Modeling and simulation of imaging systems is a critical capability used throughout the lifetime of a remote sensing system. Simulating HSI systems is challenging because the mathematical methods and models used for a normal PAN or MSI system with filters cannot be used, as the vast shape diversity of the Point Spread Function (PSF) across the image plane prevents the use of an Optical Transfer Function (OTF). Provided that the HSI system satisfies the condition tint∆f ≫ 1, where tint is the integration time and ∆f is the channel bandwidth, one can avoid the complexities of partial coherence and add intensities at the focal plane. For every sub-sampled wavelength and sub-sampled location in the image plane, a unique superposition PSF needs to be computed. This requires optimized GPU CUDA kernels running in a high performance computing environment controlled with Message Passing Interface (MPI). The simulation is broken into five key steps: (1) creation of EAR hyper-cubes for each step of the CONOPS using RIT's Digital Imaging and Remote Sensing Image Generation (DIRSIG), (2) GPU accelerated Fourier Optics propagation to create superposition kernels derived from local PSFs, (3) application of the kernels to the processed DIRSIG hyper-cubes using superposition integration, (4) simulation of Focal Plane Array (FPA) detector properties, and (5) assembling the final hyper-cube image and metadata from the sequence of FPA data sets.
Modeling the imaging chain has become a critical design tool to understand the relationship between design trades and image quality in camera systems. The mathematical models for the fundamental components of an imaging chain are well understood and have been validated using working camera systems. However, the complexity of camera designs continues to grow as the technology advances to drive higher performance using different approaches. The fundamental imaging chain models do not always meet the needs of the new imaging system designs, thus requiring the models to advance in complexity as well. Of particular interest to the optical designers is the development of mathematical models that enable more complex modeling of the wavefront errors for the optical transfer functions (OTF) in the image chain models. A tutorial on the imaging chain is given followed by an innovative approach using an η matrix for modeling the OTF in the imaging chain.
Recent technological advances in computing capabilities and persistent surveillance systems have led to increased focus on new methods of exploiting geospatial data, bridging traditional photogrammetric techniques and state-of-the-art multiple view geometry methodology. The structure from motion (SfM) problem in Computer Vision addresses scene reconstruction from uncalibrated cameras, and several methods exist to remove the inherent projective ambiguity. However, the reconstruction remains in an arbitrary world coordinate frame without knowledge of its relationship to a xed earth-based coordinate system. This work presents a novel approach for obtaining geoaccurate image-based 3-dimensional reconstructions in the absence of ground control points by using a SfM framework and the full physical sensor model of the collection system. Absolute position and orientation information provided by the imaging platform can be used to reconstruct the scene in a xed world coordinate system. Rather than triangulating pixels from multiple image-to-ground functions, each with its own random error, the relative reconstruction is computed via image-based geometry, i.e., geometry derived from image feature correspondences. In other words, the geolocation accuracy is improved using the relative distances provided by the SfM reconstruction. Results from the Exelis Wide-Area Motion Imagery (WAMI) system are provided to discuss conclusions and areas for future work.
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