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.

中文翻译：

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单个训练数据样本在改进的可能性 c 均值分类器中处理特定作物异质性的重要性

在遥感图像中，基于像素的分类器使用从训练样本数据集生成的均值或方差-协方差统计参数。这些参数并不代表类内变化的总体情况。这项研究工作启发了每个训练样本在处理类内异质性而不是使用统计参数（平均值）方面的作用。改进的可能性 c 均值模糊算法，能够进行单类映射，已经被试验来处理类内的异质性。芥菜、小麦和草类的映射是使用拉贾斯坦邦巴纳斯塔利的 Sentinel-2A/2B 的多光谱时间图像进行的。已经观察到，在基于模糊的分类器中使用单个样本代替统计参数时，由于类内的异质性，确定的单个类受到的影响最小。计算了有利和不利类别的平均成员差异以及 F 分数、kappa 和总体准确度。通过所有这些参数，作为平均值的单个样本优于其他训练方法。