Importance of individual sample of training data in modified possibilistic c -means classifier for handling heterogeneity within a specific crop

In remote sensing images, pixel-based classifiers use means or variance-covariance statistical parameters generated from training sample data sets. These parameters do not represent in totality about variations within the class. This research work enlightens each training sample’s role in handling heterogeneity within the class instead of using statistical parameters (mean). Modified possibilistic c-means fuzzy algorithm, capable of single class mapping, has been experimented to handle heterogeneity within the class. The mapping of mustard, wheat, and grass classes has been conducted using multispectral temporal images of Sentinel-2A/2B of Banasthali, Rajasthan region. It has been observed that while using individual samples in place of statistical parameters in fuzzy-based classifiers, the individual class identified has been least affected due to heterogeneity within the class. Mean membership difference for favorable and non-favorable classes as well as F-score, kappa, and overall accuracy have been calculated. Through all these parameters, individual samples as mean outperformed other training approaches.