Effect of Texture Feature Combination on Satellite Image Classification

Dr. Mohammed Sahib Mahdi Altaei, Saif Mohammed Ahmed, Dr. Hayder Ayad


This paper presents a method for SIC to classify a PatternNet satellite image dataset taken from Google Earth and Google Map Application newly adopted in 2017, this SIC method use firstly a pre-processing step to verify the way of how represent the gray level of each sample image using multiple features based descriptors to handling the problem of selecting the descriptors for SIC. It suggested to use several type of texture feature extraction method, each of these method tested with the Support Vector Machine (SVM) to verify its ability to extract d a discriminative features. Also the feature selection method used to remove the less informative features of each method to get the more relevant features, finally the decision result of each feature extraction method from the classifier tested with feature combination methods, it is used to improve the final decision of the SIC method by combine multi feature extraction method.


Texture Features, Remote Sensing, Features Combination, Satellite Image Classification, Edge Histogram Descriptor, Local Binary Pattern, Grey Level Co-occurrence Matrix, Support Vector Machine

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DOI: https://doi.org/10.26483/ijarcs.v9i2.5897


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