Integrated Detection and Segmentation for Hyperspectral Imagery Using Neural Networks

摘要

The combination of hyperspectral imaging systems and neural networks are changing the approach to the challenging problem of automatic target recognition (ATR). This paper summarizes a research effort to demonstrate the utility of neural networks in processing hyperspectral imagery for target detection and segmentation. Pixel registered imagery containing 32 spectral bands in the 2.0 to 2.5 μm range was used to train and test a backpropagation neural network for detection of camouflaged relocatable targets. Initially, neural networks trained and tested using all 32 spectral bands. Because of the high degree of correlation between features (i.e. spectral hinds), the dimensionality of the feature set was reduced to 11 spectral bands using both traditional (Karhunen-Loe've) and recently introduced neural network analysis techniques (Ruck's saliency). The neural network was reconfigured and retrained resulting in a probability of correct classification (PCC) of 99.8%. The neural networks were implemented in hardware on the Intel ETANN chip, a special purpose analog neural network chip. Pixel level classification allows detection and segmentation of targets in parallel. Integrated detection and segmentation (IDS) offers a powerful, alternative approach in an ATR scenario.