Abstract
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.
Highlights
Multisensor superresolution is a technique for enhancing the spatial resolution of a low-resolution (LR) image by fusing it with an auxiliary high-resolution (HR) image obtained by a different imaging sensor
texture-guided multisensor superresolution (TGMS) is based on multiresolution analysis (MRA), considering object structures and texture information
The proposed method is compared with three benchmark pan-sharpening methods—namely, Gram-Schmidt adaptive (GSA) [6], smoothing filtered-based intensity modulation (SFIM) [7], and generalized Laplacian pyramid (GLP) [8]
Summary
Multisensor superresolution is a technique for enhancing the spatial resolution of a low-resolution (LR) image by fusing it with an auxiliary high-resolution (HR) image obtained by a different imaging sensor. The spatial resolution of remote sensing instruments is often designed at a moderate or large scale due to the trade-off between sensor specifications, such as spatial resolution, spectral resolution, swath width, and signal-to-noise ratio. There is always demand for enhancing the spatial resolution of remotely sensed images. Multisensor superresolution has been widely used in the remote sensing community to address the issue of spatial resolution by using complementary data sources. Pan-sharpening is the most common multisensor superresolution technique, where an LR multispectral (MS) image is sharpened by fusing it with an HR panchromatic (PAN) image. Many spaceborne MS sensors are mounted together with PAN sensors, and pan-sharpened products are distributed as default. Component substitution (CS) methods [4,5,6] and multiresolution analysis (MRA)
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