Purdue University Purdue e-Pubs LARS Technical Reports Laboratory for Applications of Remote Sensing 1-1-1980 Parallel Processing Implementations of a Contextual Classifier for Multispectral Remote Sensing Data Howard J. Siegel Philip H. Swain Bradley W. Smith Follow this and additional works at: http://docs.lib.purdue.edu/larstech Siegel, Howard J.; Swain, Philip H.; and Smith, Bradley W., "Parallel Processing Implementations of a Contextual Classifier for Multispectral Remote Sensing Data" (1980). LARS Technical Reports. Paper 51. http://docs.lib.purdue.edu/larstech/51 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact
[email protected] for additional information. PARALLEL PROCESSING IMPLEMENTATIONS 060380 OF A CONTEXTUAL CLASSIFIER FOR MULTISPECTRAL REMOTE SENSING DATA HOWARD JAY SIEGEL J PHILIP H. SWAIN J AND BRADLEY W. SMITH Purdue University ABSTRACT pIer algorithms used for remote sensing data analysis) typically require large a Contextual classifiers are being de mounts of computation time. One way to veloped as a method to exploit the spati reduce the execution time of these tasks al/spectral context of a pixel to achieve is through the use of parallelism. Vari accurate classification. Classification ous parallel processing systems that can algorithms such as the contextual classi be used for remote sensing have been fier typically require large amounts of built or proposed. The Control Data Cor computation time. One way to reduce the poration Flexible Processor system is a execution time of these tasks is through commercially available multiprocessor sys the use of parallelism. The applicability tem which has been recommended for use in of the CDC Flexible Processor system and remote sensing [4,5].