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.
R. Woodley et al., "Automatic Building Identification Using GPS and Machine Learning," International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2739-3742, Institute of Electrical and Electronics Engineers (IEEE), Jan 2010.
The definitive version is available at http://dx.doi.org/10.1109/IGARSS.2010.5653179
30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 (2010: Jul. 25-30, Honolulu, HI)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
Article - Conference proceedings
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