CHIMES: An enhanced end-to-end Cranfield hyperspectral image modelling and evaluation system - Zahidi, Usman A. (Cranfield University, 2020)
Hyperspectral remote sensing enables establishing semantics from an image by providing spectral details used for differentiating materials. The airborne/satellite setup for remote sensing are typically expensive in terms of time and cost-effectiveness. It is therefore important to predict performance of such systems as a precursor. Hyperspectral scene simulation is a technique which allows the detailed spatial and spectral information of a natural scene to be reconstructed without the need for expensive and time-consuming airborne/spaceborne image acquisition systems. It helps in predicting the potential performance of airborne/satellite systems, moreover, it enables varying atmospheric conditions, estimating degradation in sensor performance to provide better uncertainty analysis and traceability, performance analysis of data processing algorithms and counter-measures/camouflage assessment in military applications. Digital Imaging Remote Sensing Image Generation (DIRSIG) developed by Rochester Institute of Technology and Camoflauge Electro-Optic Simulator (CameoSim) by Lockheed Martin are the two earliest research and commercial products, respectively, that incorporate hyperspectral rendering for accurate physicsbased radiance estimation. Although CameoSim is a well-established Scene simulator, however it does not support volumetric scattering and localised adjacency model. DIRSIG has provided support form these features in newly developed version called DIRSIG5. Due to export control restriction it is typically not possible to access these simulators, hence motivates development of inhouse scene simulator. This thesis summarises the research which constitutes part of the deliverable under the DSTL R-Cloud project for the establishment of an in-house HSI scene simulator, which is known as the Cranfield Hyperspectral Image Modelling and Evaluation System (CHIMES). CHIMES is a physicsbased rendering enabled simulator and the main concept follows directly the radiative transfer (RT) big equation, with some components adopted from DIRSIG and CameoSim etc. The goal of the present research has been set and the work has been progressed in the following manner: • The establishment of CHIMES from scratch; • Validation of CHIMES through direct comparison with commercial-off-the-shelf (COTS) simulator such as CameoSim (CS); • Enhancement of CHIMES over the COTS simulator (e.g. CS) to include automatic in-scene atmospheric parametrisation, localised adjacency-effect model and volumetric scattering to achieve a more realistic scene simulation particularly for the rugged terrain; • To propose methods on how difficult issues such as shadows can be mitigated in scene simulation. This thesis summarises the work performed as according to the above 4 objectives with main results as follows: • CHIMES has been shown to reproduce the scene simulation performed by a COTS simulator (e.g. CameoSim) under various atmospheric conditions. • An automatic atmosphere parameterisation search algorithm has been shown to be effective to allow the simulation of the scene without the need of repeated trial and error atmospheric parameter adjustments. • Two adjacency models: the Background One-Spectra Adjacency Effect Model (BOAEM) and the Texture-Spectra Incorporated Adjacency Effect Model (TIAEM) have been developed under this work. The BOAEM is somewhat similar to that adopted in CS with a distinctive feature of volumetric scattering, however, the TIAEM is a terrain dependence adjacency which is much more sophisticated. It has been shown that at high altitude scene, TIAEM performs better than the BOAEM by 6.0% and by 10.0% better than CameoSim particularly in the 2D geometric simulation, in terms of `1-norm error. In the lower altitude scene, BOAEM performs better than both TIAEM and CameoSim by 22.0% and 16%. In a 3D scene (i.e. terrain with Digital Elevation Model (DEM)) with sensor at lower altitude CameoSim error raises by 5 times compared to GT. BOAEM still performs better than TIAEM by a similar 22% `1-norm error. • A means for assessing the shadowed pixels of the scene has been proposed and the validation of the model requires more comprehensive ground truth (GT) data which will be performed in the future research. Most of the above results have been published in three journal papers as part of the contributions towards the HSI research community