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- W3115223653 abstract "This article addresses the problem of remote sensing image pan-sharpening from the perspective of generative adversarial learning. We propose a novel deep neural network-based method named pansharpening GAN (PSGAN). To the best of our knowledge, this is one of the first attempts at producing high-quality pan-sharpened images with generative adversarial networks (GANs). The PSGAN consists of two components: a generative network (i.e., generator) and a discriminative network (i.e., discriminator). The generator is designed to accept panchromatic (PAN) and multispectral (MS) images as inputs and maps them to the desired high-resolution (HR) MS images, and the discriminator implements the adversarial training strategy for generating higher fidelity pan-sharpened images. In this article, we evaluate several architectures and designs, namely, two-stream input, stacking input, batch normalization layer, and attention mechanism to find the optimal solution for pan-sharpening. Extensive experiments on QuickBird, GaoFen-2, and WorldView-2 satellite images demonstrate that the proposed PSGANs not only are effective in generating high-quality HR MS images and superior to state-of-the-art methods but also generalize well to full-scale images." @default.
- W3115223653 created "2021-01-05" @default.
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- W3115223653 date "2021-12-01" @default.
- W3115223653 modified "2023-10-14" @default.
- W3115223653 title "PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening" @default.
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- W3115223653 doi "https://doi.org/10.1109/tgrs.2020.3042974" @default.
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