Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Mookambiga, A.; Gomathi, V.

doi:10.1007/s11045-016-0415-2

Cited by 62 publications

(39 citation statements)

References 56 publications

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“…To verify the validity of the algorithm, we experimented separately on the synthetic and real data sets. The first set of synthetic data for the experiment was collected by ROSIS [1]. The dataset is denoted as PaviaU dataset.…”

Section: Experimental Results With the Synthetic Datasetmentioning

confidence: 99%

“…The Moffett Field dataset [1] which is provided by Airborne Visible Infrared Image spectrometer (AVIRIS) is utilized for the second experiment to demonstrate the potential of the proposed method in synthetic datasets. These HSIs used in the experiment consist of 176 bands in the spectrum of 0.4 2.5μm − .…”

Section: Experimental Results With the Synthetic Datasetmentioning

confidence: 99%

“…The first two experiments were carried out on the synthetic datasets. In order to prove that this method is also applicable to the real data set, we selected the Hyperion dataset [1] for the third experiment. Hyperion dataset has been obtained by the EO-1 spacecraft.…”

Section: Experimental Results With the Real Datasetmentioning

confidence: 99%

“…However, the HSI and PANI often fail to meet the requirements of high spatial and spectral resolutions for application in remote sensing image classification, target detection and other fields. Therefore, the fusion of the PANI and HSI which is also called pansharpening is of great research and exploration significance to obtain remote sensing images that can simultaneously maintain high spatial resolution and comprehensive spectral information [1][2][3]. In recent years, hyperspectral pansharpening technology has received the attention of researchers and developed rapidly.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Weighted Regression and Guided Filter Hybrid Method for Hyperspectral Pansharpening

Dong¹,

Xiao²

2019

KSII TIIS

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The goal of hyperspectral pansharpening is to combine a hyperspectral image (HSI) with a panchromatic image (PANI) derived from the same scene to obtain a single fused image. In this paper, a new hyperspectral pansharpening approach using adaptive weighted regression and guided filter is proposed. First, the intensity information (INT) of the HSI is obtained by the adaptive weighted regression algorithm. Especially, the optimization formula is solved to obtain the closed solution to reduce the calculation amount. Then, the proposed method proposes a new way to obtain the sufficient spatial information from the PANI and INT by guided filtering. Finally, the fused HSI is obtained by adding the extracted spatial information to the interpolated HSI. Experimental results demonstrate that the proposed approach achieves better property in preserving the spectral information as well as enhancing the spatial detail compared with other excellent approaches in visual interpretation and objective fusion metrics.

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Section: Experimental Results With the Synthetic Datasetmentioning

confidence: 99%

Section: Experimental Results With the Synthetic Datasetmentioning

confidence: 99%

Section: Experimental Results With the Real Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Weighted Regression and Guided Filter Hybrid Method for Hyperspectral Pansharpening

Dong¹,

Xiao²

2019

KSII TIIS

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“…The first data set is the Moffett field data set, acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor [62], which can provide the HS image 224 bands in the spectral range of 400-2500 nm. Water absorption bands and damaged bands are discarded, and 176 bands are used for the experiment.…”

Section: Data Setmentioning

confidence: 99%

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

Zhong

Lei

et al. 2019

Remote Sensing

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Hyperspectral (HS) imaging is conducive to better describing and understanding the subtle differences in spectral characteristics of different materials due to sufficient spectral information compared with traditional imaging systems. However, it is still challenging to obtain high resolution (HR) HS images in both the spectral and spatial domains. Different from previous methods, we first propose spectral constrained adversarial autoencoder (SCAAE) to extract deep features of HS images and combine with the panchromatic (PAN) image to competently represent the spatial information of HR HS images, which is more comprehensive and representative. In particular, based on the adversarial autoencoder (AAE) network, the SCAAE network is built with the added spectral constraint in the loss function so that spectral consistency and a higher quality of spatial information enhancement can be ensured. Then, an adaptive fusion approach with a simple feature selection rule is induced to make full use of the spatial information contained in both the HS image and PAN image. Specifically, the spatial information from two different sensors is introduced into a convex optimization equation to obtain the fusion proportion of the two parts and estimate the generated HR HS image. By analyzing the results from the experiments executed on the tested data sets through different methods, it can be found that, in CC, SAM, and RMSE, the performance of the proposed algorithm is improved by about 1.42%, 13.12%, and 29.26% respectively on average which is preferable to the well-performed method HySure. Compared to the MRA-based method, the improvement of the proposed method in in the above three indexes is 17.63%, 0.83%, and 11.02%, respectively. Moreover, the results are 0.87%, 22.11%, and 20.66%, respectively, better than the PCA-based method, which fully illustrated the superiority of the proposed method in spatial information preservation. All the experimental results demonstrate that the proposed method is superior to the state-of-the-art fusion methods in terms of subjective and objective evaluations.

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Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species‐rich grasslands

Rossi

Kneubühler

Schütz

et al. 2021

Remote Sens Ecol Conserv

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Increasing evidence suggests that remotely sensed spectral diversity is linked to plant species richness. However, a conflicting spectral diversity–biodiversity relationship in grasslands has been found in previous studies. In particular, it remains unclear how well the spectral diversity–biodiversity relationship holds in naturally assembled species‐rich grasslands. To address the linkage between spectral diversity and plant species richness in a species‐rich alpine grassland ecosystem, we investigated (i) the trade‐off between spectral and spatial resolution in remote sensing data; (ii) the suitability of three different spectral metrics to describe spectral diversity (coefficient of variation, convex hull volume and spectral species richness) and (iii) the importance of confounding effects of live plant biomass, dead plant biomass and plant life forms on the spectral diversity–biodiversity relationship. We addressed these questions using remote sensing data collected with consumer‐grade cameras with four spectral bands and 10 cm spatial resolution on an unmanned aerial vehicle (UAV), airborne imaging spectrometer data (AVIRIS‐NG) with 372 bands and 2.5 m spatial resolution, and a fused data product of both datasets. Our findings suggest that a fused dataset can cope with the requirement of both high spatial‐ and spectral resolution to remotely measure biodiversity. However, in contrast to several previous studies, we found a negative correlation between plant species richness and spectral metrics based on the spectral information content (i.e. spectral complexity). The spectral diversity calculated based on the spectral complexity was sensitive to live and dead plant biomass. Overall, our results suggest that remote sensing of plant species diversity requires a high spatial resolution, the use of classification‐based spectral metrics, such as spectral species richness, and awareness of confounding factors (e.g. plant biomass), which may be ecosystem specific.

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Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Cited by 62 publications

References 56 publications

An Adaptive Weighted Regression and Guided Filter Hybrid Method for Hyperspectral Pansharpening

An Adaptive Weighted Regression and Guided Filter Hybrid Method for Hyperspectral Pansharpening

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species‐rich grasslands

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