A segment-based approach to classify agricultural lands using multi-temporal kompsat-2 and envisat asar data

Özdarıcı Ok, Aslı
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.