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The persistence scatter Interferometric Synthetic Aperture Radar (PS-InSAR) analysis method is a technique that utilizes the persistent scatter in synthetic aperture radar images and image analysis is used via the interference of 25 or more slave images in a master image. The accuracy of the method is determined by the similarity between images, where the higher the coherence, the more accurate the image. Minimum spanning tree (MST) algorithms are used to find the optimal coherence by considering the temporal and spatial coherence of each image, instead of using the more common star graph, which interferes with the slave images in a single master image. When MST algorithms are employed considering the high coherence between images, pairs with high coherence can be connected; however, a higher number of images requires a higher processing speed. In this study, MST algorithms were therefore used by only considering the basic information in an image such as the spatial and temporal baselines, without the need for image processing. To verify the MST algorithms, a three-dimensional regression analysis was conducted, considering the correlation between the spatial and temporal baselines and the coherence. Furthermore, a novel MST algorithm was performed, which considered the weights derived from the preceding analysis. The results showed a high coherence of 98.5%, which was achieved rapidly, and a processing speed increase of approximately 120% over the results attained using the star graph. This study could be of great help in performing SAR image processing, which is part of remote sensing.

Optimal Minimum Spanning Tree Algorithm for Improving Coherence of Persistence Scatter-Interferometric Synthetic Aperture Radar Technique