Summary: Spatial Techniques for Image Classification
Department of Computer Engineering
Bilkent, 06800, Ankara, Turkey
The constant increase in the amount and resolution of remotely sensed imagery necessitates
development of intelligent systems for automatic processing and classification. We describe a
Bayesian framework that uses spatial information for classification of high-resolution images.
First, spectral and textural features are extracted for each pixel. Then, these features are
quantized and are used to train Bayesian classifiers with discrete non-parametric density models.
Next, an iterative split-and-merge algorithm is used to convert the pixel level classification
maps into contiguous regions. Then, the resulting regions are modeled using the statistical
summaries of their spectral, textural and shape properties, and are used with Bayesian classifiers
to compute the final classification maps. Experiments with three ground truth data sets show
the effectiveness of the proposed approach over traditional techniques that do not make strong
use of region-based spatial information.
The amount of image data that is received from satellites is constantly increasing. For example,