Next Article in Journal
Fast Split Bregman Based Deconvolution Algorithm for Airborne Radar Imaging
Next Article in Special Issue
Monitoring Land Surface Deformation Associated with Gold Artisanal Mining in the Zaruma City (Ecuador)
Previous Article in Journal
Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in the Water Column of Freshwater Lakes
Previous Article in Special Issue
Change Analysis in Urban Areas Based on Statistical Features and Temporal Clustering Using TerraSAR-X Time-Series Images
Article

A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images

1
Department of Civil Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj 6617715175, Iran
2
Centre Eau Terre Environnement, Institute National de la Recherche Scientifique, Quebec, QC G1K 9A9, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1746; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111746
Received: 16 April 2020 / Revised: 20 May 2020 / Accepted: 22 May 2020 / Published: 29 May 2020
(This article belongs to the Special Issue Multi-temporal Synthetic Aperture Radar)
In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was developed for accurately detecting changed regions from the difference image. The proposed model extracts the changed areas as a target feature from the difference image based on training data from changed and unchanged regions. In this research, we used the Otsu histogram thresholding method to produce the training data automatically. In addition, the training data were updated in the process of minimizing the energy function of the model. To evaluate the accuracy of the model, we applied the proposed method to three benchmark SAR data sets. The proposed model obtains 84.65%, 87.07%, and 96.26% of the Kappa coefficient for Yellow River Estuary, Bern, and Ottawa sample data sets, respectively. These results demonstrated the effectiveness of the proposed approach compared to other methods. Another advantage of the proposed model is its high speed in comparison to the conventional methods. View Full-Text
Keywords: SAR images; multitemporal; environmental change detection; active contours model SAR images; multitemporal; environmental change detection; active contours model
Show Figures

Graphical abstract

MDPI and ACS Style

Ahmadi, S.; Homayouni, S. A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images. Remote Sens. 2020, 12, 1746. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111746

AMA Style

Ahmadi S, Homayouni S. A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images. Remote Sensing. 2020; 12(11):1746. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111746

Chicago/Turabian Style

Ahmadi, Salman, and Saeid Homayouni. 2020. "A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images" Remote Sensing 12, no. 11: 1746. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111746

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop