Processing Framework for Landslide Detection Based on Synthetic Aperture Radar (SAR) Intensity-Image Analysis

Abstract

We present an object-based image analysis (OBIA) approach to identify temporal changes in radar-intensity images and to locate land-cover changes caused by mass-wasting processes at small to large scales, such as landslides. Our approach is based upon change detection in SAR intensity images that remain in their original imaging coordinate system rather than being georeferenced and map-projected, in order to reduce accumulation of filtering artifacts and other unwanted effects that would deteriorate the detection efficiency. Intensity images in their native slant-range coordinate frame allow for a consistent level of detection of land-cover changes. By analyzing intensity images, a much faster response can be achieved and images can be processed as soon as they are made publicly available. In this study, OBIA was introduced to systematically and semiautomatically detect landslides in image pairs with an overall accuracy of at least 60% when compared to in-situ landslide inventory data. In this process, the OBIA feature extraction component was supported by derived data from a polarimetric decomposition as well as by texture indices derived from the original image data. The results shown here indicate that most of the landslide events could be detected when compared to a closer visual inspection and to established inventories, and that the method could therefore be considered as a robust detection tool. Significant deviations are caused by the limited geometric resolution when compared to field data and by an additional detection of stream-related sediment redeposition in our approach. This overdetection, however, turns out to be potentially beneficial for assessing the risk situation after landslide events.

Landslide Susceptibility Prediction Based on High-Trust Non-Landslide Point Selection

“…Formula ( 8) can be transformed into Formula (9), where H is the output matrix of the hidden layer, β is the output weight, and T is the output result. Once the input weight w i and the paranoid vector b are randomly determined, the output matrix H is uniquely determined, and the output weight β can be determined.…”

“…Many scholars have researched landslide disasters, including susceptibility prediction, disaster risk assessment, landslide mechanism analysis, and detection [5][6][7][8][9]. Landslide susceptibility prediction comprehensively analyzes various geological and environmental factors, historical landslide data, and physical laws of landslides in the study area to identify the probability of future landslides in the study area [10].…”

Evaluation of Remote Mapping Techniques for Earthquake-Triggered Landslide Inventories in an Urban Subarctic Environment: A Case Study of the 2018 Anchorage, Alaska Earthquake

“…Pixel intensity was converted to the backscattering coefficient measured in decibel (dB) units that ranges from c. +10 dB for very bright objects to −40 dB for very dark surfaces. Differencing these data that are converted to decibel units is equivalent to the log-ratio method used in other studies (Mondini et al, 2019;Jung and Yun, 2020;Lin et al, 2021) to determine the change in amplitude between SAR scenes. These studies compute the log-ratio value as A ratio log 10 A pre A post (4) in which the A pre and A post values correspond to the radar brightness coefficient values (Mondini et al, 2019;Jung and Yun, 2020;Lin et al, 2021).…”

Performance Study of Landslide Detection Using Multi-Temporal SAR Images

“…Kinabalu earthquake in Malaysia [12], the 2018 Lombok earthquakes in Indonesia [10], and the 2018 Hokkaido Eastern Iburi earthquake in Japan [10,[13][14][15][16][17]. Examples of rainfall-triggered landslides include the 2009 Typhoon Morakot in Taiwan [18], the 2011 Typhoon Talas in Honshu, Japan [19], the 2015 heavy rain in Chin State, Myanmar [20], and the 2017 heavy rain in Kyushu, Japan [16].…”