Agriculture has an important role in Turkey / hence automated approaches are crucial to
maintain sustainability of agricultural activities. The objective of this research is to
classify eight crop types cultivated in Karacabey Plain located in the north-west of
Turkey using multi-temporal Kompsat-2 and Envisat ASAR satellite data. To fulfill this
objective, first, the fused Kompsat-2 images were segmented separately to define
homogenous agricultural patches. The segmentation results were evaluated using multiple
goodness measures to find the optimum segments. Next, multispectral single-date
Kompsat-2 images with the Envisat ASAR data were classified by MLC and SVMs
algorithms. To combine the thematic information of the multi-temporal data set,
probability maps were generated for each classification result and the accuracies of the
thematic maps were then evaluated using segment-based manner. The results indicated
that the segment-based approach based on the SVMs method using the multispectral
Kompsat-2 and Envisat ASAR data provided the best classification accuracies. The
combined thematic maps of June-August and June-July-August provided the highest
overall accuracy and kappa value around 92% and 0.90, respectively, which was 4%
better than the highest result computed with the MLC method. The produced thematic
maps were also evaluated based on field-based manner and the analysis revealed that the
classification performances are directly proportional to the size of the agricultural fields.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12614195/index.pdf |
| Date | 01 February 2012 |
| Creators | Ozdarici Ok, Asli |
| Contributors | Akyurek, Zuhal |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | Access forbidden for 1 year |
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