tailieunhanh - High Performance Computing in Remote Sensing - Chapter 7

Dịch từ: Tiếng Anh Nhiều cơ quan quốc tế và Tổ chức nghiên cứu được Hiện nay Cống hiến cho sự phân tích và giải thích các dữ liệu hình ảnh thu chiều cao trên khu vực của trái đất [1]. Ví dụ, NASA IS liên tục thu thập hình ảnh hyperspectral Dù bằng cách sử dụng quang phổ kế của Phòng thí nghiệm Jet Propulsion của hình ảnh nhìn thấy được-Hồng ngoại (AVIRIS) [2], nào các biện pháp phản xạ trong phạm vi bước sóng từ 0,4-2,5 micron bằng cách sử dụng 224 kênh quang phổ ở độ phân giải quang phổ của 10 nm | Chapter 7 Parallel Implementation of Morphological Neural Networks for Hyperspectral Image Analysis Javier Plaza University of Extremadura Spain Rosa Perez University of Extremadura Spain Antonio Plaza University of Extremadura Spain Pablo Martinez University of Extremadura Spain David Valencia University of Extremadura Spain Contents Introduction . 132 Parallel Morphological Neural Network Algorithm. 134 Parallel Morphological Algorithm . 134 Parallel Neural Algorithm. 137 Experimental Results . 140 Performance Evaluation Framework. 140 Hyperspectral Data Sets. 142 Assessment of the Parallel Algorithm. 144 Conclusions and Future Research. 148 Acknowledgment . 149 References . 149 Improvement of spatial and spectral resolution in latest-generation Earth observation instruments is introducing extremely high computational requirements in many remote sensing applications. While thematic classification applications have greatly benefited from this increasing amount of information new computational requirements have been introduced in particular for hyperspectral image data sets with 131 2008 by Taylor Francis Group LLC 132 High-Performance Computing in Remote Sensing hundreds of spectral channels and very fine spatial resolution. Low-cost parallel computing architectures such as heterogeneous networks of computers have quickly become a standard tool of choice for dealing with the massive amount of image data sets. In this chapter a new parallel classification algorithm for hyperspectral imagery based on morphological neural networks is presented and discussed. The parallel algorithm is mapped onto heterogeneous and homogeneous parallel platforms using a hybrid partitioning scheme. In order to test the accuracy and parallel performance of the proposed approach we have used two networks of workstations distributed among different locations and also a massively parallel Beowulf cluster at NASA s Goddard Space Flight Center