Automatic Subsidence Troughs Detection in SAR Interferograms Using Circlet Transform

Abstract

This article presents the results of automatic detection of subsidence troughs in synthetic aperture radar (SAR) interferograms. The detection of subsidence troughs is based on the circlet transform, which is able to detect features with circular shapes. Compared to other methods of detecting circles, the circular transform takes into account the finite data frequency. Moreover, the search shape is not limited to a circle but identified on the basis of a certain width. This is especially important in the case of detection of subsidence troughs whose shapes may not be similar to circles or ellipses but to their fragments. The transformation works directly on the image gradient; it does not require further binary segmentation or edge detection as in the case of other methods, e.g., the Hough transform. The entire processing process can be automated to save time and increase reliability compared to traditional methods. The proposed automatic detection method was tested on a differential interferogram that was generated based on Sentinel-1A SAR images of the Upper Silesian Coal Basin area. The test carried out showed that the proposed method is 20% more effective in detecting troughs that than the method using Hough transform.

A Lightweight Anchor-Free Subsidence Basin Detection Model With Adaptive Sample Assignment in Interferometric Synthetic Aperture Radar Interferogram

“…This method is limited by the feature detection operator and cannot effectively detect the subsidence basin with obscure edge features and too small scope. Bala et al (2021) proposed a circlet transform method for detecting subsidence basins. This method reduces manual intervention, but the detection time is longer.…”

Detecting, Monitoring, and Analyzing the Surface Subsidence in the Yellow River Delta (China) Combined with CenterNet Network and SBAS-InSAR

“…Wang et al [23] used the histogram of oriented gradients and a support vector machine model in machine learning to extract the subsidence basins of the mining area from the wide differential interferogram, and the accuracy rate reached 85%. Bata et al [24] proposed the automatic subsidence area detection in SAR interferograms by the method of circlet transform and tested it on the Upper Silesian Coal Basin located in Southern Poland, and the detection efficiency of this method was improved by 20% compared with the Hough transform. However, traditional mathematical and machine learning methods are easily limited by the amount of data and need to add features manually [25].…”

Automatic Detection of Subsidence Troughs in SAR Interferograms Using Mathematical Morphology

“…Its detection rate varied from 30% to 53% with a relatively low number of false alarms. In [11], circlet transform was compared with Hough transform [12,13]. There are also works that are based on a convolutional neural network [14,15].…”

“…Each of those methods has its limitations, which is why their efficiency in trough detection in noisy satellite images is low. Methods [10][11][12][13] use circular or elliptical troughs in the detection process. In many cases, the shape of the troughs on interferograms is deformed, which significantly reduces the effectiveness of these solutions.…”

“…Moreover, the proposed method is much less computationally expensive, which is quite important due to the large size of the processed SAR images. However, most of the solutions [10][11][12][13][14][15] can be used to support the detection of subsidence in larger SAR images, but they are not suitable for fully automated operations.…”