TWO-DIMENSIONAL NEURAL NETWORK ENTROPY FOR REMOTE SENSING IMAGE ANALYSIS

被引：0
|
作者：
Velichko, Andrei [1 ]
Wagner, Matthias P. [2 ]
Taravat, Alireza [3 ]
机构：
[1] Petrozavodsk State Univ, Inst Phys & Technol, Petrozavodsk, Russia
[2] Panopterra, Darmstadt, Germany
[3] Deimos Space, Oxford OX110QR, England
来源：
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年
关键词：
Entropy; image features; sentinel-2; remote sensing;
D O I：
10.1109/IGARSS46834.2022.9883430
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
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.
引用
收藏
页码：1952 / 1954
页数：3
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