Description
Book SynopsisSolutions for Time-Critical Remote Sensing Applications
The recent use of latest-generation sensors in airborne and satellite platforms is producing a nearly continual stream of high-dimensional data, which, in turn, is creating new processing challenges. To address the computational requirements of time-critical applications, researchers have begun incorporating high performance computing (HPC) models in remote sensing missions. High Performance Computing in Remote Sensing is one of the first volumes to explore state-of-the-art HPC techniques in the context of remote sensing problems. It focuses on the computational complexity of algorithms that are designed for parallel computing and processing.
A Diverse Collection of Parallel Computing Techniques and Architectures
The book first addresses key computing concepts and developments in remote sensing. It also covers application areas not necessarily related to remote sensing, such as multimedia and video processing. Each subsequent chapter illustrates a specific parallel computing paradigm, including multiprocessor (cluster-based) systems, large-scale and heterogeneous networks of computers, grid computing platforms, and specialized hardware architectures for remotely sensed data analysis and interpretation.
An Interdisciplinary Forum to Encourage Novel Ideas
The extensive reviews of current and future developments combined with thoughtful perspectives on the potential challenges of adapting HPC paradigms to remote sensing problems will undoubtedly foster collaboration and development among many fields.
Table of ContentsPreface. High Performance Computing Architectures for Remote Sensing Data Analysis: Overview and Case Study. Computer Architectures for Multimedia and Video Analysis. Parallel Implementation of the ORASIS Algorithm for Remote Sensing Data Analysis. Parallel Implementation of the Recursive Approximation of an Unsupervised Hierarchical Segmentation Algorithm. Computing for Analysis and Modeling of Hyperspectral Imagery. Parallel Implementation of Morphological Neural Networks for Hyperspectral Image Analysis. Parallel Wildland Fire Monitoring and Tracking Using Remotely Sensed Data. An Introduction to Grids for Remote Sensing Applications. Remote Sensing Grids: Architecture and Implementation. Open Grid Services for Envisat and Earth Observation Applications. Design and Implementation of a Grid Computing Environment for Remote Sensing. A Solutionware for Hyperspectral Image Processing and Analysis. AVIRIS and Related 21st-Century Imaging Spectrometers for Earth and Space Science. Remote Sensing and High Performance Reconfigurable Computing Systems. FPGA Design for Real-Time Implementation of Constrained Energy Minimization for Hyperspectral Target Detection. Real-Time Online Processing of Hyperspectral Imagery for Target Detection and Discrimination. Real-Time On-Board Hyperspectral Image Processing Using Programmable Graphics Hardware. Index.
