Sensitivity Analysis (SA)

1. Rrs Forward Model Inversion Model Has to be the same or approx. ={0.05, 0.05, 0.01, 0.4, 1.5} Clear Waters ={… … …} Optimization, Inversion method (2) Synthetic Vector (3) ={0.05, 0.2, 0.001, 0.1, 1} F o M r o w d a e r l d Forward Model ={0.05, 0.05, 0.01, 0.4, 1.5} (2) (3) I M n o v d e e r l s i o n Inversion Model Simlab FAST Method Has to be the same or approx. θ ={… … …} γ ={…….} Optimization, Inversion method ={0.05, 0.2, 0.001, 0.1, 1} SA Utilizing High-Performance Computing to Investigate Performance and Sensitivity of an Inversion Model for Hyperspectral Remote Sensing of Shallow Coral Ecosystems Carolina Gerardino (UPRM)Dr. James Goodman (UPRM), Dr. Wilson Rivera (UPRM)carolina.gerardino@ece.uprm.edu, jgoodman@uprm.edu, wrivera@ece.uprm.edu This work was supported in part by CenSSIS, the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number ECE-9986821). Abstract Working with Synthetic Vector Future Research This poster describes the implementation of a semi-analytical inversion model within a parallel processing framework. Preliminary results of the implementation in a C++/OOL, C++/MPI and the IDL/ENVI environment using a synthetic vector are presented. The C++/OOL implementation provides both the foundation for assessing real-time processing capabilities as well as the computation power necessary for addressing complex optimization and sensitivity questions. Sensitivity Analysis (SA) SA studies the effect of changes in model assumptions (Nuisance Parameters) on a given output (Parameters of interest)[9]. For the SA, the objective function is redefined in function of two types of parameters: Nuisance parameters (γ) and Parameters of interest(θ). Sensitivity Analysis State of the Art • Applications to simultaneously classify water properties, bathymetry and benthic composition using hyperspectral remote sensing was previously demonstrated using AVIRIS imagery from Hawaii [1]. At the core of this approach was the application of a semi-analytical inversion model for simultaneously extracting bathymetry and water property parameters. • The semi-analytical inversion model employs a non-linear optimization routine to retrieve estimates of bathymetry and water properties, the algorithm is based on quasi-single-scattering theory, and was developed utilizing Hydrolight (Sequoia Scientific Inc.) simulations to populate parameters of the semi-analytical model [2]. • The advantages of utilizing high performance computing resources to solve hyperspectral imaging problems (CPU time and memory capacity) has been previously demonstrated [3,4,7]. Parameters of interest • The semi-analytical model has been implemented in C++ using the Message Passing Interface(MPI) [5. • Additionally, for solving the constrained nonlinear optimization problem were tested different optimization libraries: • ConminC++ Library [6][7] • OOL (Open Optimization Library) [8] Nuisance Parameters Experimental Results Significance Remote sensing is increasingly being employed as a significant component in the evaluation and management of coral ecosystems (visual overview, and the quantitative abilities for systematic assessment and monitoring). Hyperspectral instruments provide much greater spectral detail, and thus an improved ability to extract multiple layers of information from the spectrally complex environment associated with coral reefs and other shallow costal subsurface environments. Implementing hyperspectral algorithms into parallel computing frameworks provides both the foundation for assessing real-time processing capabilities as well as the computational power necessary for addressing complex optimization and sensitivity questions. Value Added to CenSSIS Figure 2. Error of the different optimization routine References Model Implementation • Goodman, J.A. (2004). Hyperspectral remote sensing of coral reefs: deriving bathymetry, aquatic optical properties and a benthic spectral unmixing classification using AVIRIS data in the Hawaiian Islands. PhD Dissertation, University of California, Davis. • Lee, Z.P., K. Carder, C.D. Mobley, R. Steward, and J. Patch (1998). Hyperspectral remote sensing for shallow waters. 1. a semi-analytical model. Applied Optics, 37, 6329-6338. • Lugo-Beauchamp, W., C. Carvajal-Jimenez and W. Rivera (2004). Performance of Hyperspectral Imaging Algorithms on IA-64. Proc. IASTED International Conference on Circuits, Signals, and Systems, p. 327-332. • Hawick, K.A. and H. A. James (1997).  Distributed high-performance computing for remote sensing. Proceedings of the ACM/IEEE Conference on Supercomputing. • Snir, M., S. Otto, S. Huss-Lederman, D. Walker and J. Dongarra (1998). MPI–The Complete Reference. Volume 1 - The MPI-1 Core, 2nd edition. The MIT Press. • NASA (1978). Conmin User’s Manual. NASA Technical Memorandum X-62282. • C. Gerardino, Y. Rivera, W. Rivera, and J. A. Goodman (2006), Parallel implementation of an inversion model for hyperspectral remote sensing. 49th IEEE International Midwest Symposium on Circuits and Systems(submitted). • http://ool.sourceforge.net/ OOL (Open Optimization Library) • Saltelli, A., Tarantola S. Campolongo F. and Rato M. (2004), “Sensitivity Analysis in Practice A Guide to Assessing Scientific Models ”. John Wiley & Sons. • A semi-analytical inversion model [2,3] is independently applied to each pixel of a hyperspectral image, finding the parameters that minimize the error of the objective function, obj (1). These parameters are: • P: Phytoplankton absorption at 440nm (m-1) • G: Gelbstoff/detritus absorption at 440nm (m-1) • BP: Absorption coefficient for particle back- • scattering, view angle and sea state (m-1) • B:Bottom albedo at 550nm • H:Water depth (m) • The inversion model is a function of both rrs, subsurface remote sensing reflectance(2), and Rrs, above surface remote sensing reflectance (3). Figure 3. Error of the different optimization routine for parameter BP (3) (1) Figure 2. Parallel execution times of Kaneohe Bay hyperspectral image with OOL.