Hyper-Sharpening Based on Spectral Modulation

Lu, Xiaochen; Zhang, Junping; Yu, Xiangzhen; Tang, Wenming; Li, Tong; Zhang, Ye

doi:10.1109/jstars.2019.2908984

Cited by 15 publications

(8 citation statements)

References 54 publications

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“…in which the center wavelength (cλ) of the individual bands are replaced by the values in Table 1. In both Equations ( 9) and (10), REIP, also termed red-edge position (REP), is a wavelength expressed in nm. REIP is affected by biochemical and biophysical parameters and has been used to estimate leaf chlorophyll or nitrogen content [1].…”

Section: Extraction Of Biophysical Parameters From Hs and Ms Datamentioning

confidence: 99%

“…Alternatively, hyper-sharpening may be brought back to blind source separation concepts [8]. Originally devised for singleplatform data [6,9], hyper-sharpening has been extended to multi-platform data [10,11]. Since hyper-sharpening can be regarded as an MS-to-HS fusion, it can be tackled as problem of spectral unmixing, for which an approach based on spectral profiles has been recently proposed [12].…”

Section: Introductionmentioning

confidence: 99%

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Spatial Resolution Enhancement of Vegetation Indexes via Fusion of Hyperspectral and Multispectral Satellite Data

Alparone,

Arienzo,

Garzelli

2024

Remote Sensing

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The definition and calculation of a spectral index suitable for characterizing vegetated landscapes depend on the number and widths of the bands of the imaging instrument. Here, we point out the advantages of performing the fusion of hyperspectral (HS) satellite data with the multispectral (MS) bands of Sentinel-2 to calculate such vegetation indexes as the normalized area over reflectance curve (NAOC) and the red-edge inflection point (REIP), which benefit from the availability of quasi-continuous pixel spectra. Unfortunately, MS data may be acquired from satellite platforms with very high spatial resolution; HS data may not. Despite their excellent spectral resolution, satellite imaging spectrometers currently resolve areas not greater than 30 × 30 m2, where different thematic classes of landscape may be mixed together to form a unique pixel spectrum. A way to resolve mixed pixels is to perform the fusion of the HS dataset with the same dataset produced by an MS scanner that images the same scene with a finer spatial resolution. The HS dataset is sharpened from 30 m to 10 m by means of the Sentinel-2 bands that have all been previously brought to 10 m. To do so, the hyper-sharpening protocol, that is, m:n fusion, is exploited in two nested steps: the first one to bring the 20 m bands of Sentinel-2 all to 10 m, the second one to sharpen all the 30 m HS bands to 10 m by using the Sentinel-2 bands previously hyper-sharpened to 10 m. Results are presented on an agricultural test site in The Netherlands imaged by Sentinel-2 and by the satellite imaging spectrometer recently launched as a part of the environmental mapping and analysis program (EnMAP). Firstly, the excellent match of statistical consistency of the fused HS data to the original MS and HS data is evaluated by means of analysis tools, existing and developed ad hoc for this specific case. Then, the spatial and radiometric accuracy of REIP and NAOC calculated from fused HS data are analyzed on the classes of pure and mixed pixels. On pure pixels, the values of REIP and NAOC calculated from fused data are consistent with those calculated from the original HS data. Conversely, mixed pixels are spectrally unmixed by the fusion process to resolve the 10 m scale of the MS data. How the proposed method can be used to check the temporal evolution of vegetation indexes when a unique HS image and many MS images are available is the object of a final discussion.

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Section: Extraction Of Biophysical Parameters From Hs and Ms Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Resolution Enhancement of Vegetation Indexes via Fusion of Hyperspectral and Multispectral Satellite Data

Alparone,

Arienzo,

Garzelli

2024

Remote Sensing

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“…HSI fusion algorithms can be roughly divided into three categories: pan-sharpening-based methods [19]- [22], matrix factorization-based methods [23]- [30], and deep learningbased methods [31]- [39].…”

Section: Related Workmentioning

confidence: 99%

A Twice Optimizing Net With Matrix Decomposition for Hyperspectral and Multispectral Image Fusion

Shen

Liu

Xiao

et al. 2020

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

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Fusing a low-resolution hyperspectral (LRHS) image and a high-resolution multispectral (HRMS) image to generate a high-resolution hyperspectral (HRHS) image has grown a significant and attractive application in remote sensing fields. Recently, the popularization of deep learning has injected more possibilities into the fusion work. However, there still exists a difficulty that is how to make the best of the acquired LRHS and HRMS images. In this paper, we present a twice optimizing net with matrix decomposition to fulfill the fusion task, which can be roughly divided into three stages: pre-optimization, deep prior learning, post-optimization. Specifically, we first transform this fusion problem into a spectral optimization problem and a spatial optimization problem with the help of matrix decomposition. These two optimization problems can be handled sequentially by solving a linear equation respectively and then we can obtain the initial HRHS image by multiplying the two solutions. Next, we establish the mapping between the initial image and the reference image through an end-to-end deep residual network based on local and non-local connectivity. In order to get better performance, we have customized a loss function specifically for the fusion task as well. Finally, we return the predicted result again to the optimization procedure to get the final fusion image. After the evaluation on three simulated datasets and one real dataset, it illustrates that the proposed method outperforms many state-of-the-art ones.

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“…For this reason, most current studies utilize synthetic data sets to demonstrate the effectiveness of their approaches, which means that the higher-resolution PAN or MS image is commonly synthesized by band averaging of the HS image. In such situations, only a few studies have taken account of the effects of different platforms or acquisition conditions [20,21]. Therefore, this is a major obstacle in the hyper-sharpening field.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network

Yang

Zhang

et al. 2021

Remote Sensing

Self Cite

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Super-resolution (SR) technology has emerged as an effective tool for image analysis and interpretation. However, single hyperspectral (HS) image SR remains challenging, due to the high spectral dimensionality and lack of available high-resolution information of auxiliary sources. To fully exploit the spectral and spatial characteristics, in this paper, a novel single HS image SR approach is proposed based on a spatial correlation-regularized unmixing convolutional neural network (CNN). The proposed approach takes advantage of a CNN to explore the collaborative spatial and spectral information of an HS image and infer the high-resolution abundance maps, thereby reconstructing the anticipated high-resolution HS image via the linear spectral mixture model. Moreover, a dual-branch architecture network and spatial spread transform function are employed to characterize the spatial correlation between the high- and low-resolution HS images, aiming at promoting the fidelity of the super-resolved image. Experiments on three public remote sensing HS images demonstrate the feasibility and superiority in terms of spectral fidelity, compared with some state-of-the-art HS image super-resolution methods.

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Hyper-Sharpening Based on Spectral Modulation

Cited by 15 publications

References 54 publications

Spatial Resolution Enhancement of Vegetation Indexes via Fusion of Hyperspectral and Multispectral Satellite Data

Spatial Resolution Enhancement of Vegetation Indexes via Fusion of Hyperspectral and Multispectral Satellite Data

A Twice Optimizing Net With Matrix Decomposition for Hyperspectral and Multispectral Image Fusion

Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network

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