1 November 1991 Development of an electronic terrain board as a processor test and evaluation tool
Author Affiliations +
Proceedings Volume 10307, Automatic Object Recognition; 103070H (1991) https://doi.org/10.1117/12.2283658
Event: SPIE Institutes for Advanced Optical Technologies 7, 1991, Bellingham, Washington, United States
Current development, training, and testing of automatic target recognizers/cuers relies almost exclusively on image data taken at field sites or from physical terrain boards. Each of these approaches has several advantages as well as disadvantages. For example, field test data are severely limited in the variety of terrain and targets typically available. In addition, the environment is too variable to support parametric testing of processors. On the other hand, the targets and their signatures are real as is atmospheric attenuation, sensor settings, sensor artifacts, etc. In contrast, the physical terrain board is highly controllable and is ideally suited for parametric studies of processors. However, the physical terrain boards are simulations of targets and backgrounds and typically do not include the important contributions of sensor-specific noise or atmospheric attenuation on target signatures. More importantly, physical terrain boards have not yet incorporated a method for multi-sensor testing. This paper will describe in detail the advantages and disadvantages of field and physical terrain board testing and will present the concept of a digital terrain board that addresses many of the limitations of previous approaches while not sacrificing their advantages. Specific approaches will be discussed and preliminary results of testing processors with several gradations of synthetic imagery will be presented.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Clarence P. Walters, Clarence P. Walters, } "Development of an electronic terrain board as a processor test and evaluation tool", Proc. SPIE 10307, Automatic Object Recognition, 103070H (1 November 1991); doi: 10.1117/12.2283658; https://doi.org/10.1117/12.2283658

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