A classification method with a spatial-spectral variability

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Abstract
A classification method which takes into account not only spectral information but also spatial information is proposed for high-spatial-resolution multi-spectral scanner data such as Landsat TM and SPOT HRV data. Such a spatial feature can be used with spectral features in a unified way, in a pixel-wise Gaussian-based Maximum Likelihood classification (MLC)because the probability density function of a spatial feature is similar to the normal distribution under some assumptions From experiments, there was found to be a substantial improvement in the overall classification accuracy for TM forestry data. The probability of correct classification (PCC) for the new clearcut and the alpine meadow classes increased by 7 to 97 per cent correct by adding the spatial feature.

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