Land cover mapping using combined Landsat TM imagery and textural features from ERS-1 synthetic aperture radar imagery

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Abstract
Texture features computed from unfiltered ERS-1 SAR imagery have been used as additional features alongside Landsat TM radiances to map Mediterranean land cover. The texture features were normalized to reduce the impact of speckle noise. The classification procedure was carried out with a multilayer perceptron neural network. The results show that the addition of contrast, angular second moment, entropy, and inverse difference moment features from SAR, in addition to TM channels, can give overall accuracy improvement in land cover classification of 2 - 3%. While overall this is not very significant, for particular classes the use of texture leads to greater improvements in accuracy which could be useful in mapping applications. The results of the use of the SAR texture measures were compared using a number of different accuracy measures derived from individual confusion matrices.

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