A SEGMENT-BASED APPROACH TO CLASSIFY CROP TYPES IN AGRICULTURAL LANDS BY USING MULTI-TEMPORAL OPTICAL AND MICROWAVE IMAGES

  • Aslı Özdarıcı Ok
  • Zuhal Akyürek
Keywords: Agriculture, Multi-Temporal Image Classification, Segment-Based Approach, Kompsat-2, Envisat ASAR

Abstract

An automatic classification approach is performed to classify major crop types cultivated in Karacabey Plain, Bursa, through multi-temporal Kompsat-2 and Envisat ASAR data. First, the single-date pancromatic and multispectral Kompsat-2 images are fused with an appropriate image fusion method and 1m colour Kompsat-2 images are generated. Next, different parameter combinations are applied on the fused images in spatial and colour space to find out the optimum segmentation results. The optimum segments are then evaluated using multiple evaluation criteria. Two different classification approaches, pixel-based and segment-based, are tested in this study. First, Image classification are performed on the multispectral Kompsat-2 images. Then the Kompsat-2 images (4m) are classified with Envisat ASAR data. In this way contribution of the Envisat ASAR images to the classification accuracy are tested. Next, distance maps are produced for each thematic map to combine the information of multi-temporal images.The produced thematic maps are evaluated based on pixel-based and segment-based manner using confusion matrices. Results indicate that Envisat ASAR data improve the accuracy of thematic maps. The highest accuracies are obtained for the combined thematic maps of June-August and June-July-August (%88.71 overall accuracy and 0.86 kappa) computed for the segment-based approach.

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Published
2013-01-21
How to Cite
[1]
A. Özdarıcı Ok and Z. Akyürek, “A SEGMENT-BASED APPROACH TO CLASSIFY CROP TYPES IN AGRICULTURAL LANDS BY USING MULTI-TEMPORAL OPTICAL AND MICROWAVE IMAGES”, JAST, vol. 6, no. 1, pp. 31-43, Jan. 2013.
Section
Articles