ANN based GUI to Classify Satellite Images for Remote Sensing

Automatic classification of satellite images to detect the presence of urban/forested/deserted features of the areas on earth plays a most crucial part in the field of remote sensing. This paper presents an image classification technique to classify the urban/forested/deserted areas on input satellite images by utilizing mean-shift clustering with artificial neural network. The input satellite images are first enhanced through fuzzy histogram equalization and the Haralick's texture features are utilized to ascertain the arbitrariness in the image. Testing and training is done through the ANN and the resultant image is finally utilized to quantify its accuracy and error rate. The proposed algorithm is implemented on MATLAB GUI and obtains a classification accuracy of 90.9% and is immune to noise.

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