A Benchmarking Protocol for Pansharpening: Dataset, Preprocessing, and Quality Assessment

Vivone, Gemine; Mura, Mauro Dalla; Garzelli, Andrea; Pacifici, Fabio

doi:10.1109/jstars.2021.3086877

Cited by 81 publications

(34 citation statements)

References 40 publications

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“…To verify the effect of the proposed algorithm, the performance with reference for the HS image and the performance without reference for the MS image were tested. The comparison method in this paper refers to the Matlab ToolBox [2], [38]- [40], and the comparative methods are described as follows.…”

Section: Resultsmentioning

confidence: 99%

Smoothing Filter-Based Panchromatic Spectral Decomposition for Multispectral and Hyperspectral Image Pansharpening

Xie

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper proposes an efficient and high-fidelity panchromatic (PAN) spectral decomposition method based on smoothing filtering for multispectral (MS) and hyperspectral (HS) image pansharpening. The proposed method assumes that the high-frequency spatial details at the same scale are the same for different spectral images captured by the satellite at the same time. When the PAN image is pre-filtered and downsampled to the MS scale, it will have the same high-frequency spatial detail as the MS image, with only low-frequency spectral differences. Pre-filtering for anti-aliasing when downsampling. Then the spectral decomposition coefficients from down-sampled PAN image to MS image can be calculated on MS scale. The lowfrequency spectral information of an image at different scales is the same, the spectral decomposition coefficients on the MS scale can be up-sampled to the PAN scale, and the original PAN image can be spectrally decomposed to obtain the sharpened image. The proposed method only decomposes the low-frequency spectrum, and the high-frequency spatial details are the same as the original PAN image, the spatial detail is well preserved. This paper verifies the effect of the proposed method on MS and HS image sharpening through experiments, and the results show that the proposed method is better than the comparison method. This paper also controls other variables to compare with the HPM method. The results show that the hybrid quality with no reference (HQN R) and spatial distortion index (Ds) of the proposed method are better than the HPM method.

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Section: Resultsmentioning

confidence: 99%

Smoothing Filter-Based Panchromatic Spectral Decomposition for Multispectral and Hyperspectral Image Pansharpening

Xie

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…It consists of an improved adaptive spatially-weighted filter that can achieve both of the above functions. In [9], image preprocessing steps such as filtering, upsampling, and band registration were standardized by providing references. These steps suggest that preprocessing is an important part of many applications based on remote sensing images.…”

Section: Preprocessing Methods For Ship Detectionmentioning

confidence: 99%

Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection

Guo

Wang

2022

Applied Sciences

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The complexity of changeable marine backgrounds makes ship detection from satellite remote sensing images a challenging task. The ubiquitous interference of cloud and fog led to missed detection and false-alarms when using imagery-based optical satellite remote sensing. An off-shore ship detection method with scene classification and a saliency-tuned YOLONet is proposed to solve this problem. First, the image blocks are classified into four categories by a density peak clustering algorithm (DPC) according to their grayscale histograms, i.e., cloudless areas, thin cloud areas, scattered clouds areas, and thick cloud areas. Secondly, since the ships can be regarded as salient objects in a marine background, the spectral residue saliency detection method is used to extract prominent targets from different image blocks. Finally, the saliency tuned YOLOv4 network is designed to quickly and accurately detect ships from different marine backgrounds. We validated the proposed method using more than 2000 optical remote sensing images from the GF-1 satellite. The experimental results demonstrated that the proposed method obtained a better detection performance than other state-of-the-art methods.

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“…Two test images, acquired by two different platforms, GeoEye-1 and WorldView-3, have been used in the simulations. In this section, besides describing the two datasets, which belong to the reference pansharpening dataset, namely PAirMax, described in [64], we present an analysis of the solution of the multivariate regressions (Equations ( 7) and ( 16)) for the two datasets.…”

Section: Data Setsmentioning

confidence: 99%

Advantages of Nonlinear Intensity Components for Contrast-Based Multispectral Pansharpening

et al. 2022

Self Cite

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In this study, we investigate whether a nonlinear intensity component can be beneficial for multispectral (MS) pansharpening based on component-substitution (CS). In classical CS methods, the intensity component is a linear combination of the spectral components and lies on a hyperplane in the vector space that contains the MS pixel values. Starting from the hyperspherical color space (HCS) fusion technique, we devise a novel method, in which the intensity component lies on a hyper-ellipsoidal surface instead of on a hyperspherical surface. The proposed method is insensitive to the format of the data, either floating-point spectral radiance values or fixed-point packed digital numbers (DNs), thanks to the use of a multivariate linear regression between the squares of the interpolated MS bands and the squared lowpass filtered Pan. The regression of squared MS, instead of the Euclidean radius used by HCS, makes the intensity component no longer lie on a hypersphere in the vector space of the MS samples, but on a hyperellipsoid. Furthermore, before the fusion is accomplished, the interpolated MS bands are corrected for atmospheric haze, in order to build a multiplicative injection model with approximately de-hazed components. Experiments on GeoEye-1 and WorldView-3 images show consistent advantages over the baseline HCS and a performance slightly superior to those of some of the most advanced methods.

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A Benchmarking Protocol for Pansharpening: Dataset, Preprocessing, and Quality Assessment

Cited by 81 publications

References 40 publications

Smoothing Filter-Based Panchromatic Spectral Decomposition for Multispectral and Hyperspectral Image Pansharpening

Smoothing Filter-Based Panchromatic Spectral Decomposition for Multispectral and Hyperspectral Image Pansharpening

Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection

Advantages of Nonlinear Intensity Components for Contrast-Based Multispectral Pansharpening

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