Conventional singe baseline InSAR is easily affected by atmospheric artifacts, making it difficult to generate highprecision DEM. To solve this problem, in this paper, a multi-baseline interferometric phase accumulation method with weights fixed by coherence is proposed to generate higher accuracy DEM. The mountainous area in Kunming, Yunnan Province, China is selected as study area, which is characterized by cloudy weather, rugged terrain and dense vegetation. The multi-baseline InSAR experiments are carried out by use of four ALOS-2 PALSAR-2 images. The generated DEM is evaluated by Chinese Digital Products of Fundamental Geographic Information 1:50000 DEM. The results demonstrate that: 1) the proposed method can reduce atmospheric artifacts significantly; 2) the accuracy of InSAR DEM generated by six interferograms satisfies the standard of 1:50000 DEM Level Three and American DTED-1.
The uneven settlement of high-speed railway (HSR) brings about great threat to the safe operation of trains. Therefore, the subsidence monitoring and prediction of HSR has important significance. In this paper, an improved multitemporal InSAR method combing PS-InSAR and SBAS-InSAR, Multiple-master Coherent Target Small-Baseline InSAR (MCTSB-InSAR), is used to monitor the subsidence of partial section of the Beijing-Tianjin HSR (BTHSR) and the Beijing-Shanghai HSR (BSHSR) in Beijing area. Thirty-one TerraSAR-X images from June 2011 to December 2016 are processed with the MCTSB-InSAR, and the subsidence information of the region covering 56km*32km in Beijing is dug out. Moreover, the monitoring results is validated by the leveling measurements in this area, with the accuracy of 4.4 mm/year. On the basis of above work, we extract the subsidence information of partial section of BTHSR and BSHSR in the research area. Finally, we adopt the idea of timing analysis, and employ the back-propagation (BP) neural network to simulate the relationship between former settlement and current settlement. Training data sets and test data sets are constructed respectively based on the monitoring results. The experimental results show that the prediction model has good prediction accuracy and applicability.