Feature Matching Stage Research Articles

Modifications in the SIFT operator for effective SAR image matching

With the increasing availability and rapidly improving the spatial resolution of synthetic aperture radar (SAR) images from the latest and future satellites like TerraSAR-X and TanDEM-X, their applicability in remote sensing applications is set to be paramount. Considering challenges in the field of point feature-based multisensor/multimodal SAR image matching/registration and advancements in the field of computer vision, we extend the applicability of the scale invariant feature transform (SIFT) operator for SAR images. In this article, we have analysed the feature detection, identification and matching steps of the original SIFT processing chain. We implement steps to counter the speckle influence, which deteriorates the SIFT operator performance for SAR images. In feature identification, we evaluate different local gradient estimating techniques and highlight the fact that giving up the SIFT's rotation invariance characteristic increases the potential number of matches when the multiple SAR images from different sensors have been acquired with the same geometrical acquisition parameters. In the feature matching stage, we propose to assist the standard SIFT matching scheme to utilise the SIFT operator capability for effective results in challenging SAR image matching scenarios. The results obtained for SAR images acquired by different sensors using different incidence angles and orbiting directions over both rural and semi urban land cover, highlight the SIFT operator's capability for point feature matching in SAR imagery.

EFFECTIVE PARAMETERS IN SEARCH SPACE REDUCTION USED IN A FAST EDGE-BASED STEREO MATCHING

Reduction of search region in stereo correspondence can increase performances of the matching process, in the context of execution time and accuracy. For an edge-based stereo matching, we establish relationships between the search space and the parameters like relative displacement of edges, disparity under consideration, image resolution, CCD (Charge-Coupled Device) dimension and focal length of the stereo system. Then, we propose a novel matching strategy for the edge-based stereo. Afterward, we develop a fast edge-based stereo algorithm with combination of the obtained matching strategy and a multiresolution technique using Haar wavelet. Considering the conventional multiresolution technique using Haar wavelet, the execution times of our proposed method are decreased between 26% to 47% in the feature matching stage. Moreover, the execution time of the overall algorithms (including feature extraction and feature matching) is decreased between 15% to 20%. Theoretical investigation and experimental results show that our algorithm has a very good performance; therefore this new algorithm is very suitable for fast edge-based stereo applications like stereo robot vision.