Automatic building identification using gps and machine learning

Video sensor capabilities and sophistication has improved to the point that they are being utilized in vast and diverse applications. Many such applications are now on the verge of providing too much video information reducing the ability to review, categorize, and process the immense amounts of video. Advancement in other technology areas such as Global Positioning System (GPS) processors and single board computers have paved the way for a new development of smart video sensors. A need exists to be able to identify stationary objects, such as buildings, and register their location back to the GIS database. Furthermore, transmitting large image streams from remote locations would quickly use available band width (BW) precipitating the need for processing to occur at the sensor location. This paper addresses the problem of automatic target recognition. Utilizing an Adaptive Resonance Theory approach to cluster templates of target buildings processing and memory requirements can be significantly reduced allowing for processing at the sensor. The results show that the network successfully classifies targets and their location in a virtual test bed environment eventually leading to autonomous and passive information processing.