用神经网络算子提升遥感图像建模与增强效果。
Implementation of neural network operators with applications to remote sensing data
- 基于双曲正切激活的多维神经网络算子，实现图像建模与重缩放。
- 在RETINA数据集上，结构相似性（SSIM）优于双线性与双三次插值。
- 适合遥感图像处理，尤其对细节保留要求高的场景。
本文基于多维神经网络（NN）算子理论，提出两种以双曲正切型函数为激活函数的算法。第一种用于建模多维信号（如数字图像），第二种用于数据重缩放与增强。针对遥感数据（以图像形式呈现）进行了多项应用研究，实验基于公开的RETINA数据集开展。与双线性、双三次插值等经典方法对比，所提算法在结构相似性指数（SSIM）上表现更优，尤其在保持图像细节方面优势显著。
原文摘要 · Abstract (English)
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).
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