While performing the photo interpretation task using very high resolution images, the resolution of the image is often reduced to make its processing feasible. However, in low resolution images, it becomes quite difficult to segment and locate targets of interest such as aircraft, which are relatively small. Further, in recognizing aircraft, it is generally assumed that aircraft are already located. The emphasis is placed on model matching for recognizing isolated aircraft. However, locating potential areas in the images, where aircraft may be found, is non-trivial since it requires an accurate labeling of an image. We have developed a Knowledge-Based Photo Interpretation (KEPI) system that analyzes high resolution images. This system locates aircraft by first finding large structures in low resolution images and focusing attention on areas such as tarmacs, runways, parking areas, that have high probability of containing aircraft. Higher resolution images of the regions that are the focus of attention are used in subsequent analysis. The system makes extensive use of contextual knowledge such as spatial and locational information about airport scenes. We show results using high resolution TV data.