The Use of Surface Topography for the Identification of Discontinuous Displacements Due to Cracks

The determination of three components of displacements at material surfaces is possible using surface topography information of undeformed (reference) and deformed states. The height digital image correlation (hDIC) technique was developed and demonstrated to achieve micro-level in-plane resolution and nanoscale out-of-plane precision. However, in the original formulation hDIC and other topography-based correlation techniques perform well in the determination of continuous displacements. In the present study of material deformation up to cracking and filan failure, the ability to identify discontinuous triaxial displacements at emerging discontinuities is important. For this purpose, a new method reported herein was developed based on the hDIC technique. The hDIC solution procedure comprises two stages, namely, integer-pixel level correlation and sub-pixel level correlation. In order to predict the displacement and height changes in discontinuous regions, a smoothing stage was inserted between the two main stages. The proposed method determines accurately the discontinuous edges, and the out-of-plane displacements become sharply resolved without any further intervention in the algorithm function. High computational demand required to determine discontinuous displacements using high density topography data was tackled by employing the graphics processing unit (GPU) parallel computing capability with the paging approach. The hDIC technique with GPU parallel computing implementation was applied for the identification of discontinuous edges in an aluminium alloy dog bone test specimen subjected to tensile testing up to failure.

BLOCK ADJUSTMENT OF LARGE-SCALE HIGH-RESOLUTION OPTICAL SATELLITE IMAGERY WITHOUT GCPS BASED ON THE GPU

<p><strong>Abstract.</strong> Precise geo-positioning of high-resolution optical satellite imagery without ground control points (GCPs) has always been the goal pursued by photogrammetry scholars. This paper introduces the block adjustment (BA) without GCPs based on rational function model (RFM) model and its practical application in high-precision geo-positioning of optical satellite imagery. The mainly key technologies of BA model construction based on virtual control points (VCPs), gross error detection and elimination, and GPU parallel computing method of large-scale adjustment are studied. On this basis, experimental analysis and validation of 123 images of ZY-3 satellite in Taihu are carried out. The results show that the sparse matrix compression can reduce the memory requirement effectively. The GPU parallel computing can solve the problem of large-scale BA computational efficiency. In addition, after BA, the maximum residual is 3.79 pixel, the root mean square error (RMSE) is 0.37 pixel in the <i>x</i> (flight) direction, the maximum residual error is 7.18 pixel, and the RMSE is 0.66 pixel in the <i>y</i> (scan) direction. The proposed method has certain accuracy and stability in large-scale BA without GCPs. The relative positioning accuracy can reach sub-pixel level, which could meet the requirements of cartographic mosaicking.</p>