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Texture-Guided Multisensor Superresolution for Remotely Sensed Images
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This paper presents a novel technique, namely texture-guided multisensor superresolution (TGMS), for fusing a pair of multisensor multiresolution images to enhance the spatial resolution of a lower-resolution data source. TGMS is based on multiresolution analysis, taking object structures and image textures in the higher-resolution image into consideration. TGMS is designed to be robust against misregistration and the resolution ratio and applicable to a wide variety of multisensor superresolution problems in remote sensing. The proposed methodology is applied to six different types of multisensor superresolution, which fuse the following image pairs: multispectral and panchromatic images, hyperspectral and panchromatic images, hyperspectral and multispectral images, optical and synthetic aperture radar images, thermal-hyperspectral and RGB images, and digital elevation model and multispectral images. The experimental results demonstrate the effectiveness and high general versatility of TGMS.
Title: Texture-Guided Multisensor Superresolution for Remotely Sensed Images
Description:
This paper presents a novel technique, namely texture-guided multisensor superresolution (TGMS), for fusing a pair of multisensor multiresolution images to enhance the spatial resolution of a lower-resolution data source.
TGMS is based on multiresolution analysis, taking object structures and image textures in the higher-resolution image into consideration.
TGMS is designed to be robust against misregistration and the resolution ratio and applicable to a wide variety of multisensor superresolution problems in remote sensing.
The proposed methodology is applied to six different types of multisensor superresolution, which fuse the following image pairs: multispectral and panchromatic images, hyperspectral and panchromatic images, hyperspectral and multispectral images, optical and synthetic aperture radar images, thermal-hyperspectral and RGB images, and digital elevation model and multispectral images.
The experimental results demonstrate the effectiveness and high general versatility of TGMS.
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