochrome (CC), and Covariance Equalization (CE) change detection algorithms. View Full-Text
Keywords: change detection; heterogeneous data; EMAP; multi-modal images change detection; heterogeneous data; EMAP; multi-modal images
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MDPI and ACS Style

Kwan, C.; Ayhan, B.; Larkin, J.; Kwan, L.; Bernabé, S.; Plaza, A. Performance of Change Detection Algorithms Using Heterogeneous Images and Extended Multi-attribute Profiles (EMAPs). Remote Sens. 2019, 11, 2377.

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