Classifying multispectral data by neural networks
| Autor: | Harold H. Szu, Richard K. Kiang, Brian A. Telfer |
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| Rok vydání: | 1993 |
| Předmět: |
Pixel
Artificial neural network Computer Networks and Communications business.industry Computer science Pattern recognition computer.software_genre Subpixel rendering Synthetic data Least mean squares filter Thematic Mapper Benchmark (computing) Data mining Artificial intelligence Electrical and Electronic Engineering business computer Energy (signal processing) |
| Zdroj: | Telematics and Informatics. 10:209-222 |
| ISSN: | 0736-5853 |
| DOI: | 10.1016/0736-5853(93)90026-z |
| Popis: | Several energy functions for synthesizing neural networks are tested on 2-D synthetic data and on Landsat-4 Thematic Mapper data. These new energy functions, designed specifically for minimizing misclassification error, in some cases yield significant improvements in classification accuracy over the standard least mean squares energy function. In addition to operating on networks with one output unit per class, a new energy function is tested for binary encoded outputs, which result in smaller network sizes. The Thematic Mapper data (four bands were used) is classified on a single pixel basis, to provide a starting benchmark against which further improvements will be measured. Improvements are underway to make use of both subpixel and superpixel (i.e. contextual or neighborhood) information in tile processing. For single pixel classification, the best neural network result is 78.7 percent, compared with 71.7 percent for a classical nearest neighbor classifier. The 78.7 percent result also improves on several earlier neural network results on this data. |
| Databáze: | OpenAIRE |
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