A Hyperspectral Image NSST-HMF Model and Its Application in HS-Pansharpening

Wang, Xianghai; Mu, Zhenhua; Song, Ruoxi; Tao, Jingzhe; Song, Chuanming

doi:10.1109/tgrs.2020.2967549

Cited by 10 publications

(5 citation statements)

References 41 publications

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“…Under the maximum a posterior framework, the hyper-Laplacian distribution induces the nonconvex p (0 < p < 1) norm sparse prior. Similarly, a nonsubsampled shearlet transform hidden Markov forest (NSST-HMF) model was proposed to investigate the statistical properties of NSST coefficients of hyperspectral images and the PAN image in the transformation domain [24]. This new work shows that the NSST coefficients also exhibit a heavy-tail distribution, which can be well modeled by the Gaussian mixture model.…”

Section: B Enhanced Spectral Fidelitymentioning

confidence: 99%

Pansharpening by Combining Enhanced Spatial-Spectral Fidelity and Gradient-Domain Guided Filtering

Liu

et al. 2023

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

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Pansharpening techniques fuse the complementary information from a high-resolution panchromatic (PAN) and low-resolution multispectral (LRMS) images to produce a highresolution multispectral (HRMS) image. However, most of the existing pansharpening methods have been affected in the fullresolution domain due to both the absence of ground truths and unavoidable unknown noises. To address this problem, a new pansharpening method has been proposed that combines enhanced sparse models and gradient-domain guided image filtering. Specifically, a deep multiscale Laplacian pyramid super-resolution network improves the resolution of the original LRMS image instead of bicubic interpolation. Then, the accurate preservation of spatialspectral characteristics is achieved in a variational framework with enhanced spatial-spectral fidelity in the image gradient domain. Meanwhile, the gradient-domain guided image filter is used to effectively improve the extraction accuracy of spatial characteristics from the PAN image. Finally, the enhanced sparse regularization on the latent HRMS image is designed to remove noise and artifacts while promoting piecewise-smooth solutions. The experimental results on public satellite datasets demonstrate the superiority of the proposed method against existing pansharpening methods in terms of both full-resolution performance indexes and visual quality.

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Section: B Enhanced Spectral Fidelitymentioning

confidence: 99%

Pansharpening by Combining Enhanced Spatial-Spectral Fidelity and Gradient-Domain Guided Filtering

Liu

et al. 2023

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

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“…HSI has rich spatial and spectral information to provide a strong basis for its classification, but also brings some difficulties for the classification. (1) The large amount of HSI data leads to a large computational effort for HSI-related processing, and how to quickly and accurately classify the image elements to be classified is one of the key issues to be considered when studying HSI classification. (2) The spatial resolution of HSI is lower than the spectral resolution at the nanometer level, and there are some mixed pixels in the image, and the existence of mixed pixels makes the accurate classification of HSI difficult.…”

Section: Research Difficulties Of Hyperspectral Images Classification...mentioning

confidence: 99%

“…Hyperspectral images (HSI) classification technology originated in the early 1980s, when it was not developed rapidly due to insufficient technology and imperfect hardware facilities [1]. It was not until the successful launch of the "Gaofen 5" satellite that researchers' attention to hyperspectral remote sensing technology reached a new height, promoting the development of HSI classification technology, which has been widely used in marine hydrographic detection, ecological and environmental monitoring, fine agriculture and other related fields.…”

Section: Introductionmentioning

confidence: 99%

A Review of Hyperspectral Image Classification Based on Joint Spatial-spectral Features

Gan

2022

J. Phys.: Conf. Ser.

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Hyperspectral image classification technology is a basic work in the application of hyperspectral images. In recent years, with the innovation and development of hyperspectral image classification technology, the method and performance of hyperspectral image classification based on joint spatial-spectral features have made breakthroughs, and have gradually become the focus of researchers. In order to further promote the development of the spatial-spectral feature union class method and improve the classification accuracy of hyperspectral images, this paper summarizes the commonly used spatial-spectral union algorithms. Firstly, the introduction briefly outlines the background and research status of this field. Some common problems in the process of hyperspectral image classification are listed. Finally, some current hyperspectral image classification methods based on joint spatial-spectral features are introduced. The main roles and existing problems of spatial-spectral joint features in the field of hyperspectral image classification are summarized in detail, and the future research directions are prospected.

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“…Nonsubsampled shearlet transform (NSST) is a kind of nonsubsampled multiscale transform, which was introduced based on the theory of shearlet transform [ 11 , 18 ]. The image is decomposed by NSST into multiple scales with multiple directions by multiscale and multidirectional decompositions.…”

Section: Related Workmentioning

confidence: 99%

“…Yin et al [ 17 ] proposed an image fusion technique via NSST and parameter-adaptive pulse coupled neural network (PAPCNN) to improve the contrast and brightness of the fused medical images. Wang et al [ 18 ] introduced the nonsubsampled shearlet transform hidden Markov forest (NSST-HMF) model for pansharpening to improve the spatial resolution of hyperspectral images while preserving spectral features.…”

Section: Introductionmentioning

confidence: 99%

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

2021

Sensors

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The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is utilized to decompose the source images into low-frequency sub-bands and high-frequency sub-bands. Low-frequency sub-bands reflect the contrast and brightness of the source images, while high-frequency sub-bands reflect the texture and details of the source images. Using this information, the contrast saliency map and SML fusion rules are introduced into the corresponding sub-bands. Finally, the inverse NSST reconstructs the fusion image. Experimental results demonstrate that the proposed multisource remote image fusion technique performs well in terms of contrast enhancement and detail preservation.

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A Hyperspectral Image NSST-HMF Model and Its Application in HS-Pansharpening

Cited by 10 publications

References 41 publications

Pansharpening by Combining Enhanced Spatial-Spectral Fidelity and Gradient-Domain Guided Filtering

Pansharpening by Combining Enhanced Spatial-Spectral Fidelity and Gradient-Domain Guided Filtering

A Review of Hyperspectral Image Classification Based on Joint Spatial-spectral Features

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

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