Image accuracy and representational enhancement through low-level, multi-sensor integration techniques

摘要

Multi-Sensor Integration (MSI) is the combining of data and information from more than one source in order to generate a more reliable and consistent representation of the environment. The need for MSI derives largely from basic ambiguities inherent in our current sensor imaging technologies. These ambiguities exist as long as the mapping from reality to image is not 1-to-1. That is, if different (open quotes)realities(close quotes) lead to identical images, a single image cannot reveal the particular reality which was the truth. MSI techniques attempt to resolve some of these ambiguities by appropriately coupling complementary images to eliminate possible inverse mappings. What constitutes the best MSI technique is dependent on the given application domain, available sensors, and task requirements. MSI techniques can be divided into three categories based on the relative information content of the original images with that of the desired representation: (1) (open quotes)detail enhancement,(close quotes) wherein the relative information content of the original images is less rich than the desired representation; (2) (open quotes)data enhancement,(close quotes) wherein the MSI techniques are concerned with improving the accuracy of the data rather than either increasing or decreasing the level of detail; and (3) (open quotes)conceptual enhancement,(close quotes) wherein the image contains more detail than is desired, making it difficult to easily recognize objects of interest. In conceptual enhancement one must group pixels corresponding to the same conceptual object and thereby reduce the level of extraneous detail.