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Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details
Abstract
Image fusion techniques are widely used to integrate a lower spatial resolution multispectral image with a higher spatial resolution panchromatic image, such as Thematic Mapper (TM) multispectral band and SPOT Panchromatic images. However, the existing techniques either cannot avoid distorting the image spectral properties or involve complicated and time-consuming frequency decomposition and re-construction processing. A simple spectral preserve fusion technique: the Smoothing Filter-based Intensity Modulation (SFIM) has thus been developed based on a simplified solar radiation and land surface reflection model. By using a ratio between a higher resolution image and its low pass filtered (with a smoothing filter) image, spatial details can be modulated to a co-registered lower resolution multispectral image without altering its spectral properties and contrast. The technique can be applied to improve spatial resolution for either colour composites or individual bands. The fidelity to spectral property and the spatial textural quality of SFIM are convincingly demonstrated by an image fusion experiment using TM and SPOT Panchromatic images of south-east Spain. The visual evaluation and statistical analysis compared with HSI and Brovey transform techniques confirmed that SFIM is a superior fusion technique for improving spatial detail of multispectral images with their spectral properties reliably preserved.
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Cited by
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening
Yuan, Q., Wei, Y., Meng, X., Shen, H., & Zhang, L. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2794888 · Cited by 649
Pixel-wise regression using U-Net and its application on pansharpening
Yao, W., Zeng, Z., Lian, C., & Tang, H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.103 · Cited by 133
Metadata
- Authors
- Liu, J. G.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2000-01
- Volume
- 21
- Issue
- 18
- Pages
- 3461-3472
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2001800591
- Cited by
- 965
- References
- 0
- FWCI
- 12.32
- Type
- journal article
- Language
- en
- Open access
- closed
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