Dr. Joan B. Lurie Profile

Dr. Joan B. Lurie

VP, Business Development

SPIE Involvement:

Author

SPIE Journal Paper | 1 September 2002 Open Access

Remote Sensing Technology and Applications

Joan Lurie, Edward Irvin

OE, Vol. 41, Issue 09, (September 2002) https://doi.org/10.1117/12.10.1117/1.1509457

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Proceedings Article | 31 December 1996 Paper

Hyperview-fast, economical access to large data sets: a system for the archiving and distribution of hyperspectral data sets and derived products

Joan Lurie

Proceedings Volume 2960, (1996) https://doi.org/10.1117/12.262480

KEYWORDS: Data archive systems, Data acquisition, Satellites, Internet, Sensors, Image compression, Spectroscopy, Data communications, Data processing, Data compression

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TRW, under a Small Satellite Technology Initiative (SSTI) contract, is building the Lewis satellite. The principal sensor on Lewis is a hyperspectral imaging spectrometer. Part of the SSTI mission is to establish the commercial and educational utility of this data and the hyperspectral data already being acquired on airborne platforms. Essential requirements are rapid availability (after data acquisition) and easy accessibility to a catalog of images and imagery products. Each image is approximately 256 by 512 pixels with 384 bands of data acquired at each pixel. For some applications, some users will want the entire data sets; in other cases partial data sets (e.g. three band images) will be all that a user can handle or need for a given application. In order to make the most effective use of this new imagery and justify the cost of collecting it, we must find ways to make the information it contains more readily accessible to an ever broadening community of potential users. Tools are needed to store, access, and communicate the data more efficiently, to place it in context, and to derive both qualitative and quantitative information from it. A variety of information products which address the specific needs of particular user communities will be derived from the imagery. The data is unique in its ability to provide high spatial and spectral resolution simultaneously, and shows great promise in both military and civilian applications. A data management and analysis system has been built at TRW. This development has been prompted by the business opportunities, by the series of instruments built here and by the availability of data from other instruments. The products of the processing system have been shown to prospective customers in the U.S. and abroad. The system has been used to process data produced by TRW sensors and other instruments. This paper provides an overview of the TRW hyperspectral collection, data handling and exploitation capability.

Proceedings Article | 4 November 1996 Paper

Establishment of a multimedia data storage and dissemination system for earth observation studies

Pamela Emch, Joan Lurie, William Noah

Proceedings Volume 2818, (1996) https://doi.org/10.1117/12.256096

KEYWORDS: Databases, Data archive systems, Multimedia, Data storage, Ecosystems, Silicon, Video, Prototyping, Radon, Data acquisition

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Proceedings Article | 21 December 1994 Paper

Image quality measures to assess hyperspectral compression techniques

Joan Lurie, Bruce Evans, Brian Ringer, Mathew Yeates

Proceedings Volume 2313, (1994) https://doi.org/10.1117/12.197349

KEYWORDS: Image compression, Image quality, Quality measurement, Data acquisition, Image classification, Wavelets, Sensors, Image segmentation, Visualization, Data processing

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The term 'multispectral' is used to describe imagery with anywhere from three to about 20 bands of data. The images acquired by Landsat and similar earth sensing satellites including the French Spot platform are typical examples of multispectral data sets. Applications range from crop observation and yield estimation, to forestry, to sensing of the environment. The wave bands typically range from the visible to thermal infrared and are fractions of a micron wide. They may or may not be contiguous. Thus each pixel will have several spectral intensities associated with it but detailed spectra are not obtained. The term 'hyperspectral' is typically used for spectral data encompassing hundreds of samples of a spectrum. Hyperspectral, electro-optical sensors typically operate in the visible and near infrared bands. Their characteristic property is the ability to resolve a large number (typically hundreds) of contiguous spectral bands, thus producing a detailed profile of the electromagnetic spectrum. Like multispectral sensors, recently developed hyperspectral sensors are often also imaging sensors, measuring spectral over a two dimensional spatial array of picture elements of pixels. The resulting data is thus inherently three dimensional - an array of samples in which two dimensions correspond to spatial position and the third to wavelength. The data sets, commonly referred to as image cubes or datacubes (although technically they are often rectangular solids), are very rich in information but quickly become unwieldy in size, generating formidable torrents of data. Both spaceborne and airborne hyperspectral cameras exist and are in use today. The data is unique in its ability to provide high spatial and spectral resolution simultaneously, and shows great promise in both military and civilian applications. A data analysis system has been built at TRW under a series of Internal Research and Development projects. This development has been prompted by the business opportunities, by the series of instruments built here and by the availability of data from other instruments. The products of the processing system has been used to process data produced by TRW sensors and other instruments. Figure 1 provides an overview of the TRW hyperspectral collection, data handling and exploitation capability. The Analysis and Exploitation functions deal with the digitized image cubes. The analysis system was designed to handle various types of data but the emphasis was on the data acquired by the TRW instruments.

Proceedings Article | 25 February 1994 Paper

Model-based object recognition in aerial photography

James Knowles, James Pearson, Brian Ringer, Joan Lurie

Proceedings Volume 2103, (1994) https://doi.org/10.1117/12.169474

KEYWORDS: Image processing, Binary data, Object recognition, Edge detection, 3D modeling, Model-based design, Photography, Atmospheric modeling, Algorithm development, Optical correlators

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