| Autor: |
Kai Zhang, Feng Zhang, Zhixi Feng, Jiande Sun, Quanyuan Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 426-439 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2020.3043521 |
| Popis: |
In this article, we proposed a novel image fusion method based on multiscale convolution sparse decomposition (MCSD). A unified framework based on MCSD is first utilized to decompose panchromatic (PAN) image and the spatial component of upsampled low spatial resolution multispectral (LR MS) images, which can produce the corresponding low frequencies and feature maps. By combining convolution sparse decomposition with multiscale analysis, MCSD can efficiently approximate the spatial and spectral information in images. Next, a binary map generated from gradient information is utilized to integrate the low frequencies of LR MS and PAN images. For feature maps, the fusion gain for each pixel is calculated according to the similarity between the local patches from them. Finally, the fused image is reconstructed by the sum of fused low frequency and feature maps. Some experiments are conducted on QuickBird and GeoEye-1 satellite datasets. Compared with other methods, the proposed method performs better in visual and numerical evaluations. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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