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Siegel, H. J.

Publications and source records attributed to Siegel, H. J..

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Contextual classification on a CDC Flexible Processor system

A potential hardware organization for the Flexible Processor Array is presented. An algorithm that implements a contextual classifier for remote sensing data analysis is given, along with uniprocessor classification algorithms. The Flexible Processor algorithm is provided, as are simulated timings for contextual classifiers run on the Flexible Processor Array and another system. The timings are analyzed for context neighborhoods of sizes three and nine.

Smith, B. W.↗
NASA NTRS1981

Contextual classification on PASM

The use of N microprocessors in the SIMD mode of parallel processing to do classifications almost N times faster than a single microprocessor is discussed. Examples of contextual classifiers are given, uniprocessor algorithms for performing contextual classifications are presented, and their computational complexity is analyzed. The SIMD mode of parallel processing is defined and PASM is overviewed. The presented uniprocessor algorithms are used as a basis for developing parallel algorithms for performing computationally intensive contextual classifications.

Siegel, H. J.↗
NASA NTRS1980

Parallel processing implementations of a contextual classifier for multispectral remote sensing data

Contextual classifiers are being developed as a method to exploit the spatial/spectral context of a pixel to achieve accurate classification. Classification algorithms such as the contextual classifier typically require large amounts of computation time. One way to reduce the execution time of these tasks is through the use of parallelism. The applicability of the CDC flexible processor system and of a proposed multimicroprocessor system (PASM) for implementing contextual classifiers is examined.

Siegel, H. J.↗
NASA NTRS1980

Parallel processing implementations of a contextual classifier for multispectral remote sensing data

The 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.

Siegel, H. J.↗
NASA NTRS1979

A method for classifying multispectral remote sensing data using context

The paper describes a method of classifying multispectral remote sensing data using a context classifier. Because the computational requirements of the context classifier are very large, its implementation on parallel/pipelined multiprocessor systems is being investigated, so that the types of computations can be efficiently implemented. Special considerations necessary for such implementations are discussed, with particular reference to implementation on an array of Control Data Corporation Flexible Processors.

Swain, P. H.↗