Chul-Soo Ye

Digital elevation model combination using triangular image warping interpolation and maximum likelihood

The main purpose of this letter was to construct a large-region Digital Elevation Model (DEM) from small-area DEMs. Image mosaicking and data fusion techniques are necessary in constructing a large-region DEM from small-area DEMs. The topographic distortion of the image must be corrected because of viewing angle and time differences among the acquired images. The orthorectification and image mosaicking are executed using a grid matrix of the longitude and latitude obtained from DEM extraction at 10 m intervals. The height at each grid location in the overlapping region is determined using a triangular image warping interpolation and a maximum likelihood procedure. The images used in the experiments are two pairs of images from the SPOT High Resolution Visible (HRV) sensor. The results show that a large-region DEM can be obtained from small-area DEMs.

Stereo Matching Method using Directional Feature Vector

In this paper we proposed multi-directional matching windows combined by multi-dimensional feature vector matching, which uses not only intensity values but also multiple feature values, such as variance, first and second derivative of pixels. Multi-dimensional feature vector matching has the advantage of compensating the drawbacks of area-based stereo matching using one feature value, such as intensity. We define matching cost of a pixel by the minimum value among eight multi-dimensional feature vector distances of the pixels expanded in eight directions having the interval of 45 degrees. As best stereo matches, we determine the two points with the minimum matching cost within the disparity range. In the experiment we used aerial imagery and IKONOS satellite imagery and obtained more accurate matching results than that of conventional matching method.