There are many applications for micro-optics. Perhaps the most exciting use of micro-optics is in fiber optical communication systems, getting the light signals in and out of the fibers. Other possible uses include data processing, chemical sensing, and spectroscopic applications. In this talk we will describe (mu) ChemLab, the Polychromator, a new spectroscopic gas identification device, a fiberoptic status monitor, and a deep X-ray lithographic technique for fabricating micro-optical systems.
Hadamard Transform Spectrometer (HTS) approaches share the multiplexing advantages found in Fourier transform spectrometers. Interest in Hadamard systems has been limited due to data storage/computational limitations and the inability to perform accurate high order masking in a reasonable amount of time. Advances in digital micro-mirror array (DMA) technology have opened the door to implementing an HTS for a variety of applications including fluorescent microscope imaging and Raman imaging. A Hadamard transform spectral imager (HTSI) for remote sensing offers a variety of unique capabilities in one package such as variable spectral and temporal resolution, no moving parts (other than the micro-mirrors) and vibrational insensitivity. An HTSI for remote sensing using a Texas Instrument digital micro-mirror device (DMD) is being designed for use in the spectral region 1.25 - 2.5 micrometers . In an effort to optimize and characterize the system, an HTSI sensor system simulation has been concurrently developed. The design specifications and hardware components for the HTSI are presented together with results calculated by the HTSI simulation that include the effects of digital (vs. analog) scene data input, detector noise, DMD rejection ratios, multiple diffraction orders and multiple Hadamard mask orders.
The ISIS approach to spectral imaging seeks to bridge the gap between tuned multispectral and fixed hyperspectral imaging sensor. By allowing the definition of completely general spectral filter functions, truly optimal measurement can be made for a given task. These optimal measurements significantly improve signal to noise ratio and speed, minimize data volume and data rate, while preserving classification accuracy. This paper investigates the application of the ISIS sensing approach in two sample biomedical applications: prostate and colon cancer screening. It is shown that is these applications, two to three optimal measurements are sufficient to capture the majority of classification information for critical samples constituents. In the prostate cancer example, the optimal measurements allow 8 percent relative improvement in classification accuracy of critical cell constituents over a red, green, blue (RGB) sensor. In the colon cancer example, use of optimal measurements boost the classification accuracy of critical cell constituents by 28 percent relative to the RGB sensor. In both cases, optimal measurements match the performance achieved by the entire hyperspectral data set. The paper concludes that an ISIS style spectral imager can acquire these optimal spectral images directly, allowing improved classification accuracy imager can acquire these optimal spectral images directly, allowing improved classification accuracy over an RGB sensor. Compared to a hyperspectral sensor, the ISIS approach can achieve similar classification accuracy using a significantly lower number of spectral samples, thus minimizing overall sample classification time and cost.
The information-efficient spectral imaging sensor (ISIS) seeks to improve system performance by processing hyperspectral information in the optical hardware. Its output may be a gray scale image in which one attempts to maximize the contrast between a given target and the background. Alternatively, its output may be a small number of images, rather than a full data cube, that retain the essential information required in the application. The principal advantages of ISIS is that it offers the discrimination of hyperspectral imaging while achieving the signal-to-noise ratio of multispectral imaging. The paper focuses on construction of the filter vectors that are needed to program ISIS. The instrument produces an image which is essentially a dot product of the scene and the filter vector. Both single vector and multiple vector approaches are considered. Also, we discuss some subtle points related to optimizing the filter vectors.
A specialized hyperspectral imager has been developed that preprocesses the spectra from an image before the light reaches the detectors. This 'optical computer' does not allow the flexibility of digital post-processing. However, the processing is done in real time and the system can examine approximately equals 2 X 106 scene pixels/sec. Therefore, outdoors it could search for pollutants, vegetation types, minerals, or man-made objects. On a high-speed production line it could identify defects in sheet products like plastic wrap or film, or on painted or plastic parts.
Conference Committee Involvement (1)
Imaging Spectrometry IX
6 August 2003 | San Diego, California, United States
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