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 flow of high-dimensional info, which, in flip, is growing new processing demanding situations. to handle the computational requisites of time-critical functions, researchers have all started 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 options within the context of distant sensing difficulties. It specializes in the computational complexity of algorithms which are designed for parallel computing and processing.

*A assorted choice of Parallel Computing innovations and Architectures *

The publication first addresses key computing thoughts and advancements in distant sensing. It additionally covers program components now not unavoidably relating to distant sensing, corresponding to multimedia and video processing. each one next bankruptcy illustrates a selected parallel computing paradigm, together with multiprocessor (cluster-based) structures, large-scale and heterogeneous networks of pcs, grid computing systems, and really expert architectures for remotely sensed information research and interpretation.

*An Interdisciplinary discussion board to motivate Novel Ideas*

The huge studies of present and destiny advancements mixed with considerate views at the capability demanding situations of adapting HPC paradigms to distant sensing difficulties will surely foster collaboration and improvement between many fields.

**Read or Download High Performance Computing in Remote Sensing (Chapman & Hall Crc Computer & Information Science Series) PDF**

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**Extra info for High Performance Computing in Remote Sensing (Chapman & Hall Crc Computer & Information Science Series)**

**Example text**

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 . . . . .

One of the main advantages of systolic array-based implementations is that they are able to provide a systematic procedure for system design that allows for the derivation of a well-defined processing element-based structure and an interconnection pattern that can then be easily ported to real hardware configurations. Using this procedure, we can also calculate the data dependencies prior to the design, and in a very straightforward manner. Our proposed design intends to maximize computational power of the hardware and minimize the cost of communications.

2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Evolution of Cluster Computing in Remote Sensing . . . . . . . . 2 Heterogeneous Computing in Remote Sensing . . . . . . . . . . 3 Specialized Hardware for Onboard Data Processing . . . . . . . . 3 Case Study: Pixel Purity Index (PPI) Algorithm . . . . . . . . . . . . . 1 Algorithm Description . . . . . . .