Journal Article10.1109/TGRS.2020.2975230
Band-Independent Encoder–Decoder Network for Pan-Sharpening of Remote Sensing Images
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TL;DR: The proposed band-independent encoder–decoder network outperforms several state-of-the-art pan-sharpening methods in both visual appearance and objective indexes, and the single-band evaluation results further verify the superiority of the proposed method.
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Abstract: Pan-sharpening is a fundamental task for remote sensing image processing. It aims at creating a high-resolution multispectral (HRMS) image from a multispectral (MS) image and a panchromatic (PAN) image. In this article, a new band-independent encoder–decoder network is proposed for pan-sharpening. The network takes a single band of the MS (BMS) image, the PAN image, and the low-resolution PAN (LRPAN) image as inputs. The output of the network is the corresponding band of high-resolution MS (HRBMS) image. In this way, the network can process MS images with any number of bands. The overall structure of the network consists of two encoder–decoder modules at low-resolution and high-resolution, respectively. An auxiliary LRPAN image is used to speed up the training and improve the performance. The partly shared network and hierarchical structure for low-resolution and high-resolution enable a better fusion of features extracted from different scales. With a fast fine-tuning strategy, the trained model can be applied to images from different sensors. Experiments performed on different data sets demonstrate that the proposed method outperforms several state-of-the-art pan-sharpening methods in both visual appearance and objective indexes, and the single-band evaluation results further verify the superiority of the proposed method.
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