An Approach for the Automatic Segmentation of High Resolution Satellite Images into Agricultural Fields
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Monitoring and management of land use plays an important role in the economic development of agriculture regions in the world. Satellite images have become very popular data source for crop mapping in agricultural areas. With this respect, the extraction of agricultural land parcels is critical for crop classification and further field-based analysis operations. An efficient image analysis procedure is needed to automatically extract agricultural fields with the minimum user intervention. When performed on field-by-field basis, agricultural crop classification produces much better results. Field-based image classification techniques use approaches that assign crop labels for the agricultural fields individually. The classification is performed within permanent field boundaries which are stored in a geographic information system (GIS) database as vector dataset.In this study, a field-based segmentation approach is proposed to extract sub-fields within permanent boundaries of agricultural fields from high resolution remotely sensed imagery.