Co-Registration of Multi-Temporal Dem Based on Sift Algorithm for Change Detection of Glaciers

Abstract

ABSTRACT:To detect the change of geographic objects by using multi-temporal DEM, the data must be co-registered firstly. In this paper, the Scale-Invariant Feature Transform (SIFT) algorithm is used to co-register multi-temporal DEM data and glacier change detection.Firstly, the DEM is converted into image space and extracts feature information, calculate multiple sets of match point coordinates, and achieve swift and accurate DEM data co-registration using SIFT algorithm. Secondly, the difference between co-registered DEM datasets are analysed. Total area change and average rate of change are calculated. Finally, the change of multi-temporal DEM data of glaciers in Langkazi County, Tibet from 2004 to 2014 is detected using the method proposed in this paper. The results indicate that the proposed method is able to detect change of the glaciers and the overall accuracy is higher than 85%.

Measurement of Soil Tillage Using UAV High-Resolution 3D Data

“…The result of differential DTMs never reflects the actual scenario of changes if the issues mentioned above have not been controlled. Several methods have been used to ensure (or improve) the quality of mapping relative changes or volume changes based on correction of relative position between multi-temporal 3D data at the point cloud or DTM levels, such as (1) co-registering point clouds using stable areas, such as roads (using the CloudCompare software) before the generation of the two DSMs [22]; (2) using SIFT key points to detect and match functions (in OpenCV) for the co-registration of raster DSMs [59]; (3) aligning dense point clouds by fitting a reference plane using CloudCompare software [31]; and (4) estimating a mean depth value for differential DTMs, applied for the estimation of a landslide volume [60] in areas of difficult and restricted access.…”