By Antonio J. Plaza, Chein-I Chang
Solutions for Time-Critical distant Sensing Applications
The contemporary use of latest-generation sensors in airborne and satellite tv for pc structures is generating an almost continuous circulate of high-dimensional facts, which, in flip, is developing new processing demanding situations. to deal with the computational specifications of time-critical functions, researchers have began incorporating excessive functionality computing (HPC) types in distant sensing missions. High functionality Computing in distant Sensing is among the first volumes to discover state of the art HPC innovations within the context of distant sensing difficulties. It makes a speciality of the computational complexity of algorithms which are designed for parallel computing and processing.
A varied number of Parallel Computing recommendations and Architectures
The booklet first addresses key computing options and advancements in distant sensing. It additionally covers software components now not unavoidably with regards to distant sensing, resembling multimedia and video processing. every one next bankruptcy illustrates a particular parallel computing paradigm, together with multiprocessor (cluster-based) structures, large-scale and heterogeneous networks of desktops, grid computing systems, and really expert architectures for remotely sensed facts research and interpretation.
An Interdisciplinary discussion board to motivate Novel Ideas
The large stories of present and destiny advancements mixed with considerate views at the strength demanding situations of adapting HPC paradigms to distant sensing difficulties will definitely foster collaboration and improvement between many fields.
Read Online or Download High Performance Computing in Remote Sensing (Chapman & Hall Crc Computer & Information Science Series) PDF
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Additional info for High Performance Computing in Remote Sensing (Chapman & Hall Crc Computer & Information Science Series)
2 Parallel Implementations . . . . . . . . . . . . . . . . . . . . . 1 Cluster-Based Implementation of the PPI Algorithm . . . 2 Heterogeneous Implementation of the PPI Algorithm . . 3 FPGA-Based Implementation of the PPI Algorithm . . . 4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 1 High-Performance Computer Architectures . . . . . . . . . . . . 2 Hyperspectral Data .
2 Parallel Implementations . . . . . . . . . . . . . . . . . . . . . 1 Cluster-Based Implementation of the PPI Algorithm . . . 2 Heterogeneous Implementation of the PPI Algorithm . . 3 FPGA-Based Implementation of the PPI Algorithm . . . 4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 1 High-Performance Computer Architectures . . . . . . . . . . . . 2 Hyperspectral Data . . . . . .
3 provides an application case study: the well-known Pixel Purity Index (PPI) algorithm , which has been widely used to analyze hyperspectral images and is available in commercial software. The algorithm is first briefly described and several issues encountered in its implementation are discussed. Then, we provide HPC implementations of the algorithm, including a cluster-based parallel version, a variation of this version specifically tuned for heterogeneous computing environments, and an FPGA-based implementation.
High Performance Computing in Remote Sensing (Chapman & Hall Crc Computer & Information Science Series) by Antonio J. Plaza, Chein-I Chang