Work

Improved Pixel Offset Tracking Method Based on Corner Point Variation in Large-Gradient Landslide Deformation Monitoring

Year: 2025

Type: article

Abstract: Aiming at the problems of feature matching difficulty and limited extension application in the existing pixel offset tracking method for large-gradient landslides, this paper proposes an improved pixel offset tracking method based on corner point variation. Taking the Jinshajiang Baige landslide as the research object, the method’s effectiveness is verified using sentinel data. Through a series of experiments, the results show that (1) the use of VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarisation information combined with the mean value calculation method can improve the accuracy and credibility of the circling of the landslide monitoring range, make up for the limitations of the single polarisation information, and capture the landslide range more comprehensively, which provides essential information for landslide monitoring. (2) The choice of scale factor has an essential influence on the results of corner detection, in which the best corner effect is obtained when the scale factor R is 2, which provides an essential reference basis for practical application. (3) By comparing traditional normalized and adaptive window cross-correlation methods with the proposed approach in calculating landslide offset distances, the proposed method shows superior matching accuracy and sliding direction estimation. (4) Analysis of pixels P1, P2, and P3 confirms the method’s high accuracy and reliability in landslide displacement assessment, demonstrating its advantage in tracking pixel offsets in large-gradient scenarios. Therefore, the proposed method offers an effective solution for large-gradient landslide monitoring, overcoming limitations of feature matching and limited applicability. It is expected to provide more reliable technical support for geological disaster management.

Source: Remote Sensing

DOI: 10.3390/rs17193292

Authors: Dingyi Zhou, Zhifang Zhao, Fei Zhao

Institution: Yunnan University

Language: English

Topic: Synthetic Aperture Radar (SAR) Applications and Techniques

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funders: National Natural Science Foundation of China, Yunnan Provincial Department of Education Science Research Fund Project