Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1055-1060, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1055-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Sep 2017
A PAN-SHARPENING METHOD BASED ON GUIDED IMAGE FILTERING: A CASE STUDY OVER GF-2 IMAGERY
Y. Zheng1, M. Guo1, Q. Dai2,3, and L. Wang1 1School of Forestry, Southwest Forestry University, Kunming, 650224, China
2School of Printing and Packaging, Wuhan University, Wuhan, 430079, China
3School of Material Engineering, Southwest Forestry University, Kunming, 650224, China
Keywords: Remote Sensing, Image Fusion, Pan-sharpening, Guided Filter, GF-2 Imagery Abstract. The GaoFen-2 satellite (GF-2) is a self-developed civil optical remote sensing satellite of China, which is also the first satellite with the resolution of being superior to 1 meter in China. In this paper, we propose a pan-sharpening method based on guided image filtering, apply it to the GF-2 images and compare the performance to state-of-the-art methods. Firstly, a simulated low-resolution panchromatic band is yielded; thereafter, the resampled multispectral image is taken as the guidance image to filter the simulated low resolution panchromatic Pan image, and extracting the spatial information from the original Pan image; finally, the pan-sharpened result is synthesized by injecting the spatial details into each band of the resampled MS image according to proper weights. Three groups of GF-2 images acquired from water body, urban and cropland areas have been selected for assessments. Four evaluation metrics are employed for quantitative assessment. The experimental results show that, for GF-2 imagery acquired over different scenes, the proposed method can not only achieve high spectral fidelity, but also enhance the spatial details
Conference paper (PDF, 1667 KB)


Citation: Zheng, Y., Guo, M., Dai, Q., and Wang, L.: A PAN-SHARPENING METHOD BASED ON GUIDED IMAGE FILTERING: A CASE STUDY OVER GF-2 IMAGERY, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1055-1060, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1055-2017, 2017.

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