SPARSE ADAPTIVE FILTER

摘要

The disclosure provides a filtering engine for selecting a subset of hyperspectral imaging wavebands having information useful for detecting a target in a scene. Selecting these wavebands, called “sparse bands,” is an iterative process. One or more search techniques of varying computational complexity are used in the process. The techniques rely on various selection criteria, including a signal to clutter ratio that measures the “goodness” of band selection. A convenient example of the filtering engine uses several of the techniques together in a layered approach. In this novel approach, simpler computational techniques are applied, initially, to reduce a number of bands. More computationally intensive techniques then search the reduced band space. Accordingly, the filtering engine efficiently selects a set of sparse bands tailored for each target and each scene, and maintains some of the detection capability provided with a full set of wavebands.

主权项

1. A method for selecting a subset of hyperspectral imaging wavebands to detect a target in a scene, the method comprising:
in a filtering engine provided with a plurality of spectra of a target and a covariance matrix of a scene, for each spectrum in the plurality of spectra of the target: computing a signal-to-clutter ratio of a full set of wavebands representing the probability of detecting the target viewed in the scene; selecting a subset of wavebands from the full set of wavebands; computing a signal-to-clutter ratio of the selected subset of wavebands representing the probability of detecting the target viewed in the scene using the selected subset of wavebands; comparing the signal-to-clutter ratio of the subset of wavebands to the signal-to-clutter ratio of the full set of wavebands; determining whether the subset of wavebands is a good enough set of wavebands based on the comparison; andproviding the good enough set of wavebands to detect the target in the scene.