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.
Highlights
Change detection is the process of identifying the differences on the Earth’s surface by multi-temporal images acquired in the same geographical area at different times [1,2]
Because of the advantage of repetitive data acquisition, both satellite images and aerial photographs obtained by optical and synthetic aperture radar (SAR) sensors have been widely used in land-cover change detection
Optical images have been widely applied in change detection [2,3,7], night-time and severe weather limit the use of optical images in practice
Summary
Change detection is the process of identifying the differences on the Earth’s surface by multi-temporal images acquired in the same geographical area at different times [1,2]. Because of the advantage of repetitive data acquisition, both satellite images and aerial photographs obtained by optical and synthetic aperture radar (SAR) sensors have been widely used in land-cover change detection. Optical images have been widely applied in change detection [2,3,7], night-time and severe weather limit the use of optical images in practice. Because SAR is an active microwave sensor with all-weather, day-and-night operational imaging capability [8], the use of SAR sensors instead of optical sensors is attractive in change detection studies [9].
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