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Two-Dimensional Neural Network Entropy for Remote Sensing Image Analysis | IEEE Conference Publication | IEEE Xplore

Two-Dimensional Neural Network Entropy for Remote Sensing Image Analysis


Abstract:

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...Show More

Abstract:

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.
Date of Conference: 17-22 July 2022
Date Added to IEEE Xplore: 28 September 2022
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Conference Location: Kuala Lumpur, Malaysia

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