Abstract
The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by Rj test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.
Highlights
The successful launch of China’s first multi-polarization synthetic aperture radar (SAR) imaging satellite (Gaofen-3, GF3) on 10 August 2016 [1], has greatly promoted research on PolSAR in China [2].The GF3 data possess the advantages of the traditional SAR images, such as being immune to the influence of weather and illumination, but they feature a variety of polarization imaging modes, allowing us to obtain more information on the scattering of objects and achieve improved object interpretation [3]
By comparing some filter methods of software and NEST software and the Root-Mean-Square Errors (RMSE) of co-registration were less decreasing the influence of speckle noise, we found that the Lee Sigma filter has the better balance in than one pixel in this study
To overcome the limitations of the existing unsupervised change detection methods, an unsupervised change detection method using time-series of PolSAR images was proposed in this paper, which integrates advantages of the omnibus test statistic, Generalized Statistical Region Merging (GSRM), and Generalized Gaussian Mixture Model (GGMM) techniques in this paper
Summary
The successful launch of China’s first multi-polarization synthetic aperture radar (SAR) imaging satellite (Gaofen-3, GF3) on 10 August 2016 [1], has greatly promoted research on PolSAR in China [2].The GF3 data possess the advantages of the traditional SAR images, such as being immune to the influence of weather and illumination, but they feature a variety of polarization imaging modes, allowing us to obtain more information on the scattering of objects and achieve improved object interpretation [3]. With the development of PolSAR satellites, a large number of time-series PolSAR images are available from different sensors (such as ENVISAT-ASAR, ALOS-PALSAR, TerraSAR-X, Radarsat-2), which can better reflect the dynamic changes of the Earth’s surface. These images have been used in a wide range of applications in the fields of disaster prevention and mitigation [4,5,6], agriculture monitoring [7], forestry [8], land-cover change [9,10] and weather forecasting [11]. Some researchers have compared pair-wise images in the time-series images and detected
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