'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Cataloged from PDF version of article.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 linear strips of woody vegetation that include
hedgerows and riparian vegetation that are important elements of
the landscape ecology and biodiversity. The proposed framework
exploits the spectral, textural, and shape properties of objects
using hierarchical feature extraction and decision-making steps.
First, a multifeature and multiscale strategy is used to be able
to cover different characteristics of these objects in a wide range
of landscapes. Discriminant functions trained on combinations of
spectral and textural features are used to select the pixels that may
belong to candidate objects. Then, a shape analysis step employs
morphological top-hat transforms to locate the woody vegetation
areas that fall within the width limits of an acceptable object,
and a skeletonization and iterative least-squares fitting procedure
quantifies the linearity of the objects using the uniformity of the
estimated radii along the skeleton points. Extensive experiments
using QuickBird imagery from three European Union member
states show that the proposed algorithms provide good localization
of the target objects in a wide range of landscapes with very
different characteristics