Abstract
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
Highlights
With the rapid development of remote sensors, a great deal of optical earth observation satellites and digital aerial cameras can simultaneously obtain high spectral resolution multispectral (MS) and high spatial resolution panchromatic (Pan) images (Yun, 2012)
A pan-sharpening method based on guided image filtering is proposed and applies to GaoFen-2 satellite (GF-2) images
The MS image consists of four bands and the spectral range of the MS bands is exactly covered by the range of the Pan band
Summary
With the rapid development of remote sensors, a great deal of optical earth observation satellites and digital aerial cameras can simultaneously obtain high spectral resolution multispectral (MS) and high spatial resolution panchromatic (Pan) images (Yun, 2012). The images obtained from a single sensor often cannot meet applications, such as visual interpretation, change detection and detailed land cover classification, etc. The representative CS methods include principal component analysis (PCA), Gram-Schmidt transformation (GS), Intensity-HueSaturation (IHS) and University of New Brunswick (UNB) method (Zhang, 2004), etc. With more and more sensors with different spectral and spatial properties were launched, these existing methods show various limitations, and have not fully assessed on data sets captured by the new sensors (Zhang, 2004)
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