Introduction:Over the last decades, there has been a remarkable increase in the number of remote sensing sensors on-board various satellite, aircraft, and land vehicle platforms. Large volumes of panchromatic, multispectral, and hyperspectral data with high resolutions (e.g., meter to sub-meter level for satellites) have been collected periodically. Fusion of these multidimensional remote sensing data along with in situ observations from multiple sensors can help us to derive more information than is possible from a single sensor alone. Examples include the determination of the composition of ground vegetation, localization of mineral resources, and other traditional application areas. However, the more detailed information such as shape and object type cannot often be derived precisely. New automatic target recognition methods that can distinguish critical object attribute and geometric characteristics would allow extraction of scale and rotationally variant objects and targets in a scene, or distinguishing two objects made of the same material. Recent development and advances in the biologically inspired methods involve segmenting patterns, materials, and objects, among other capabilities. If such human perception supported object recognition methods can be combined with much developed geometry based object recognition techniques, a variety of object recognition tasks in civilian, military, and intelligent applications can be significantly improved and speed up. They can also open up new application fields that may be otherwise impossible. In this research, we will develop an innovative biologically and geometrically inspired approach to target recognition for multispectral/hyperspectral and multiplatform image analysis. Objectives of the Research The objectives of this research project are to:
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