As discussed in the background of U.S. Pat. No. 6,654,690 to Rahmes et al. and assigned to the assignee of the present invention, topographical models of geographical areas may be used for many applications. For example, topographical models may be used in flight simulators and for planning military missions. Furthermore, topographical models of man-made structures (e.g., cities) may be extremely helpful in applications such as cellular antenna placement, urban planning, disaster preparedness and analysis, and mapping, for example.
Various types and methods for making topographical models are presently being used. One common topographical model is the digital elevation map (DEM). A DEM is a sampled matrix representation of a geographical area that may be generated in an automated fashion by a computer. In a DEM, co-ordinate points are made to correspond with a height value. DEMs are typically used for modeling terrain where the transitions between different elevations (e.g., valleys, mountains, etc.) are generally smooth from one to a next. That is, DEMs typically model terrain as a plurality of curved surfaces and any discontinuities therebetween are thus “smoothed” over. For this reason, DEMs generally are not well suited for modeling man-made structures, such as skyscrapers in a downtown area, with sufficient accuracy for many of the above applications.
Another approach to producing topographical models has been developed by the Harris Corporation, assignee of the present invention, and is commercially referred to as RealSite®. RealSite® provides a semi-automated process for making three-dimensional (3D) topographical models of geographical areas, including cities, that have accurate textures and structure boundaries. Moreover, RealSite® models are geospatially accurate. That is, the location of any given point within the model corresponds to an actual location in the geographical area with very high accuracy (e.g., within a few meters). The data used to generate RealSite® models may include aerial and satellite photography, electro-optical, infrared, and light detection and ranging (LIDAR).
RealSite® models not only provide enhanced accuracy over prior automated methods (such as automated DEM generation), but since they are produced using a semi-automated computer process they may be created much more rapidly than comparable manually rendered models. Yet, even though the RealSite® model generation process begins with actual data of a geographic location, some user delineation may be required to distinguish objects within an input data set before automated computer algorithms can render the final models. Thus, producing RealSite® models for large geometric areas of several kilometers, for example, may require a significant amount of time and labor.
Accordingly, U.S. Pat. No. 6,654,690 discloses a significant advance of an automated method for making a topographical model of an area including terrain and buildings thereon based upon randomly spaced data of elevation versus position. The method may include processing the randomly spaced data to generate gridded data conforming to a predetermined position grid, processing the gridded data to distinguish building data from terrain data, and performing polygon extraction to make the topographical model of the area including terrain and buildings thereon.
Change detection is an important part of many commercial Geographic Information Systems (GIS)-related applications. Moreover, given the recent explosion of available imagery data and the increasing number of areas-of-interest throughout the world, the trend is towards rapid, automated change detection algorithms. To make effective use of these imagery databases care should generally be taken that the newly collected imagery match the existing/reference imagery's characteristics such as coverage, field-of-view, color, and most notably, sensor location and viewpoint.
Unfortunately, this presents a difficulty since in many cases it is time-consuming, very difficult or even impossible to replicate the original collection scenario due to: sensor-scheduling (in the case of space-based), cost of re-flying the sensor (in the case of aerial-based), or that the sensor is no longer in use (both cases). Thus large amounts of collected imagery may go underutilized in regards to change detection.
The current state of the art in change detection involves either: (1) geo-registering two images (reference and new collect images) together so that the automated change detection algorithms will have a high rate of success, or (2) performing sophisticated pixel-correlation change detection algorithms that tend to be slow, iterative in nature, and manually intensive, since the algorithms often need to be tweaked between runs. The first case requires a high degree of correlation in the location and parameters of the sensor, or sensors, if they are different between the two collects. The second case does not require as high a degree of correlation although some is still needed, but it is neither automated nor fast. Neither approach is satisfactory.
An article by Walter entitled “Automated GIS Data Collection and Update,” pp. 267-280, 1999, examines data from different sensors regarding their potential for automatic change detection. Along these lines an article entitled “Automatic Change Detection of Urban Geospatial Databases Based on High Resolution Satellite Images Using AI Concepts” to Samadzadegan et al. discloses an automatic change detection approach for changes in topographic urban geospatial databases taking advantage of fusion of description and logical information represented on two levels. U.S. Pat. No. 6,904,159 discloses identifying moving objects in a video using volume growing and change detection masks. U.S. Pat. No. 6,243,483 discloses a mapping system for the integration and graphical display of pipeline information that enables automated pipeline surveillance.
Accordingly, although a growing body of geospatial scene model data exists, it has not yet been exploited in the area of automated change detection of sensor images.