Multilayer Perceptron for analyzing satellite data
Different ANN architectures of MLP have been trained by BP and used to analyze Landsat TM images. Two different approaches have been applied for training: an ordinary approach (for one hidden layer M-H1-L & two hidden layers M-H1-H2-L) and one-against-all strategy (for one hidden layer (M-H1-1)...
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| Format: | Article |
| Language: | English |
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University of Baghdad
2011-12-01
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| Series: | Iraqi Journal of Physics |
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| Online Access: | https://ijp.uobaghdad.edu.iq/index.php/physics/article/view/784 |
| _version_ | 1827990834582126592 |
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| author | Raed Shadfan |
| author_facet | Raed Shadfan |
| author_sort | Raed Shadfan |
| collection | DOAJ |
| description |
Different ANN architectures of MLP have been trained by BP and used to analyze Landsat TM images. Two different approaches have been applied for training: an ordinary approach (for one hidden layer M-H1-L & two hidden layers M-H1-H2-L) and one-against-all strategy (for one hidden layer (M-H1-1)xL, & two hidden layers (M-H1-H2-1)xL). Classification accuracy up to 90% has been achieved using one-against-all strategy with two hidden layers architecture. The performance of one-against-all approach is slightly better than the ordinary approach
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| first_indexed | 2024-04-10T00:41:33Z |
| format | Article |
| id | doaj.art-34457b034e944efd9797ec8e4a97889a |
| institution | Directory Open Access Journal |
| issn | 2070-4003 2664-5548 |
| language | English |
| last_indexed | 2024-04-10T00:41:33Z |
| publishDate | 2011-12-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Iraqi Journal of Physics |
| spelling | doaj.art-34457b034e944efd9797ec8e4a97889a2023-03-14T05:46:53ZengUniversity of BaghdadIraqi Journal of Physics2070-40032664-55482011-12-01916Multilayer Perceptron for analyzing satellite dataRaed Shadfan0Department of Computer Science, Faculty of Information Technology, Petra University Different ANN architectures of MLP have been trained by BP and used to analyze Landsat TM images. Two different approaches have been applied for training: an ordinary approach (for one hidden layer M-H1-L & two hidden layers M-H1-H2-L) and one-against-all strategy (for one hidden layer (M-H1-1)xL, & two hidden layers (M-H1-H2-1)xL). Classification accuracy up to 90% has been achieved using one-against-all strategy with two hidden layers architecture. The performance of one-against-all approach is slightly better than the ordinary approach https://ijp.uobaghdad.edu.iq/index.php/physics/article/view/784MLP, Landsat, Classification |
| spellingShingle | Raed Shadfan Multilayer Perceptron for analyzing satellite data Iraqi Journal of Physics MLP, Landsat, Classification |
| title | Multilayer Perceptron for analyzing satellite data |
| title_full | Multilayer Perceptron for analyzing satellite data |
| title_fullStr | Multilayer Perceptron for analyzing satellite data |
| title_full_unstemmed | Multilayer Perceptron for analyzing satellite data |
| title_short | Multilayer Perceptron for analyzing satellite data |
| title_sort | multilayer perceptron for analyzing satellite data |
| topic | MLP, Landsat, Classification |
| url | https://ijp.uobaghdad.edu.iq/index.php/physics/article/view/784 |
| work_keys_str_mv | AT raedshadfan multilayerperceptronforanalyzingsatellitedata |