A segment-based approach to classify agricultural lands by using multi-temporal optical and microwave data

Abstract

This research study aims to classify crop diversity in agricultural land with a segment-based approach using multi-temporal Kompsat-2 and Environmental Satellite (Envisat) advanced synthetic aperture radar (ASAR) data acquired in June, July and August on Karacabey Plain, Turkey. Analyses start with the image segmentation process applied to the fused optical images to search homogenous objects. The segmentation outputs are evaluated using multiple goodness measures, which take into consideration area and location similarities. Image classifications are performed on each multispectral (MS) single date image. In order to combine the most probable classes of the thematic maps, distance maps are generated. Evaluations of the thematic maps are performed through confusion matrices based on pixel-based and segment-based approaches. The results indicate that the highest overall accuracy of 88.71% and a kappa result of 0.86 are provided for the segment-based approach of the combined thematic map along with the microwave data, which is around 10% higher than the related pixel-based results.

Acknowledgements

The Kompsat-2 data were provided by a project called DAP-2008-07-02-07, funded by the Geodetic and Geographic Information Technologies (GGIT) Department of the Middle East Technical University (METU) in Turkey. The Envisat ASAR data were supplied by a Category-1 ESA project (Project No: 4825). This study was also supported for six months in 2010 by the Scientific and Technological Research Council of Turkey. We thank Assoc. Prof. Dr Lutfi Suzen (Department of Geological Engineering at METU) and Assistant Prof. Dr Ilkay Ulusoy (Department of Electrical and Electronics Engineering at METU) for their valuable comments during the study and our colleagues Ali Ozgun Ok and Resat Gecen who contributed to the fieldwork of the study. We thank the farmers and staff working in the irrigation department of the region for contributing to our study with respect to the field observations. Finally, we thank two anonymous reviewers for their valuable contributions to improve the study.