Greer J.B.,Drive Intelligence |
Flake J.C.,Booz Allen Hamilton
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013
The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: The Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - An AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras. © 2013 SPIE.
Braun T.R.,Drive Intelligence
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010
Image processing applications typically parallelize well. This gives a developer interested in data throughput several different implementation options, including multiprocessor machines, general purpose computation on the graphics processor, and custom gate-array designs. Herein, we will investigate these first two options for dictionary learning and sparse reconstruction, specifically focusing on the K-SVD algorithm for dictionary learning and the Batch Orthogonal Matching Pursuit for sparse reconstruction. These methods have been shown to provide state of the art results for image denoising, classification, and object recognition. We'll explore the GPU implementation and show that GPUs are not significantly better or worse than CPUs for this application. © 2010 SPIE.
Hwangbo J.,Drive Intelligence
American Society for Photogrammetry and Remote Sensing Annual Conference 2012, ASPRS 2012 | Year: 2012
We present Visual Intelligence Iris One™ Stereo System designed to achieve the performance of the film aerial cameras. The patented ARCA™ design uses synchronously operating camera module heads to form a single virtual central-perspective image. The geometric accuracy of ARCA system is achieved from laboratory calibration as well as calibration flight. First, each camera module head is calibrated to define the camera module model. Then, the entire ARCA arrays are calibrated to obtain the relative position and orientation of the camera modules. After the laboratory calibration, a single Virtual Frame image is formed. The residual of calibration of a single camera module head and the Virtual Frame is less than 1 μm. One of the coveted advantages of the film camera is the ability to achieve 0.6 B/H ratio for engineering-quality precision mapping. Designed with the long along-track footprint, the Iris One Stereo system can achieve the B/H ratio of about 0.6. The geometric accuracy of Iris One Stereo System is obtained by examining the image residuals from aerial triangulation of a test flight.
Streutker D.R.,Drive Intelligence |
Streutker D.R.,Idaho State University |
Glenn N.F.,Idaho State University |
Shrestha R.,Idaho State University
Photogrammetric Engineering and Remote Sensing | Year: 2011
A method is presented for the co-registration of overlapping elevation surfaces based on local slope analysis. Comparison and statistical analysis of local slope versus local elevation difference between overlapping surfaces allows for the estimation of both vertical and horizontal offsets between the two surfaces. This method is then used to re-align the flight lines of a Light Detection and Ranging (lidar) dat a set collected in southern Idaho resulting in a dataset with significantly higher accuracy than the original. The relative horizontal accuracy is doubled, with a final value of approximately 25 to 30 cm, while the relative vertical accuracy is improved by several centimeters to a final value of approximately 6 to 7 cm. © 2011 American Society for Photogrammetry and Remote Sensing.
Tang J.,Drive Intelligence
Current Genomics | Year: 2011
Microbial metabolomics constitutes an integrated component of systems biology. By studying the complete set of metabolites within a microorganism and monitoring the global outcome of interactions between its development processes and the environment, metabolomics can potentially provide a more accurate snap shot of the actual physiological state of the cell. Recent advancement of technologies and post-genomic developments enable the study and analysis of metabolome. This unique contribution resulted in many scientific disciplines incorporating metabolomics as one of their "omics" platforms. This review focuses on metabolomics in microorganisms and utilizes selected topics to illustrate its impact on the understanding of systems microbiology. © 2011 Bentham Science Publishers.