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This paper demonstrates the capability of a set of image search algorithms and display tools to search large databases for multi-and hyperspectral image cubes most closely matching a particular query cube. An interactive search and analysis tool is presented and tested based on a relevance feedback approach that uses the "human-in-the-loop" to enhance a content-based image retrieval process to rapidly find the desired set of image cubes.

Abstract-A high speed of retrieval is very important to developing an effective image cube search algorithm for the remote sensing community. Following the work of Berman and Shapiro, it is shown that a triangle inequality search technique applied to a relevance feedback retrieval algorithm can significantly speed up the search for and retrieval of physical events of interest in large remote-sensing databases. An improvement in retrieval speed is illustrated using hurricane queries applied to the multispectral GOES database.

A scenario-based model has been developed to predict performance of infrared imaging sensors, including optimal and suboptimal processing gains from filtering and tracking. The geometry-based driver allows easy setup of physically meaningful scenarios, including a 3-D extended target model (thermal emission and reflected earth, sun, and sky radiance), clutter background, and MODTRAN-based atmospherics. The sensor model accounts for optics, detector, scanning, piatform jitter, pattern and sensor noise, and focal plane sampling. Integrated filter and tracker models allow for end-to-end trades and assessing the relative impact of filter and tracker processing. The filter model is a fourier-based ESNR model with a range of filter and registration options. The tracker model is likelihood-based, not simulation or Monte-Carlo, allowing quick identification of dominant effects on performance. Log-likelihood evolves with measurement updates and spreading loss from plant noise, and its statistics characterize optimal tracker perfonnance. Log-likelihood field statistics reveal the effects of suboptimal processing, including covariance misestimation and peak strength thresholding. Various physically meaningful outputs include minimum time to confirm track, ROC curves, and noise exceedance plots. Trade studies generated from this model are presented, illustrating dependencies on scenario, sensor, and signal processing.

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