Landslide Displacement Monitoring by a Fully Polarimetric SAR Offset Tracking Method

Changcheng Wang, Xiaokang Mao, Qijie Wang
2016 Remote Sensing  
Landslide monitoring is important for geological disaster prevention, where Synthetic Aperture Radar (SAR) images have been widely used. Compared with the Interferometric SAR (InSAR) technique, intensity-based offset tracking methods (e.g., Normalized Cross-Correlation method) can overcome the limitation of InSAR's maximum detectable displacement. The normalized cross-correlation (NCC) method, based on single-channel SAR images, estimates azimuth and range displacement by using statistical
more » ... ng statistical correlation between the matching windows of two SAR images. However, the matching windows-especially for the boundary area of landslide-always contain pixels with different moving characteristics, affecting the precision of displacement estimation. Based on the advantages of polarimetric scattering properties, this paper proposes a fully polarimetric SAR (PolSAR) offset tracking method for improvement of the precision of landslide displacement estimation. The proposed method uses the normalized inner product (NIP) of the two temporal PolSAR Pauli scattering vectors to evaluate their similarity, then retrieve the surface displacement of the Slumgullion landslide located in southwestern Colorado, USA. A pair of L-band fully polarimetric SAR images acquired by the Jet Propulsion Laboratory's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system are selected for experiment. The results show that the Slumgullion landslide's moving velocity during the monitoring time ranges between 1.6-10.9 mm/d, with an average velocity of 6.3 mm/d. Compared with the classical NCC method, results of the proposed method present better performance in the sub-pixel estimation. Furthermore, it performs better when estimating displacement in the area around the landslide boundaries. Differential SAR interferometry (D-InSAR) is one of the two main approaches used to estimate the landslide displacement using SAR data. It uses SAR images' phase difference information, and has been widely used in land deformation monitoring [8] [9] [10] [11] [12] [13] . Furthermore, as polarimetric information can help to improve the coherence, some researchers have combined polarimetric SAR (PolSAR) data with D-InSAR to improve the results [14] [15] [16] . Although the D-InSAR technique has been successfully applied in retrieving highly accurate landslide displacement in some case studies [17] , it also has some problems. One issue is that it only measures the landslide displacement in the line-of-sight (LOS) direction [18, 19] . Therefore, its result has neither the deformation along the azimuth direction, nor the displacement information when the moving direction of the landslide is perpendicular to the LOS direction. Second, the D-InSAR method cannot work when the landslide displacement exceeds the maximum detectable displacement. Last but not least, its successful application is often limited by de-correlation effects (phase noise). The other method is the offset tracking method, which uses SAR images' intensity information to estimate the deformation along the azimuth and range direction [20-23]. As SAR intensity information is more stable than phase information, it is easier to compute landslide displacement in both the azimuth and range direction by intensity-based offset tracking methods [24] . The precision of this method is related to the spatial resolution of SAR images, which is lower than that of the D-InSAR method [19] . However, with the new generation of high resolution sensors (e.g., TerraSAR-X, Radarsat-2, and high resolution airborne SAR systems), the precision of this method has been improved [25]. The normalized cross-correlation (NCC) offset tracking is a classical intensity-based displacement estimation approach [26] which has been widely used for glacier velocity estimation [23], earthquake displacement [27] [28] [29] [30] , landslide monitoring, and so on. In addition, as polarimetric SAR images provide more valuable information than that of single channel SAR images, they may help to improve the precision of intensity-based deformation estimation. However, few studies have taken into account the polarimetric information for landslide displacement estimation. Most studies use single-channel SAR intensity images to estimate landslide deformation. Therefore, we attempt to use PolSAR images to improve the accuracy of the intensity-based deformation estimation. Erten, et al. [31, 32] proposed two PolSAR tracking methods for glacier velocity estimation, one based on the mutual information of temporal polarimetric covariance matrices and the other based on maximum likelihood estimation. These two methods improved the precision of the offset tracking method compared with single SAR. These methods measure the second-order statistical dependence between two temporal polarimetric covariance matrices. In this paper, taking the advantages of polarimetric scattering properties, we propose a new polarimetric SAR tracking method to improve the accuracy of landslide displacement estimation. The rest of the paper is arranged as follows. Section 2 simply introduces the classical NCC method and presents the principle of the proposed polarimetric SAR offset tracking method for landslide monitoring. Section 3 illustrates the experimental results. Then, we make a performance analysis of the proposed method in Section 4. Conclusions are drawn in Section 5. Methodology The Classical Normalized Cross-Correlation Tracking Algorithm The normalized cross-correlation (NCC) tracking method is a classical intensity-based matching method, which finds the best match by maximizing a similarity measurement of candidate local blocks of two images. Then, the local offset of each pixel is determined by the peak value of the corresponding candidate correlation plane [33] . For two SAR images, I M and I S , the correlation (ρ) between two image templates is calculated with Equation (1).
doi:10.3390/rs8080624 fatcat:denxw7gctvhahf5sjmd3q65cqu