PARCEL-BASED CROP MAPPING THROUGH MULTI-TEMPORAL MASKING CLASSIFICATION OF LANDSAT 7 IMAGES IN KARACABEY, TURKEY

摘要

This study describes the parcel-based classification of agricultural crops using multi-date Landsat 7 ETM+ images acquired in May, July, and August 2000. The study area is located in North-West of Turkey with an area of about 170 km~(2) and grows a variety of crops. The objective was to map the summer (August) crops within the agricultural land parcels. The classification methodology is based on a multi-temporal masking of Landsat 7 ETM+ images. First, a supervised per-pixel classification of the three images (May, July, and August 2000) was performed using a maximum likelihood classifier algorithm. The accuracy of classified outputs was computed by comparing them with the ground truth information. Those classes that meet the threshold values were masked out and the August image was re-classified using the unmasked classes only, excluding the masked fields from the classification. The masking technique was applied to overcome the problems caused by the spectral overlaps between the information classes. After completing the classification process, the multi-temporal classified output of the August image was analyzed in a field specific manner in the integration of remote sensing and geographic information system (GIS). In each parcel, the percentages of classified pixels were computed and the modal class label was assigned to the parcel. The analysis results were fed back to a GIS database for immediate update. The resulting classification accuracy of the multi-temporal masking technique was 81percent, which was 10percent more accurate than the classification of the August image only.