论文标题
熵近似通过机器学习回归：遥感中图像的不规则评估应用
Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing
论文作者
论文摘要
首次显示使用机器学习（ML）回归方法的各种类型熵的近似。这项研究中提出的ML模型通过近似诸如奇异值分解熵（SVDEN），置换熵（Permen），样品熵（SAMPEN）和神经网络熵（NNETEN）及其2DAMOGIES等不同的熵技术来定义短期序列的复杂性。使用圆形内核技术测试了一种用于计算2D图像的SVDEN2D，permen2d和sampen2d的新方法。提出了基于Sentinel-2图像的培训和测试数据集（两个训练图像和一百九十八个测试图像）。使用计算Sentinel-2图像和R^2度量评估的2D熵的示例来证明熵近似的结果。该方法在短时间序列中的适用性为从n = 5到n = 113个元素的长度序列。发现R^2公制的趋势随着时间序列的长度而减小。对于SVDEN熵，n = 5的回归精度为r^2> 0.99，n = 113的r^2> 0.82。对于ML_SVDEN2D和ML_NNETEN2D模型，观察到了最佳指标。该研究的结果可用于使用ML回归的各种类型的熵近似的基础研究，以及在遥感中加速熵计算。模型的多功能性显示在使用Planck地图和Logistic Map的合成混沌时间序列上。
Approximation of entropies of various types using machine learning (ML) regression methods are shown for the first time. The ML models presented in this study define the complexity of the short time series by approximating dissimilar entropy techniques such as Singular value decomposition entropy (SvdEn), Permutation entropy (PermEn), Sample entropy (SampEn) and Neural Network entropy (NNetEn) and their 2D analogies. A new method for calculating SvdEn2D, PermEn2D and SampEn2D for 2D images was tested using the technique of circular kernels. Training and testing datasets on the basis of Sentinel-2 images are presented (two training images and one hundred and ninety-eight testing images). The results of entropy approximation are demonstrated using the example of calculating the 2D entropy of Sentinel-2 images and R^2 metric evaluation. The applicability of the method for the short time series with a length from N = 5 to N = 113 elements is shown. A tendency for the R^2 metric to decrease with an increase in the length of the time series was found. For SvdEn entropy, the regression accuracy is R^2 > 0.99 for N = 5 and R^2 > 0.82 for N = 113. The best metrics were observed for the ML_SvdEn2D and ML_NNetEn2D models. The results of the study can be used for fundamental research of entropy approximations of various types using ML regression, as well as for accelerating entropy calculations in remote sensing. The versatility of the model is shown on a synthetic chaotic time series using Planck map and logistic map.
