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To accurately model imagery for converting a single image source to generate imagery for another sensor, it is necessary to develop feature classification techniques that can define significant features in the imagery so that the scattering properties of the incident radiation can be used as a technique to model the desired bands of the electromagnetic spectrum. This paper wifi be concerned with the extraction of features where either color infrared (CIR) or electro-optical (EO) imagery are available as the baseline source materials. The feature extraction process will be initiated using first order techniques for the iiiital classification. This initial classification will be followed by using higher order, more computationally intensive methods. Since higher order methods are usually specific to certain features, a battery of higher order methods will be required to classify the features in an entire scene. These various classified features will be linked using techniques of image analysis. These techniques have been used to generate a sequence of images, where CIR imagery was converted to thermal infrared (TIR) and synthetic aperture radar (SAR), for mission simulation and planning. The input images are processed initially to define regions based on some measure of homogeneity within regions of the image. This processing could be based on texture or measures of signature content within different bands of a niultispectral image. Both automatic and manual classification techniques, including synergistic coinbiiiatioiis, are applicable for this stage of processing. The precise form of the processing should also be guided by whether the regions being processed consist of nianmade or natural regions. This information is very useful since manmade structures usually consist of regularly shaped, rigid regions, whereas natural objects are less well defined and usually exhibit more randomness. Thus, for manmade object feature extraction, it is appropriate to use techniques for extracting lines, regions, ellipses/circles, or other regular-shaped regions with some regular periodicity occurring within selected, larger subregions. On the other hand, naturally occurring features could be more accurately extracted using texture methods or metrics defined based on the energy content of different bands of the electromagnetic spectrum. The feature extraction techniques discussed in this paper are hierarchical in nature to reduce the computational requirements imposed on developing the large set of images required for realistic mission training. The first-order classification is most efficiently implemented by using the three bands of a CIR image which can be transformed from red, green, and blue to intensity, hue and saturation. This transformation has the twofold effect of iuakiiig the process independent of the total received intensity, while at the same time reducing the three parameter lookup table to a two parameter lookup table. The details and accuracy of these techniques, which have been implemented for mission simulation and planning, will be presented in the paper. Combining these techniques with texture methods, which are based on regularity measures of regions, leads to a refined classification. The final step in the procedure is to perform image analysis as a refinement procedure for the classification. Based on the scenes being analyzed and some known a priori scene content, detailed feature extraction procedures can be developed for specific features. Analysis of these features allows decision rules to be constructed based on the features and their interrelationships. These decision rules allow the system developer to encode the proper constraints into the algorithmic processes to determine when the feature actually exists and its limiting boundaries. As more practice and expertise is developed iii the image conversion process, these image analysis techniques will become more and more automated.
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