Clasificación espectral automática vs. clasificación visual: Un ejemplo al sur de la ciudad de México
Using a Maximum Likelihood algorithm a Landsat TM image was classified by both supervised and non–supervised approaches. In the first case, 12 classes were obtained based on 30 samples; the non–supervised procedure yielded 30 classes. Once grouped, both classifications considered 6 classes. Addition...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universidad Nacional Autónoma de México
1994-06-01
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| Series: | Investigaciones Geográficas |
| Online Access: | http://www.investigacionesgeograficas.unam.mx/index.php/rig/article/view/59028 |
| Summary: | Using a Maximum Likelihood algorithm a Landsat TM image was classified by both supervised and non–supervised approaches. In the first case, 12 classes were obtained based on 30 samples; the non–supervised procedure yielded 30 classes. Once grouped, both classifications considered 6 classes. Additionally, color composites were prepared and visually interpreted. The three products were compared in a GIS environment using a regularly distributed network of points refering the field truth. The results show that the lowest error correspond to the supervised classification (82.32% exactitude), followed by the visual interpretation (78.72%) and the non–supervised procedure (73.18%). These figures were obtained after grouping the classes according to their similarities. |
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| ISSN: | 0188-4611 2448-7279 |