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Maximum Membership Fraction Based Pure Pixel Assessment Approach for Hyperspectral Data Analysis Using Deep Learning

Land cover classification in the remote sensing has been done using various deep learning algorithms; and higher classification accuracies have been achieved. Such classification is based on the maximum membership fraction (MMF), when we use Convolutional Neural Network (CNN). MMF is basically the maximum probability fraction. A pixel under prediction has been assigned to that class which has maximum fraction out of the corresponding fractions for all land cover classes. Various methodologies exist for pure pixel extraction and used for hyperspectral unmixing. An assumption has been taken that MMF and abundance used in the case of unmixing are similar. Both MMF and abundance follow the rule of sum to one. In this paper, a classification method has been implemented using CNN to achieve better classification accuracy. Thereafter number of pure pixels extracted based on the various MMF thresholds.