Key Words Orthoimage, Back Projection, Digital Building Model.

Abstract
Orthographically rectified image maps are getting important due to its low cost and fast production. The major difficulty of generating orthoimage is the large displacement and hidden effects appeared in the original images when large-scale aerial photos are considered. We present here a new scheme to compensate the occluded information for an image from its counterpart image when digital building model and orientation parameters are available. Major components of the scheme include (1) bottom-up back projection (2) detection of hidden areas and determination of the weighing for stitching, and (3) gray value fill-in for hidden areas in conjunction with weighting average smoothing around the borders of hidden areas. Experimental results indicate that the proposed scheme may achieve accuracy better than one pixel.

1. Introduction
While the GIS applications are still booming, orthographically rectified image maps are getting important due to its low cost and fast production. There is a huge demand for more versatile and accurate map information. Digital orthoimages provide multi-scale, multi-temporal, and multi-spectral properties thus production of the images becomes a main trend in the field of GIS and photogrammetry [Hofmann, et al., 1984]. The major difficulty of generating orthoimage is the large displacement and hidden effects appeared in the original images when large-scale aerial photos are considered. The relief displacement of man-made building causes information loss and photo interpretation error.

Mayer [1999] and Shufelt ]1999] surveyed the state-of-the-art automatic building extraction techniques. They concluded that a fully automatic system is still a long way to go. While on the

Mayer [1999] and shufelt [1999] surveyed the state-of-the art automatic building extraction techniques. They concluded that a fully automatic system is still a long way to go. While on the other hand, the semi-automatic approaches are becoming important [Sahar & Krupnik, 1999]. It is not the purpose of this paper to emphasize the generation of digital building model (DBM). However, DBM is needed in the generation of orthoimage using large-scale aerial photography.

This paper presents a technique to use two (or more) images to generate the orthoimage by gray value compensation for the occlusion area. Provided that a raster-based DBM is available, we utilize the concept of Z-buffer to detect hidden area caused by man-made buildings. IN the processing of orthoimage generation for a master image, the gray values in the occluded area are filled-in from its counterpart image, i.e.,a slave image. In the mean time, a weighing average technique is performed to generate seamless mosaicking.

The flowchart of the proposed scheme is shown in figure 1. Major components of the scheme include (1) bottom-up back projection using DTM, as the dark blocks in figure 1, (2) detection of hidden areas using DBM and the determination of weighting for stitching, as the gray blocks in figure 1, and (3) gray value fill-in with smoothing around the hidden areas, as the remaining blocks in figure 1.


Figure 1. Flowchart of the proposed scheme
We describe the technique for detecting the hidden areas in the next section. In section 3, we show how to generate the orthoimage without false boundary effect. Accuracy analysis and the similarity assessment for validating the proposed scheme are included in section 4. Finally, concluding remarks are provided in the last section.