Computer Vision, Graphics, and Image Processing
Maximum likelihood unsupervised textured image segmentation
CVGIP: Graphical Models and Image Processing
Geostatistical classification for remote sensing: an introduction
Computers & Geosciences
Computing geostatistical image texture for remotely sensed data classification
Computers & Geosciences
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
A survey of image classification methods and techniques for improving classification performance
International Journal of Remote Sensing
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A supervised classification scheme to segment optical multi-spectral images has been developed. In this classifier, an automated region-growth algorithm delineates the training sets. This algorithm handles three parameters: an initial pixel seed, a window size and a threshold for each class. A suitable pixel seed is manually implanted through visual inspection of the image classes. The best value for the window and the threshold are obtained from a spectral distance and heuristic criteria. This distance is calculated from a mathematical model of spectral separability. A pixel is incorporated into a region if a spectral homogeneity criterion is satisfied in the pixel-centered window for a given threshold. The homogeneity criterion is obtained from the model of spectral distance. The set of pixels forming a region represents a statistically valid sample of a defined class signaled by the initial pixel seed. The grown regions therefore constitute suitable training sets for each class. Comparing the statistical behavior of the pixel population of a sliding window with that of each class performs the classification. For region-growth, a window size is employed for each class. For classification, the centered pixel of the sliding window is labeled as belonging to a class if its spectral distance is a minimum to the class. The window size used for classification is a function of the best separability between the classes. A series of examples, employing synthetic and satellite images are presented to show the value of this classifier. The goodness of the segmentation is evaluated by means of the k coefficient and a visual inspection of the results.