Citation Author(s):
Submitted by:
Youm Minkyo
Last updated:
Thu, 03/07/2019 - 04:14
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The PS-InSAR analysis method is a technique that utilizes persistent scatter in SAR images and performs image analysis by interfering with 25 or more slave images in a master image. Determining the accuracy of the above algorithm is the denser between images, the higher the coherence, the more accurate the image is. Therefore, the Minimum Spanning Tree (MST) algorithm is used to find the optimum coherence by considering the temporal, spatial, and coherence of each image rather than Star graph, which interferes with the rest of the slave images in one master image. However if MST algorithms are carried out considering the high coherence between images, the high coherence interference pairs can be connected, but the higher the number and size of images, the higher the processing speed. In this study, MST algorithms were carried out in consideration of the basic information of images, spatial baseline, and temporal baseline, without any imaging processing. In order to verify this, a three-dimensional regression analysis was conducted, taking into account the correlation of spatial baseline, temporal baseline, and coherence. Also, a new MST algorithm was performed taking into account the weights derived from the above analysis. The results showed that a high coherence of 98.5% over the previous analysis was achieved in a short time and an increase of about 120% over the Star graph.


One of the InSAR techniques, PS-InSAR, was developed by Ferretti and others at the POLIME Institute in Italy in the late 1990s. That is performed by using a high-density persistent scatterer with superior reflectivity of the surface [1]. The average coherence depends on which interference pair graphs are adopted in carrying out the PS-InSAR technique. Three algorithms exist to construct interference pairs, including Star graphs, small baselines, and minimum spanning trees. In the existing PS-InSAR technique, a Star graph was used to select the master image of the graph and link the slave images. Star graph assures the temporal continuity of the deformation measurements and thus the possibility of phase unwrapping. However, there is a disadvantage that the perpendicular baseline between master image and slave images affects temporal, spatial decorrelation. After this, a Small BAseline Subset (SBAS) algorithm was developed to reduce geometric errors and temporal inconsistencies. However, the SBAS algorithm may not use some of the available images and there are concerns of coherence degradation as image graphs are frequently disconnected [2]. Finally, a minimum spanning tree graph was developed, an optimal framework designed to select the optimal cross-pair considering both spatial and temporal decorrelation [3],[4]. 


The implementation of PS-InSAR technique using Star graphs and MST graphs includes China Tianjin surface displacement monitoring [3], and InSAR analysis with low precision [4]. Examples of PS-InSAR performed using MST algorithms include surface deformation measurement in QINGHAI-TIBETAN PLATEAU region[5], surface displacement monitoring [6], L Zhang's ground deformation mapping [7] and landslide displacement measurement in LAO CAI in China [9]. In this paper, we use the basic information provided in the images, such as spatial and temporal baseline. MST graph is derived without the imaging process to obtain coherence.

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