An approach for the automatic detection of agricultural field sub-boundaries from high resolution satellite images
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Agricultural crop mapping is quite important for crop yield estimation in regional and national scale. Remote sensing images are popular data to identify and classify land cover types in the agricultural areas. The recent image classification techniques for agricultural areas use approaches which work on field-by-field basis by means of assigning a crop label for each agricultural field individually. In field-based classification approaches, the classification is performed within the permanent agricultural field boundaries that are stored in a geographical information system (GIS) as vector polygons. However, crop variation within the fields is an important problem to be solved. To solve this problem, image segmentation is needed to be executed to extract the sub-boundaries within the permanent boundaries of the fields and subsequently to achieve higher accuracy in field-based classification operations.