COMPARATIVE ASSESSMENT BETWEEN PER-PIXEL AND OBJECT-ORIENTED FOR MAPPING LAND COVER AND USE
| Autor: | Jerry Adriani Johann, Lucas Volochen Oldoni, Bruno Bonemberger da Silva, Victor Hugo Rohden Prudente, Erivelto Mercante |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Object-oriented programming
010504 meteorology & atmospheric sciences Pixel business.industry Computer science Agriculture (General) Decision tree Pattern recognition 04 agricultural and veterinary sciences Land cover Image segmentation data mining Object (computer science) 01 natural sciences Agricultural and Biological Sciences (miscellaneous) S1-972 C4.5 algorithm GeoDMA decision tree 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Point (geometry) Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | Engenharia Agrícola, Vol 37, Iss 5, Pp 1015-1027 (2017) Engenharia Agrícola v.37 n.5 2017 Engenharia Agrícola Associação Brasileira de Engenharia Agrícola (SBEA) instacron:SBEA Engenharia Agrícola, Volume: 37, Issue: 5, Pages: 1015-1027, Published: SEP 2017 |
| ISSN: | 0100-6916 |
| Popis: | The traditional per-pixel classification methods consider only spectral information, and may be limited. Object-based classifiers, however, also consider shape and texture, firstly segmenting the image, and then classifying individual objects. Thus, a Geographic Object-Based Image Analysis (GEOBIA) was compared in conjunction with data mining techniques and a traditional per-pixel method. A cut of Landsat-8, bands 2 to 7, orbit/point 223/77, located between the municipalities of Cascavel, Corbélia, Cafelândia and Tupãssi, in the west part of the state of Paraná, from 12/18/2013 was used. In the GEOBIA approach was realized image segmentation, spatial and spectral attribute extraction, and classification using the decision tree supervised algorithm, J48. For the per-pixel method, we used the supervised Maximum Likelihood Classifier. Both approaches presented equivalent results, with Kappa Index of 0.75 and Global Accuracy (GA) of 78.97% for the approach by GEOBIA and Kappa Index of 0.72 and GA of 77.44% for the perpixel classification. The classification by GEOBIA showed better accuracy for the soil, forest and soybean classes, and did not show the splash aspect, which visually improves the classification result. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|