Summary: AUTOMATIC DETECTION OF HEDGES AND ORCHARDS USING VERY HIGH
SPATIAL RESOLUTION IMAGERY
Department of Computer Engineering, Bilkent University, Bilkent, 06800, Ankara, Turkey
KEY WORDS: Object recognition, texture analysis, shape analysis, image classification
Automatic mapping and monitoring of agricultural landscapes using remotely sensed imagery has been an important research problem.
This paper describes our work on developing automatic methods for the detection of target landscape features in very high spatial
resolution images. The target objects of interest consist of hedges that are linear strips of woody vegetation and orchards that are
composed of regular plantation of individual trees. We employ spectral, textural, and shape information in a multi-scale framework for
automatic detection of these objects. Extensive experiments show that the proposed algorithms provide good localization of the target
objects in a wide range of landscapes with very different characteristics.
Several EU Member States have defined various regulations for
the planning, control, maintenance, and monitoring of agricul-
tural sites as part of the EU Common Agricultural Policy. Remote
sensing has long been acknowledged as an important tool for the
classification of land cover and land use, and provides potentially
effective and efficient solutions for the implementation of such