Hyperspectral Pansharpening via Local Intensity Component and Local Injection Gain Estimation

Dong, Wenqian; Yang, Yihan; Qu, Jiahui; Xiao, Song; Du, Qian

doi:10.1109/lgrs.2021.3094216

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…By using inverse PCA transform the pansharpened HSI is obtained. In 2021, Dong et al [262] suggested a more effective CS technique by achieving the intensity component and injection gain using Binary Partition Tree (BPT) and image segmentation. Other works in this area include [92], [93].…”

Section: A Pansharpeningmentioning

confidence: 99%

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Aburaed

Alkhatib

Marshall

et al. 2023

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

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Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is Hyperspectral Imaging (HSI). Unlike Multispectral Images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or Super Resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This paper reviews and discusses the most important algorithms relevant to this area of research between 2002-2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Meta-analysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions.

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Section: A Pansharpeningmentioning

confidence: 99%

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Aburaed

Alkhatib

Marshall

et al. 2023

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

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“…The traditional pansharpening methods can be categorized into three primary approaches: component substitution (CS)-based methods [5][6][7], multi-resolution analysis (MRA)based methods [8][9][10], and variational optimization (VO)-based methods [11][12][13]. CS-based methods involve transforming the multi-spectral (MS) image into a different space and substituting its spatial components with those of the panchromatic (PAN) image.…”

Section: Introductionmentioning

confidence: 99%

SWPanGAN: A hybrid generative adversarial network for pansharpening

Huang,

Li,

Zhang

2024

IET Image Processing

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Pansharpening is a vital technique in remote sensing that combines a low‐resolution multi‐spectral image with its corresponding panchromatic image to obtain a high‐resolution multi‐spectral image. Despite its potential benefits, the challenge lies in extracting features from the source images and eliminating artefacts in the fused images. In response to the challenge, a hybrid generative adversarial network‐based model, termed SWPanGAN, is proposed. For better feature extraction, the conventional convolution neural network is replaced with a Swin transformer in the generator, which provides the generator with the ability to model long‐range dependencies. Additionally, to suppress artefacts, a wavelet‐based discriminator is proposed for effectively distinguishing the frequency discrepancy. With these modifications, both the generator and discriminator networks of SWPanGAN are enhanced. Extensive experiments illustrate that our SWPanGAN can generate high‐quality pansharpening images and surpass other state‐of‐the‐art methods.

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“…In previous works, [14][15][16] the method to address the spatial limitations of hyperspectral imagery in the field of image fusion is through pansharpening algorithms. Pansharpening is the process of using a single panchromatic image as a source of high-spatial information to be fused with low-spatial resolution (but highly spectral) images such as HSI.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral VNIR-SWIR image fusion on cultural heritage and remote sensing datasets using image sharpening techniques

Macalintal,

Hassanzadeh,

Messinger

2024

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

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Hyperspectral imaging is one of the prevailing tools for the analysis of material composition and feature identification in images. The spectral features contained within image pixels are studied to better understand a pixel's material composition. However, a major limitation of using a single hyperspectral image sensor is that not all distinguishing spectral features of an image are captured within the conventional operating wavelength bands of a single hyperspectral sensor. While a straightforward solution of using a single hyperspectral sensor with larger wavelength ranges can address such an issue, this is often met with external limiting factors such as sensor availability or costs of operations. Therefore, in order to overcome the limitations of a single-sensor approach, there is motivation to integrate information from multiple sensors instead to create a more useful and accurate representation of a real-world scene. A particular challenge is that different sensors typically produce images of the same scene but at different spatial and spectral resolutions. In this study, we attempt to fuse VNIR and SWIR hyperspectral images and evaluate the fused images' performance as applied to Cultural Heritage. Specifically, image fusion is performed on a medieval manuscript referred to as the Italian Leaf provided by the University of Durham.

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Hyperspectral Pansharpening via Local Intensity Component and Local Injection Gain Estimation

Cited by 9 publications

References 22 publications

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

SWPanGAN: A hybrid generative adversarial network for pansharpening

Hyperspectral VNIR-SWIR image fusion on cultural heritage and remote sensing datasets using image sharpening techniques

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