Visualization of uncertainty using entropy on noise clustering with entropy classifier

Noise clustering, is a vigorous clustering method, performs partitioning of data sets reducing errors caused by outliers. This uses pure spectral information in image classification. A `noisy´ classification results are often produced due to the high variation in the spatial distribution of the same class. This provides a degree of similarity for each pixel in every class. The performance of Noise Clustering with Entropy(NCWE) is evaluated in supervised mode and, the assessment of accuracy has been carried out using entropy. The basic objective of this research is to optimize the resolution parameter `δ´ for Noise clustering (NC) algorithm and regularizing parameter `ν´ for Noise clustering with entropy classifier(NCWE) and analysis of the classified fraction images. Experiments with simulated training dataset shows the optimized values of resolution parameter `δ´ is 106 and regularizing parameter `ν´ is 0.08 for Noise Clustering with Entropy(NCWE) classifier wherein minimum level of uncertainty exist. The entropy and membership verifications are taken as indirect measures to check the accuracy of classified image.