标题： 神经网络算子的实现及其在遥感数据中的应用 显示英文标题

标题： Implementation of neural network operators with applications to remote sensing data

摘要： 在本文中，我们提供了两种基于多维神经网络（NN）算子理论的算法，这些算子由双曲正切sigmoid函数激活。 理论结果被回顾以证明此处实现的算法的性能。 具体而言，第一个算法对多维信号（如数字图像）进行建模，而第二个算法则解决所考虑数据的缩放和增强问题。 我们讨论了基于NN的算法在建模和缩放/增强遥感数据（表示为图像）中的几种应用，并在（公开访问）RETINA数据集选择的遥感（RS）图像上进行了数值实验。 与传统插值方法（如双线性插值和双三次插值）的比较表明，所提出的算法优于其他方法，特别是在结构相似性指数（SSIM）方面。 显示英文摘要

摘要： In this paper, we provide two algorithms based on the theory of multidimensional neural network (NN) operators activated by hyperbolic tangent sigmoidal functions. Theoretical results are recalled to justify the performance of the here implemented algorithms. Specifically, the first algorithm models multidimensional signals (such as digital images), while the second one addresses the problem of rescaling and enhancement of the considered data. We discuss several applications of the NN-based algorithms for modeling and rescaling/enhancement remote sensing data (represented as images), with numerical experiments conducted on a selection of remote sensing (RS) images from the (open access) RETINA dataset. A comparison with classical interpolation methods, such as bilinear and bicubic interpolation, shows that the proposed algorithms outperform the others, particularly in terms of the Structural Similarity Index (SSIM).