With the recent advances achieved by deep neural networks in image processing applications, researchers have begun exploring deep learning in pansharpening and obtained remarkable results. However, the existing methods are generally limited by their weak feature representation ability, often leading to spectral distortion or spatial blur. To generate high‐quality pansharpened images, this article proposes a novel neural network for pansharpening that includes both feature extraction and excitation mechanisms to consider important features. The neural network is modified with domain knowledge in pansharpening to fully extract spectral and spatial structures, and the proposed method outperforms traditional methods.
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Extraction‐and‐excitation deep neural network for pansharpening
Semantic Scholar · Computer Science · 2020
Abstract
With the recent advances achieved by deep neural networks in image processing applications, researchers have begun exploring deep learning in pansharpening and obtained remarkable results. However, the existing methods are generally limited by their weak feature representation ability, often leading to spectral distortion or spatial blur. To generate high‐quality pansharpened images, this article proposes a novel neural network for pansharpening that includes both feature extraction and excitation mechanisms to consider important features. The neural network is modified with domain knowledge in pansharpening to fully extract spectral and spatial structures, and the proposed method outperforms traditional methods.