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The Naval Weapons Center (NWC) is currently developing automatic target classification systems for future surveillance and attack aircraft and missile seekers. Target classification has been identified as a critical operational capability which should be included on new Navy aircraft and missile developments or systems undergoing significant modifications. The objective for the Automatic Classification Infrared Ship Imagery System is to provide the following new capablities for surveillance and attack aircraft and antiship missiles: near real-time automatic classification of ships in day and night at long standoff ranges with a wide area coverage imaging infrared sensor. The sensor applies classical pattern recognition technology to automatically classify ships using Forward Looking Infrared (FLIR) images. Automatic Classification of Infrared Ship Imagery is based on the extraction of features which uniquely describe the classes of ships. These features are used in conjunction with decision rules which are established during a training phase. Conventional classification techniques require labeled samples of all expected targets, threats and non-threats for this training phase. To overcome the resulting need for the collection of an immense data base, NWC developed a Generalized Classifier which, in the training phase, requires signals only from the targets of interest, such as high value combatant threats. In the testing phase, the signals from the combatants are classified and signals from other ships, which are sufficiently different from the training data, are classified as "other" targets. This technique provides a considerable savings in computer processing time, in memory requirements and data collection efforts. Since sufficient IIR images of the appropriate quality and quantity were not available for investigating automatic IIR ship classification, TV images of ship models were used for an initial feasibility demonstration. The initial investigation made use of the experience gained with preprocessing and classifying ROR and ISAR data. For this reason, the most expedient method was to collapse the 2-dimensional TV ship images onto the longitudinal axis by summing the amplitude data in the vertical ship axis. The resulting 128 point 1-dimensional profiles show the silhouette of the ship and bear an obvious similarity with the radar data. Based on that observation, a 128 point Fourier transform was computed and the ten low order squared amplitudes of the complex Fourier coefficients were then used as feature vectors for the Generalized Classifier. In contrast to the radar data, the size of TV or IIR images of ships changes as a function of range. It is therefore necessary to develop feature extraction algorithms which are scale invariant. The central moments, which have scale and rotational invariant properties were therefore implemented. This method was suggested in 1962 by M. K. Hu (IRE Transactions on Information Theory). Using the moments alone resulted in unsatisfactory classification performance and indicated that edge enhancement was necessary and that the background needed to be rejected. The images were therefore processed with the Sobel nonlinear edge enhancement algorithm, which also has the desirable property that it works for images with low signal-to-noise ratios and poorly defined edges. Satisfactory results were obtained. In another experiment, the feature vector was composed of the five lower-order invariant moments and the five lower-order FFT coefficient squared magnitudes, excluding the zero frequency coefficient. This paper will describe the data base, the processing and classification techniques, discuss the results and addresses the topic of "Processing of Images and Data Optical Sensors."
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