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12 September 2007 Synthetic data generation of high-resolution hyperspectral data using DIRSIG
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Abstract
Remote sensing often utilizes models to predict the ability of an optical system to collect data optimally prior to costly sensor testing and manufacturing. Significant effort is required to create an accurate model, and therefore most designs focus on either radiometric or spatial precision rather than a combination of the two. We present a case study in which a model has been created to satisfy both radiometric and spatial fidelity requirements. Terrain, vegetation, targets and other components of the model were designed with high precision. Hyperspectral imagery was generated using the Digital Imaging and Remote Sensing Image Generation Model (DIRSIG) based on numerous spectral and spatial ground-truth measurements. These included spectral reflectance of targets and the environment, atmospheric variables, as well as geometry and distribution of objects within the scene. Imagery was collected by airborne systems for accuracy assessment. The generated data has been validated by qualitative evaluation of the spectral characteristics and comparisons of results from PC transform and the RX anomaly detection algorithm. Validation results indicate that the model achieved a desired level of accuracy.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marek K. Jakubowski, David Pogorzala, Timothy J. Hattenberger, Scott D. Brown, and John R. Schott "Synthetic data generation of high-resolution hyperspectral data using DIRSIG", Proc. SPIE 6661, Imaging Spectrometry XII, 66610G (12 September 2007); https://doi.org/10.1117/12.735264
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