Implementation of neural network operators with applications to remote sensing data
This work addresses image processing challenges for remote sensing applications, but it is incremental as it applies existing neural network operator theory to a specific domain.
The paper tackled the problem of modeling and enhancing remote sensing images by implementing two neural network-based algorithms, which outperformed classical interpolation methods like bilinear and bicubic interpolation, particularly in terms of the Structural Similarity Index (SSIM).
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).