In this paper the authors present the results of a research investigation on feature selection methods for neural network classifiers. As problems presented to computers for analysis become more complex and data dimensionality grows in size, traditional methods of feature extraction are being taxed beyond the limits of their usefulness. New methods of feature selection show promise in the laboratory, but need to be proven with real-world solutions. The purpose of this research is to compare the performance of newly proposed methods of selecting features on three challenging problems using non- artificial data. A feature saliency technique, and several variants of genetic algorithms, and random feature selection are compared and contrasted.
The combination of hyperspectral imaging systems and neural networks are changing the approach to the challenging problem of automatic target recognition. This paper summarizes a research effort to demonstrate the usefulness of neural networks in processing hyperspectral imagery for target detection and segmentation.
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 micrometers 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 bands), the dimensionality of the feature set was reduced to 11 spectral bands using both traditional (Karhunen-Loeve) 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.
This paper summarizes a research effort to explore the use of neural networks in processing hyperspectral imagery for the purpose of target detection and feature selection in an automatic target detection (ATR) scenario. Images containing 32 spectral bands in the 2.0 to 2.5 micrometers infrared range and with co-registered pixels were used to train and test a backpropagation neural network for detection of ground targets. The dimensionality of the original feature set was reduced using two methods, Karhunen-Loeve and Ruck's saliency technique. The results for the two feature selection techniques are compared using classifier performance as a metric. Finally, a neural network chip (ETANN) was used to test the feasibility of hardware implementation of the fusion processing.
Neural networks have proven very useful in the field of pattern classification by mapping input patterns into one of several categories. One widely used neural network paradigm is the multi- layer perceptron employing back-propagation of errors learning -- often called back- propagation networks (BPNs). Rather than being specifically programmed, BPNs `learn' this mapping by exposure to a training set, a collection of input pattern samples matched with their corresponding output classification. The proper construction of this training set is crucial to successful training of a BPN. One of the criteria to be met for proper construction of a training set is that each of the classes must be adequately represented. A class that is represented less often in the training data may not be learned as completely or correctly, impairing the network's discrimination ability. This is due to the implicit setting of a priori probabilities which results from unequal sample sizes. The degree of impairment is a function of (among other factors) the relative number of samples of each class used for training. This paper addresses the problem of unequal representation in training sets by proposing two alternative methods of learning. One adjusts the learning rate for each class to achieve user- specified goals. The other utilizes a genetic algorithm to set the connection weights with a fitness function based on these same goals. These methods are tested using both artificial and real-world training data.
Multiple sensor imaging systems 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 classification. Pixel registered imagery containing 32 spectral bands in the 2.0 to 2.5 mm range was used to train and test a backpropagation neural network for detection of camouflaged targets. An initial neural network was trained and tested using all 32 spectral bands resulting in a probability of correct classification (Pcc) at the pixel level of 98.7 percent. Because of the high degree of correlation between features (i.e., spectral bands), the dimensionality of the feature set was reduced to 11 spectral bands using a Karhunen-Loeve expansion. The neural network was reconfigured and retrained resulting in a Pcc of 99.8 percent. This second neural network was implemented in hardware on the Intel ETANN chip, a special purpose analog neural network chip resulting in a Pcc of 96.3 percent. A single ETANN chip is capable of classifying 400,000 pixels per second. The capability of classifying each individual pixel in a hyperspectral image in real time radically alters the possible approaches in an ATR scenario.
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