Panchromatic sharpening of multispectral satellite imagery via an explicitly defined convex self-similarity regularization

Chia Hsiang Wang, Chia Hsiang Lin, José M. Bioucas Dias, Wei Cheng Zheng, Kuo Hsin Tseng

研究成果: 會議貢獻類型會議論文同行評審

3 引文 斯高帕斯(Scopus)

摘要

In satellite imaging remote sensing, injecting spatial details extracted from a panchromatic image into a multispectral image is referred to as pansharpening, which is ill-posed and requires regularization. Self-similarity, a critical prior knowledge yielding great success in regularizing various imaging inverse problems, has been widely observed in natural images; its formalization is not, however, straightforward. Very recently, we mathematically described the self-similarity pattern as a weighted graph, which can then be transformed into an explicit convex regularizer, that is adopted in our pansharpening criterion design. Most importantly, such convexity allows the adoption of convex optimization theory in solving self-similarity regularized inverse problems with convergence guarantee. One step of our pansharpening algorithm is exactly the proximal operator induced by our new self-similarity regularizer, which is solved by another customized algorithm that is interesting in its own right as could be used as a denoiser. Experiments show promising performance of the proposed method.

原文???core.languages.en_GB???
頁面3129-3132
頁數4
DOIs
出版狀態已出版 - 2019
事件39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
持續時間: 28 7月 20192 8月 2019

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???event.eventtypes.event.conference???39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
國家/地區Japan
城市Yokohama
期間28/07/192/08/19

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