Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 609-616, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/609/2016/
doi:10.5194/isprs-archives-XLI-B1-609-2016
 
03 Jun 2016
AUTOMATIC FEATURE DETECTION, DESCRIPTION AND MATCHING FROM MOBILE LASER SCANNING DATA AND AERIAL IMAGERY
Zille Hussnain, Sander Oude Elberink, and George Vosselman ITC, Faculty of Geo-Information Science and Earth Observation of the University of Twente, Enschede, The Netherlands
Keywords: Feature detection, description, matching, mobile laser scanning, point cloud, aerial imagery Abstract. In mobile laser scanning systems, the platform’s position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.
Conference paper (PDF, 3496 KB)


Citation: Hussnain, Z., Oude Elberink, S., and Vosselman, G.: AUTOMATIC FEATURE DETECTION, DESCRIPTION AND MATCHING FROM MOBILE LASER SCANNING DATA AND AERIAL IMAGERY, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 609-616, doi:10.5194/isprs-archives-XLI-B1-609-2016, 2016.

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