Object-based contextual image classification built on image segmentation

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.