ABSTRACT
Wildland Fires as a Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 8.3 Mathematical Model for Wildland Fire Characterization . . . . . . . . . . . . . . . . . 156 8.4 Advanced Hyperspectral Data Processing Algorithms . . . . . . . . . . . . . . . . . . . 157
8.4.1 Morphological Algorithm for Endmember Extraction and Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
8.4.2 Orthogonal Subspace Projection Algorithm for Target Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
8.4.3 Self-Organizing Map for Neural Network Classification . . . . . . . . . . 161 8.4.4 Spectral Mixture Analysis Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
8.5 Parallel Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 8.5.1 Parallelization of Automated Morphological Classification
(AMC) Algorithm for Homogeneous Clusters . . . . . . . . . . . . . . . . . . . 163 8.5.2 Parallelization of Automated Morphological Classification
(AMC) Algorithm for Heterogeneous Clusters . . . . . . . . . . . . . . . . . . . 166 8.5.3 Parallelization of the SOM-based Classification Algorithm
for Homogeneous Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
8.6 Architectural Outline of an Advanced System for Management of Wildland Fires Using Hyperspectral Imagery . . . . . . . . . . . 169 8.6.1 Homogeneous Parallel Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 8.6.2 Heterogenous Parallel Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 8.6.3 Programmable Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
8.7 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 8.7.1 Parallel Computer Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 8.7.2 Hyperspectral Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 8.7.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
8.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 8.9 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Predicting the potential behavior and effects of wildland fires using remote sensing technology is a long-awaited goal. The role of high-performance computing in this task is essential since fire phenomena often require a response in (near) real-time. Several studies have focused on the potential of hyperspectral imaging as a baseline technology to detect and monitor wildland fires by taking advantage of the rich spectral information provided by imaging spectrometers. The propagation of fires is a very complex process that calls for the integrated use of advanced processing algorithms and mathematical models in order to explain and further characterize the process. In this chapter, we describe several advanced hyperspectral data processing algorithms that are shown to be useful in the task of detecting/tracking wildland fires and further study how such algorithms can be integrated with mathematical models, with the ultimate goal of designing an integrated system for surveillance and monitoring of fires.
