Spectral imagers in the Long Wave IR spectral range (8 to 12 microns) suffer from the problem of high production costs because the existing commercial cooled array detectors are expensive, and in fact they are prohibitively expensive for many applications. As a result, the drive to lower the cost of Long Wave IR spectral imagers is strong: this is the main motivation for CI to investigate a new design that allows these spectral imagers to be more affordable. One area of possible cost reduction without relinquishing the advantages of a cryogenically cooled detector is the method used to provide the spectral information. CI Systems has developed a long wave IR (7.7 to 12.3 micron) spectral imager concept using a Circular Variable Filter (CVF), (a proprietary component based on multiple layer interference filter technology) which has advantages over the interferometric Fourier Transform method commonly used in this spectral range. The CVF method has its own development challenges; however, once proven, this concept may be more suitable and affordable for applications in which a spectral resolution of 0.5% of the wavelength (or 50 nm at 10 μ) is required. The design of the optical system must minimize background signals without being cooled to cryogenic temperatures, so we called it VIrtually COld (or VICO). CI is in the final stages of prototype building and characterization. Present initial calibration results and measurement examples are given in this paper.
Using a set of radiometric thermal hyperspectral data cubes, we developed an algorithm which detects the formation of an anomalous gas cloud. Once we've established the presence of the cloud in the latter images, we determine the origin of the cloud in the earlier ones and track its propagation. Gas usually expands from point sources and it is difficult to know whether it is significant when it occupies merely a few pixels in the image. After the gas size expands, it is easier to analyze as an interesting anomalous feature.
Our algorithm includes techniques such as the improved K-Means classification, Spectral Angle Mapper (SAM), match filter and tracking; in the paper we will show results based on real data taken by the "FIRST" camera (Field-portable Imaging Radiometric Spectrometer Technology).
KEYWORDS: Image segmentation, Detection and tracking algorithms, Target detection, Signal to noise ratio, Image filtering, Speckle, Optical engineering, Multispectral imaging, Optical filters, RGB color model
Basing ourselves on a novel segmentation algorithm for multispectral images, we consider how to detect multipixel anomalous objects in image cubes where spectral information is available. In particular, we have developed several morphological filters to compensate for noise that may be present in the initial cube. We show that for different types of noise (Gaussian and speckle), a modification of our morphology technique allows us to better detect targets without an enhanced false-alarm result.
Over the last few years, we have developed an algorithm which detects anomalous targets in hyperspectral or multispectral images. The algorithm takes a data (image) cube with a completely unknown background, segments the cube, assigns the largest clusters as background, and determines which pixels are anomalous to the background. In the work to be presented here, we will add two additional modules. First, since our present mission is to detect military targets in a fairly barren rural background, we use the SAVI (or NDVI) metric to detect items which appear to contain chlorophyll. In this way, we can eliminate objects which in retrospect were the right sizes and shapes but were in reality plants. Second, we have developed CFAR methods to achieve a Constant False Alarm Rate while giving us the maximum probability of detecting the targets. Actual data will be analyzed by the algorithm; the ability to both determine if a target is present and where its location is will be shown.
Basing ourselves on a novel segmentation algorithm for multi-spectral images, we have considered how to detect multi-pixel anomalous objects in image cubes with a spectral component. In particular, we have developed several filters to compensate for noise which may be present in the initial cube. We show that for speckle noise, a modification of our morphology technique allows us to detect targets without an enhanced false alarm result.
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