Image Classification Algorithm
This is a sophisticated program that uses statistical techniques to discriminate between land cover types from remotely sensed imagery (i.e. determining if an area is a forest or wetland using reflectance values).
Image Classification
Four change detection techniques were evaluated in this study. The first, spectral-temporal change classification (S-TCC), is based on unsupervised classification of the spectral data for the two dates.
Either image classification methods are performed in two steps. The first step in an unsupervised image classification is performed by i.cluster; the first step in a supervised classification is executed by the GRASS program i.class.
Superior image classification and analysis
11-bit digitization (up to 2,048 levels of gray scale)
Discrete non-overlapping spectral bands ...
New color image classification function for 8-bit palette image. Now, both 8-bit and 24-bit color image can be classified using R2V's power unsupervised classification function to extract and separate color classes.
PIT's primary use is in image classification. Both unsupervised and supervised programs are included.
We will use a very cool program named MultiSpec from Purdue University. This program has many easy to use image classification functions, and best of all, it is FREE! ...
Particularly promising is the potential for applying quantitative shape analysis techniques in the areas of digital image classification and wildlife habitat modeling.
Remote sensing software makes use of image data for image classification and processing. Typically, this data must be converted into a raster format (and perhaps vector) to be used analytically with the GIS.
In vector spatial database, a variety of smoothing algorithms used to reduce file size by removing excessive turning points along a linear feature.
In raster databases, a mathematically defined operation used in image classification that removes ...
 See also: Image, Classification, Class, Cover, Map
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