H yperspectral sensors are a new class of optical sensor that collect a spectrum from each point in a scene. They differ from multispectral sensors in that the number of bands is much higher (20 or more) and the spectral bands are contiguous. For remote sensing applications, they are typically deployed on either aircraft or satellites. The data product from a hyperspectral sensor is a three-dimensional array or "cube" of data with the width and length of the array corresponding to spatial dimensions and the spectrum of each point as the third dimension. While this data cube is a convenient way to view the product, at most two of the dimensions can be acquired simultaneously. In the most common configuration, the spectral and one spatial dimension are acquired simultaneously to give a high quality spectral signature for each point in the scene. AVIRIS [1] and many other sensors directed toward terrain classification are flying spot sensors that acquire an image by raster scanning the scene in width while the platform moves to build up the second spatial dimension. More recently, the spatial resolution of hyperspectral sensors has been improved by using a two-dimensional focal plane array that simultaneously acquires a spectrum along a line of points in the scene. The second dimension is then built up by motion of the platform.

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The use of optics to detect targets has been around for a long time. Early attempts at automatic target detection assumed target plus noise, which means that the targets were small compared to the pixel field of view and therefore unresolved. However, the advent of advanced focal plane technology has resulted in optical systems that can provide highly resolved target images. The intent of this paper is to develop a general solution for the detection of resolved targets in background clutter. We recognize that resolved targets obscure any background clutter that would have been visible if the targets were absent. An optimum detection algorithm is derived that compares a test statistic to a threshold and decides a target is present if the statistic is less than the threshold. We find that the detection performance depends upon (1) the apparent contrast rather than the signal to noise ratio and (2) is highly dependent on the background clutter to common system noise ratio. In fact, the target can still be detected even when the target contrast goes to zero provided the background clutter is greater than the common system noise. Computer simulations are shown to validate the theoretical detection and false alarm probabilities. The findings in this paper should be useful to engineers and scientists designing electro-optical and infrared sensors for finding resolved targets immersed in background-cluttered images.

SUMMARY PROBLEMInvestigate a weighted-difference signal-processing algorithm for detecting ground targetr by using dual-band IR data. RESULTSThree variations of the algorithm were evaluated: (1) simple difference; (2) minimum noise; and (3) maximum SNR. The theoretical performance was compared to measured performance for two scenes collected by the NASA TIMS sensor over a rural area near Adelaide, Australia, and over a wooded area near the Redstone Arsenal. The theoretical and measured results agreed extremely.well. For a given correlation coefficient and color ratio, the amount of signal-to-noise ratio gain can be predicted. However, target input SNRs and color ratios can vary considera-iy. For the targets and scenes evaluated here, the typical gains achieved ranged from a few dB loss (targets without color) to a maximum of approximately 20 dB. Accession For NTIS GRA&I DTIC TAB