Digital processing of satellite images for lithological discrimination and classification in arid regions
Satellite images have been used as a complementary information for geological studies. In order to realise the maximum potential of satillite imagery, then improvements are needed, both in the visual presentation of such images, and in their automatic classification , in order to reveal the rock differences. Methods of processing imagery, were evaluated (band ratio, principal components, decorrelated stretch and maximum likelihood) and new (canonical regression, hue-saturation-intensity HSI transform, with modified manipulation, and watershed) were evaluated with respect to their ability to reveal rock differences. It was found that the HSI method gave the best results, both for visual presentation and automatic classifcation. This method has the ability to enhance both spectral and spatial information simultaneously without any data loss which is not the case in the other image enhancement methods (band ratio, principal components or decorrelation stretch). For automatic classification, the 'hue' images produced by the HSI transformation typically gave accurate (91%) classification of all the major rock types. Further, it was shown that the watershed method of classification was superior error rate = 9% to the maximum likelihood method (error rate 14%) for the same inputs. The new method of canonical regression was evaluated and although it was not very successful, the results were encouraging and it was concluded that this method may enable the estimation of the chemical composition of exposed rocks directly from satellite imagery.