Zusammenfassung
The continuously improving spatial resolution of remote sensing sensors
sets new demand for applications utilizing this information. The
need for the more efficient extraction of information from high resolution
RS imagery and the seamless integration of this information into
Geographic Information System (GIS) databases is driving geo-information
theory, and methodology, into new territory. As the dimension of
the ground instantaneous field of view (GIFOV), or pixel size, decreases
many more fine landscape features can be readily delineated, at least
visually. The challenge has been to produce proven man-machine methods
that externalize and improve on human interpretation skills. Some
of the most promising results come from the adoption of image segmentation
algorithms and the development of so-called object-based classification
methodologies. This paper builds on a discussion of different approaches
to image segmentation techniques and demonstrates through several
applications how segmentation and object-based methods improve on
pixel-based image analysis/classification methods. In contrast to
pixel-based procedure, image objects can carry many more attributes
than only spectral information. In this paper, I address the concepts
of object-based image processing, and present an approach that integrates
the concept of object-based processing into the image classification
process. Object-based processing not only considers contextual information
but also information about the shape of and the spatial relations
between the image regions.
- analysis,
- based
- classification,
- contextual
- database,
- databases,
- extraction,
- field
- geo
- geographic
- ground
- high
- human
- image
- imagery,
- information
- instantaneous
- interpretation
- machine
- man
- methods,
- object
- of
- pixel
- processing,
- remote
- resolution
- segmentation,
- sensing
- sensing,
- sensors,
- skills,
- spatial
- system
- systems,
- theory,
- view,
- visual
Nutzer