| Autor: |
Jiahui Ni, Zhimin Shao, Zhongzhou Zhang, Mingzheng Hou, Jiliu Zhou, Leyuan Fang, Yi Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 5468-5479 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2022.3188181 |
| Popis: |
Pansharpening in remote sensing image aims at acquiring a high-resolution multispectral (HRMS) image directly by fusing a low-resolution multispectral (LRMS) image with a panchromatic (PAN) image. The main concern is how to effectively combine the rich spectral information of LRMS image with the abundant spatial information of PAN image. Recently, many methods based on deep learning have been proposed for the pansharpening task. However, these methods usually have two main drawbacks: 1) requiring HRMS for supervised learning; and 2) simply ignoring the latent relation between the MS and PAN image and fusing them directly. To solve these problems, we propose a novel unsupervised network based on learnable degradation processes, dubbed as LDP-Net. A reblurring block and a graying block are designed to learn the corresponding degradation processes, respectively. In addition, a novel hybrid loss function is proposed to constrain both spatial and spectral consistency between the pansharpened image and the PAN and LRMS images at different resolutions. Experiments on GaoFen-2, Worldview-2, and Worldview-3 images demonstrate that our proposed LDP-Net can fuse PAN and LRMS images effectively without the help of HRMS samples, achieving promising performance in terms of both qualitative visual effects and quantitative metrics. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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