Two-Dimensional Neural Network Entropy for Remote Sensing Image Analysis

Measuring the predictability and complexity of time series using entropy is an essential tool for designing and controlling a nonlinear system in the remote sensing field. However, the existing methods have some drawbacks related to their strong dependence on method parameters. To overcome these difficulties, this study proposes a new method for estimating the two- dimensional neural network entropy (NNetEn2D) for evaluating the regularity or predictability of images using the LogNNet neural network model.