Land use and Land Cover Classification using RGB&L Based Supervised Classification Algorithm

After the Geometric correction and resampling the image should be classified. This paper introduced a new method for classifying the areas in a remotely sensed image under the category of supervised classification techniques. This classification technique describes how to classify the geographical areas in given image under supervised classification techniques conventions. So to tell the abstract of this technique in this technique from the top left corner of the image the analysis begins. Each pixel from the top left corner is taken for analysis and is examined for its resemblance with the training data. In this method training data is defined by the color values rather than by other attributes like distance from any mean points or corner of the image. This method utilizes the channeling techniques to split each pixels RGB channels from its color value and in addition the color density or the brightness of the pixel also grabbed for examination. This paper introduces the RGB&L algorithm and analyzing its performance with other classification algorithm.