Effect of Band Ratios and Indices on Classification Accuracy of Multispectral Satellite Images

      Multispectral satellite images are holding whole information about the area under investigation. Many of digital processing techniques are used to extract most of the possible information from these images, such these techniques are spectral band ratios, Tasseled Cap transformation and Principle Component Analysis (PCA). These techniques are play good part for features extraction and reducing the number of spectral bands with no loses in information.The research aims to apply classifications on band ratios and image indices which include PCA and Tasseled Cap transformations, of Enhanced Thematic Mapper Plus (ETM+) satellite images, and to show how the accuracies of the classification are differ from the accuracy of classification of the raw ETM+ bands images.The results show that the best bands ratios that adopted to represent five selected land features are 5/7, 4/5, 2/5, 1/4, and 2/4. Optimum Index Factor method (OIF) has been adopted on raw bands combinations, and on the bands ratio combinations to select which band combination that contains much information, where the results show that the best combination for raw bands is 3-4-7, and best combination for ratios band is 4/5-1/4-2/5.Iterative Self Organizing Data Analysis (ISODATA) method has been adopted as unsupervised classification, and Maximum Likelihood method as supervised classification. The overall accuracies are 87.18%, 89.45%, 89.26%,
and 89.52% for raw bands, band ratios, Tasseled Cap, and PCA respectively,which showed that transformation techniques give good spectral enhancements and feature extraction which are undistinguishable in raw image, and represent good tools to increase the accuracy of classification.