Multiresolution segmentation of forward-looking IR and SAR imagery using neural networks

Hal E. Beck; Daniel Bergondy; Joe R. Brown; Hamed Sari-Sarraf

doi:10.1117/12.25191

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1 February 1991 Multiresolution segmentation of forward-looking IR and SAR imagery using neural networks

Hal E. Beck, Daniel Bergondy, Joe R. Brown, Hamed Sari-Sarraf

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Proceedings Volume 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques; (1991) https://doi.org/10.1117/12.25191
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

A neural network approach to segmentation of forward looking infrared and synthetic aperture radar imagery is presented. This approach integrates three stages of processing. First a wavelet transform of the image is performed by projection of the image onto a set of 2-D Gabor functions. This results in a multiple-resolution decomposition of the image into oriented spatial frequency channels. Scond a neural network optimization procedure is used to estimate the wavelet transform coefficients. The third stage involves a segmentation technique that has been shown to work well on textures that human subjects readily segment into regions. Although the approach is still under development preliminary results are promising. The direction of further research efforts are discussed.

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Hal E. Beck, Daniel Bergondy, Joe R. Brown, and Hamed Sari-Sarraf "Multiresolution segmentation of forward-looking IR and SAR imagery using neural networks", Proc. SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques, (1 February 1991); https://doi.org/10.1117/12.25191

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