Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: Practical discussion and challenges

Meng, Xiangchao; Shen, Huanfeng; Li, Huifang; Zhang, Liangpei; Fu, Randi

doi:10.1016/j.inffus.2018.05.006

Cited by 253 publications

(109 citation statements)

References 84 publications

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“…The set of space-constant optimal weights {ŵ k } k=0,...,N is calculated as the minimum MSE (MMSE) solution of (5). A figure of merit of the matching achieved by (5) is given by the coefficient of determination (CD), namely R 2 , defined as:…”

Section: Spectral or Component-substitution Methodsmentioning

confidence: 99%

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Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover

et al. 2018

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Whenever vegetated areas are monitored over time, phenological changes in land cover should be decoupled from changes in acquisition conditions, like atmospheric components, Sun and satellite heights and imaging instrument. This especially holds when the multispectral (MS) bands are sharpened for spatial resolution enhancement by means of a panchromatic (Pan) image of higher resolution, a process referred to as pansharpening. In this paper, we provide evidence that pansharpening of visible/near-infrared (VNIR) bands takes advantage of a correction of the path radiance term introduced by the atmosphere, during the fusion process. This holds whenever the fusion mechanism emulates the radiative transfer model ruling the acquisition of the Earth's surface from space, that is for methods exploiting a multiplicative, or contrast-based, injection model of spatial details extracted from the panchromatic (Pan) image into the interpolated multispectral (MS) bands. The path radiance should be estimated and subtracted from each band before the product by Pan is accomplished. Both empirical and model-based estimation techniques of MS path radiances are compared within the framework of optimized algorithms. Simulations carried out on two GeoEye-1 observations of the same agricultural landscape on different dates highlight that the de-hazing of MS before fusion is beneficial to an accurate detection of seasonal changes in the scene, as measured by the normalized differential vegetation index (NDVI).

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Section: Spectral or Component-substitution Methodsmentioning

confidence: 99%

“…Histogram matching of Pan to the MMSE intensity component,Î L , should take into account (5). Thus, from the definition of CD (6):…”

Section: Spectral or Component-substitution Methodsmentioning

confidence: 99%

Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover

et al. 2018

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“…The fast pansharpening algorithm used in this study is based on the CS-based fusion framework and fully utilizes the high efficiency of the CS-based method [10,83]. Figure 7 depicts the flowchart of the proposed pansharpening method, which can be represented as:…”

Section: Pansharpeningmentioning

confidence: 99%

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

Feng

et al. 2019

Remote Sensing

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Urban geographical maps are important to urban planning, urban construction, land-use studies, disaster control and relief, touring and sightseeing, and so on. Satellite remote sensing images are the most important data source for urban geographical maps. However, for optical satellite remote sensing images with high spatial resolution, certain inevitable factors, including cloud, haze, and cloud shadow, severely degrade the image quality. Moreover, the geometrical and radiometric differences amongst multiple high-spatial-resolution images are difficult to eliminate. In this study, we propose a robust and efficient procedure for generating high-resolution and high-quality seamless satellite imagery for large-scale urban regions. This procedure consists of image registration, cloud detection, thin/thick cloud removal, pansharpening, and mosaicking processes. Methodologically, a spatially adaptive method considering the variation of atmospheric scattering, and a stepwise replacement method based on local moment matching are proposed for removing thin and thick clouds, respectively. The effectiveness is demonstrated by a successful case of generating a 0.91-m-resolution image of the main city zone in Nanning, Guangxi Zhuang Autonomous Region, China, using images obtained from the Chinese Beijing-2 and Gaofen-2 high-resolution satellites.

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“…CS-based methods are classical image fusion approaches based on the projection transformation, with typical examples of intensity-hue-saturation (IHS) [21], principal component analysis (PCA) [22], and Gram-Schmidt (GS) [23]. MRA-based methods originate from multi-resolution analysis, and they enhance the spatial resolution of HS data by injecting detailed information of MS data into the resampled HS data; e.g., the "à trous" wavelet transform (ATWT) [24] and decimated wavelet transform (DWT) [25]. Subspace-based methods find a common subspace of both input images, and they generally enhance the spatial resolution of HS data using machine learning.…”

Section: Introductionmentioning

confidence: 99%

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

et al. 2020

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The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, spectral resolution, and swath width. Unfortunately, its applications may be undermined by its relatively low spatial resolution. Therefore, the data fusion of GF-5 with high spatial resolution multispectral data is required to further enhance its spatial resolution while preserving its spectral fidelity. This paper conducted a comprehensive evaluation study of fusing GF-5 hyperspectral data with three typical multispectral data sources (i.e., GF-1, GF-2 and Sentinel-2A (S2A)), based on quantitative metrics, classification accuracy, and computational efficiency. Datasets on three study areas of China were utilized to design numerous experiments, and the performances of nine state-of-the-art fusion methods were compared. Experimental results show that LANARAS (this method was proposed by lanaras et al.), Adaptive Gram–Schmidt (GSA), and modulation transfer function (MTF)-generalized Laplacian pyramid (GLP) methods are more suitable for fusing GF-5 with GF-1 data, MTF-GLP and GSA methods are recommended for fusing GF-5 with GF-2 data, and GSA and smoothing filtered-based intensity modulation (SFIM) can be used to fuse GF-5 with S2A data.

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Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: Practical discussion and challenges

Cited by 253 publications

References 84 publications

Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover

Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

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