In urban color aerial images, shadows cast by cultural features may cause false color tone, loss of feature information, shape distortion of objects, and failure of conjugate image matching within the shadow area. This paper presents an automatic property-based approach for the detection and compensation of shadow regions with shape information preserved in complex urban color aerial images for solving problems caused by cast shadows in digital image mapping. The technique is applied in several invariant color spaces that decouple luminance and chromaticity, including HSI, HSV, HCV, YIQ, and YCbC, models. Experimental results from de-shadowing color aerial images of a complex building and a highway segment in these color models are evaluated in terms of visual comparisons and shadow detection accuracy assessments. The results show the effectiveness of the proposed approach in revealing details under shadows and the suitability of these color models in de-shadowing urban color aerial images

The Delaunay triangulation is commonly used to generate triangulared irregular network (TIN) models for a best description of the surface morphology in a variety of applications in geographic information systems (GIs). This paper discusses the definitions and basic properties of the standard and constrained Delaunay triangulations. Several existing Delaunay algorithms are reviewed and classified into three categories according to their procedures: ( I ) divide-and-conquer methods, (2) incremental insertion methods, and (3) triangulation growth methods. Furthermore, a linear-time Convex Hull Insertion algorithm is presented to construct T l N s for a set of points as well as specific features such as constraint breaklines and exclusion boundaries. Empirical results over various sets of up to 50000 points on personal computers show that the proposed algorithm efficiently expedites the construction of TIN models in approximately O ( N ) for N randomly distributed points.

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This paper develops automatic procedures for shadow detection and radiometric restoration of features under cast shadows in aerial images. Techniques including recursive quadtree image partition, adaptive thresholding, region growing segmentation, mathematical morphology, and local histogram matching are applied to automatically extract the regions of building-cast shadows and to restore the brightness of those pixels within the shadow regions. Empirical results show that the detail information of features within the shadow regions is enhanced by applying the techniques developed in this paper.

ABSTRACT:Location-based services (LBS) on web-based maps and images have come into real-time since Google launched its Street View imaging services in 2007. This research employs Google Maps API and Web Service, GAE for JAVA, AJAX, Proj4js, CSS and HTML in developing an internet platform for accessing the orientation parameters of Google Street View (GSV) panoramas in order to determine the three dimensional position of interest features that appear on two overlapping panoramas by geometric intersection. A pair of GSV panoramas was examined using known points located on the Library Building of National Chung Hsing University (NCHU) with the root-mean-squared errors of ±0.522m, ±1.230m, and ±5.779m for intersection and ±0.142m, ±1.558m, and ±5.733m for resection in X, Y, and h (elevation), respectively. Potential error sources in GSV positioning were analyzed and illustrated that the errors in Google provided GSV positional parameters dominate the errors in geometric intersection. The developed system is suitable for data collection in establishing LBS applications integrated with Google Maps and Google Earth in traffic sign and infrastructure inventory by adding automatic extraction and matching techniques for points of interest (POI) from GSV panoramas.