Next Article in Journal
Report of the 2017 IEEE Cyber Science and Technology Congress
Previous Article in Journal
Imaging Flow Velocimetry with Laser Mie Scattering
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(12), 1297; https://doi.org/10.3390/app7121297

An Unsupervised Method of Change Detection in Multi-Temporal PolSAR Data Using a Test Statistic and an Improved K&I Algorithm

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275, USA
*
Author to whom correspondence should be addressed.
Received: 30 October 2017 / Revised: 1 December 2017 / Accepted: 8 December 2017 / Published: 13 December 2017
Full-Text   |   PDF [12290 KB, uploaded 13 December 2017]   |  

Abstract

In recent years, multi-temporal imagery from spaceborne sensors has provided a fast and practical means for surveying and assessing changes in terrain surfaces. Owing to the all-weather imaging capability, polarimetric synthetic aperture radar (PolSAR) has become a key tool for change detection. Change detection methods include both unsupervised and supervised methods. Supervised change detection, which needs some human intervention, is generally ineffective and impractical. Due to this limitation, unsupervised methods are widely used in change detection. The traditional unsupervised methods only use a part of the polarization information, and the required thresholding algorithms are independent of the multi-temporal data, which results in the change detection map being ineffective and inaccurate. To solve these problems, a novel method of change detection using a test statistic based on the likelihood ratio test and the improved Kittler and Illingworth (K&I) minimum-error thresholding algorithm is introduced in this paper. The test statistic is used to generate the comparison image (CI) of the multi-temporal PolSAR images, and improved K&I using a generalized Gaussian model simulates the distribution of the CI. As a result of these advantages, we can obtain the change detection map using an optimum threshold. The efficiency of the proposed method is demonstrated by the use of multi-temporal PolSAR images acquired by RADARSAT-2 over Wuhan, China. The experimental results show that the proposed method is effective and highly accurate. View Full-Text
Keywords: change detection; test statistic; improved Kittler and Illingworth (K&I); generalized Gaussian model (GGM); PolSAR; multi-temporal change detection; test statistic; improved Kittler and Illingworth (K&I); generalized Gaussian model (GGM); PolSAR; multi-temporal
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhao, J.; Yang, J.; Lu, Z.; Li, P.; Liu, W.; Yang, L. An Unsupervised Method of Change Detection in Multi-Temporal PolSAR Data Using a Test Statistic and an Improved K&I Algorithm. Appl. Sci. 2017, 7, 1297.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top