Parallel processing implementations of a contextual classifier for multispectral remote sensing dataThe applicability of parallel processing schemes to the implementation of a contextual classification algorithm which exploits the spatial and spectral context of a multispectral remote sensing pixel to achieve classification is examined. Two algorithms for classifying each multivariate pixel taking into account the probable classifications of neighboring pixels are presented which make use of a size three horizontally linear neighborhood, and the serial computational complexity of the more efficient algorithm is shown to grow in proportion to the number of pixels and the cube of the number of possible categories. The implementation of the more efficient algorithm on a CDC Flexible Processor system and on a multimicroprocessor system such as the proposed PASM is then discussed. It is noted that the use of N processors to perform the calculations N times faster than a single processor overcomes the principal disadvantage of contexual classifiers, i.e., their computational complexity.
Document ID
19810061624
Acquisition Source
Legacy CDMS
Document Type
Conference Proceedings
Authors
Siegel, H. J. (Purdue Univ. West Lafayette, IN, United States)
Swain, P. H. (Purdue Univ. West Lafayette, IN, United States)
Smith, B. W. (Purdue University West Lafayette, IN, United States)
Date Acquired
August 11, 2013
Publication Date
January 1, 1980
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: Annual Symposium on Machine processing of remotely sensed data and soil information systems and remote sensing and soil survey