Tien-Hsin Chao

Multispectral imaging systems using acousto-optic tunable filter

This paper discusses recent activities of the Jet Propulsion Laboratory (JPL) in the development of a new type of remote sensing multispectral imaging instrument using acousto- optic tunable filter (AOTF) as a programmable bandpass filter. This remote sensor filter provides real-time operation; observational flexibility; measurements of spectral, spatial, and polarization information using a single instrument; and compact, solid state structure without moving parts. An AOTF multispectral imaging prototype system for outdoor field experiments was designed and assembled. Some preliminary experimental results are reported. The field system is used to investigate spectral and polarization signatures of natural and man-made objects for evaluation of the technological feasibility for remote sensing applications. In addition, an airborne prototype instrument is currently under development.

Grayscale optical correlator

A compact (Camcorder size), ultrahigh-speed, grayscale optical correlator capable of using grayscale input and real-valued correlation filter is reported. The unique bipolar-amplitude correlation filter modulation capability has enabled the direct implementation of composite filter algorithm such as MACH[8]. The direct usage of grayscale input also eliminated the need of image preprocessing (e.g. Sobel filtering used for binary optical correlator). System architecture of this grayscale optical correlator will be described. Experimental results obtained using optically implemented MACH filter, for the first time, will also be provided.

Vehicle detection and classification in shadowy traffic images using wavelets and neural networks

A vision-based traffic surveillance processor is being developed at JPL. This processor uses innovative image segmentation and classification techniques for vehicles in freeway images, including those with large shadows. These results enable the computation of many useful traffic parameters. A wavelet-based algorithm has been developed for vehicle detection and segmentation. Specifically, two types of mother wavelet has been created and tested: the first for shape-size discrimination of vehicles from their background; and the second for locating where vehicles join their shadows, thus enabling segmentation of the vehicles from their shadows. Combining these two wavelets enables robust segmentation of vehicles from busy freeways. This method reduces the false-alarm rate in vehicle counts, since shadows are no longer mistaken for vehicles. We use neural networks for vehicle classification. To reduce system complexity and training time, we use, as preprocessors, several feature extraction methods, such as invariant-moment and Hermite-moment computations. This preprocessing enables orders of magnitude reductions in training time and a great increase in classification accuracy.

Compact Holographic Data Storage System

JPL, under current sponsorships from NASA Space Science and Earth Science Programs, is developing a high-density, nonvolatile and rad-hard advanced Compact Holographic Data Storage (CHDS) system to enable large-capacity, high-speed, low power consumption, and read/write of data for potential commercial and NASA space applications. This CHDS system consists of laser diodes, photorefractive crystal, spatial light modulator, photodetector array, and I/O electronic interface. In operation, pages of information would be recorded and retrieved with random access and high-speed. The non-volatile, rad-hard characteristics of the holographic memory will provide a revolutionary memory technology to enhance mission capabilities for all NASAs Earth Science Mission.

Acousto-optic tunable filter imaging spectrometer for NASA applications: system issues

An acousto-optic tunable filter imaging spectrometer system operating in the visible region (0.4-0.8 um) has been developed and demonstrated. A comparison between other types ofspectrometers and advantages to future NASA missions are discussed. Performance issues relating to the spectral and imaging capabilities of the spectrometer are discussed.

Acousto-optic tunable filter imaging spectrometer for NASA applications: breadboard demonstration

An acousto-optic tunable filter imaging spectrometer is introduced. The design criterion meeting system issues such as image quality, spectral response, field of view (FOV), programmability, and system compactness are discussed. Experimental demonstration obtained from using a 1/2 x 1/2 x 2 AOTF imaging spectrometer breadboard is presented. Identification of Nd+3 containei in a Bastnasite rock using the spectrogram recorded by this imaging spectrometer is also provided.

Optical implementation of a matching pursuit for image representation

We have developed a technique for image analysis, representation, and decomposition. This technique was motivated by Mallat and Zhangs matching-pursuit algorithm. We have altered and simplified the mechanics of this algorithm to enable an extremely fast implementation via optical processing. Initial computer simulations show that our algorithm is capable of decomposing and representing a 2-D image as a linear combination of basis images with both high speed and high fidelity.

Grayscale optical correlator for real-time onboard ATR

Jet Propulsion Laboratory has been developing grayscale optical correlator (GOC) for a variety of automatic target recognition (ATR) applications. As reported in previous papers, a 128 X 128 camcorder-sized GOC has been demonstrated for real-time field ATR demos. In this paper, we will report the recent development of a prototype 512 X 512 GOC utilizing a new miniature ferroelectric liquid crystal spatial light modulator with a 7-micrometers pixel pitch. Experimental demonstration of ATR applications using this new GOC will be presented. The potential of developing a matchbox-sized GOC will also be discussed. A new application of synthesizing new complex-valued correlation filters using this real-axis 512 X 512 SLM will also be included.

Aided target recognition processing of MUDSS sonar data

The Mobile Underwater Debris Survey System (MUDSS) is a collaborative effort by the Navy and the Jet Propulsion Lab to demonstrate multi-sensor, real-time, survey of underwater sites for ordnance and explosive waste (OEW). We describe the sonar processing algorithm, a novel target recognition algorithm incorporating wavelets, morphological image processing, expansion by Hermite polynomials, and neural networks. This algorithm has found all planted targets in MUDSS tests and has achieved spectacular success upon another Coastal Systems Station (CSS) sonar image database.

64 x 64 thresholding photodetector array for optical pattern recognition

A high performance 32 X 32 peak detector array is introduced. This detector consists of a 32 X 32 array of thresholding photo-transistor cells, manufactured with a standard MOSIS digital 2-micron CMOS process. A built-in thresholding function that is able to perform 1024 thresholding operations in parallel strongly distinguishes this chip from available CCD detectors. This high speed detector offers responses from one to 10 milliseconds that is much higher than the commercially available CCD detectors operating at a TV frame rate. The parallel multiple peaks thresholding detection capability makes it particularly suitable for optical correlator and optoelectronically implemented neural networks. The principle of operation, circuit design and the performance characteristics are described. Experimental demonstration of correlation peak detection is also provided. Recently, we have also designed and built an advanced version of a 64 X 64 thresholding photodetector array chip. Experimental investigation of using this chip for pattern recognition is ongoing.