An Advanced Synthetic Image Generation Model and its Application to Multi/Hyperspectral Algorithm Development

Schott, John R.; Brown, Scott; Raqueno, Rolando V.; Gross, Harry N.; Robinson, Gary D.

doi:10.1080/07038992.1999.10874709

Cited by 157 publications

(69 citation statements)

References 14 publications

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“…Various broad-band, multi-spectral and hyperspectral imagery are generated through the integration of a suite of first principles based radiation propagation sub-models [10]. Before performing scene simulation and sensor modeling, we need to set up different scenarios and configure the sensor parameters.…”

Section: Scene Simulation and Sensor Modelingmentioning

confidence: 99%

A smart sensor with hyperspectral/range fovea and panoramic peripheral view

Wang

Zhu

Rhody

2009

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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We propose an adaptive and effective multimodal peripheral-fovea sensor design for real-time targets tracking. This design is inspired by the biological vision systems for achieving real-time target detection and recognition with a hyperspectral/range fovea and panoramic peripheral view. A realistic scene simulation approach is used to evaluate our sensor design and the related data exploitation algorithms before a real sensor is made. The goal is to reduce development time and system cost while achieving optimal results through an iterative process that incorporates simulation, sensing, processing and evaluation. Important issues such as multimodal sensory component integration, region of interest extraction, target tracking, hyperspectral image analysis and target signature identification are discussed.

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Section: Scene Simulation and Sensor Modelingmentioning

confidence: 99%

A smart sensor with hyperspectral/range fovea and panoramic peripheral view

Wang

Zhu

Rhody

2009

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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“…The DIRSIG model is a complex synthetic image generation application which produces simulated imagery in the visible through thermal infrared regions [28]. The model is designed to produce broad-band, multi-spectral and hyperspectral imagery through the integration of a suite of first principles based radiation propagation sub-models.…”

Section: Experiments Trough Simulationmentioning

confidence: 99%

Intelligent multimodal and hyperspectral sensing for real-time moving target tracking

Wang

Zhu

2008

2008 37th IEEE Applied Imagery Pattern Recognition Workshop

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Abstract-Real time moving target tracking and identification with hyperspectral imagery is still very challenging with conventional sensors and algorithms. The increased information content of hyperspectral imaging has enabled improved classification and quantification of targets of interest. However, recording hyperspectral data for target classification is very time consuming. We design a sensor platform with multi-modalities, consisting of a dual-panoramic peripheral vision system and a narrow field-of-view hyperspectral fovea. Thus, we only need to capture hyperspectal images in regions of interest. This design is inspired by the human vision system where the periphery vision of the retina is used to detect motion and the fovea of the retina is used to recognize objects.The proposed intelligent sensors design is also supported by real-time algorithms for target detection, tracking and identification. Only hyperspectral data for areas of interest are captured for target classification and recognition. Important issue such as multimodal sensing component cooperation, region of interest extraction, target tracking, hyperspectral image analyzing and target signature identification are discussed.

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“…The corresponding error between histograms of the original and the synthesized textures is also shown. In another set of experiments, we compared the textures created by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) (Schott 1999) synthetic image generation software with the texture synthesis algorithm. Finally, we show results of the synthesis experiments on hyperspectral data.…”

Section: Temperature/emissivity Separation -Marvin Boonmee (Rit Phd Smentioning

confidence: 99%

Model-based Hyperspectral Exploitation Algorithm Development

Schott¹,

Healey²,

Philpot³

2007

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LONG-TERM GOALSHyperspectral data has become a critical tool for use by military analysts and planners. The capture of fine spectral information enables the generation of information products which could not be produced using traditional imaging means. The challenge facing hyperspectral technology, as an operational capability, is with conversion of the raw sensor data into a useful information product that is accurate and reliable. Traditional approaches for processing hyperspectral data have largely focused on the use of statistical tools to process a hypercube, with little regard for other data that may describe the physical phenomena under which the data was collected. The long-term goal of this project has been to develop a new generation of hyperspectral processing algorithms that take advantage of underlying physics of hyperspectral image data while utilizing statistical processing techniques to generate final information products. OBJECTIVESThe objectives of this project are to develop hyperspectral analysis tools that incorporate physics-based processing in the following application areas:• Water quality and biological activity (littoral zone)• Material classification and identification• Atmospheric parameter retrieval/correction 1

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An Advanced Synthetic Image Generation Model and its Application to Multi/Hyperspectral Algorithm Development

Cited by 157 publications

References 14 publications

A smart sensor with hyperspectral/range fovea and panoramic peripheral view

A smart sensor with hyperspectral/range fovea and panoramic peripheral view

Intelligent multimodal and hyperspectral sensing for real-time moving target tracking

Model-based Hyperspectral Exploitation Algorithm Development

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