Noise Reduction in Hyperspectral Images Through Spectral Unmixing

Cerra, Daniele; Müller, Rupert; Reinartz, Peter

doi:10.1109/lgrs.2013.2247562

Cited by 57 publications

(42 citation statements)

References 17 publications

(23 reference statements)

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“…The fusion has produced a much clearer image, with clear and homogeneous geographic features that are barely observable in the original. Similar results have also been reported by Cerra et al [38] in the context of hyperspectral image denoising. …”

Section: Effect Of the Sparsity Termsupporting

confidence: 90%

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

2017

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Hyperspectral sensors capture a portion of the visible and near-infrared spectrum with many narrow spectral bands. This makes it possible to better discriminate objects based on their reflectance spectra and to derive more detailed object properties. For technical reasons, the high spectral resolution comes at the cost of lower spatial resolution. To mitigate that problem, one may combine such images with conventional multispectral images of higher spatial, but lower spectral resolution. The process of fusing the two types of imagery into a product with both high spatial and spectral resolution is called hyperspectral super-resolution. We propose a method that performs hyperspectral super-resolution by jointly unmixing the two input images into pure reflectance spectra of the observed materials, along with the associated mixing coefficients. Joint super-resolution and unmixing is solved by a coupled matrix factorization, taking into account several useful physical constraints. The formulation also includes adaptive spatial regularization to exploit local geometric information from the multispectral image. Moreover, we estimate the relative spatial and spectral responses of the two sensors from the data. That information is required for the super-resolution, but often at most approximately known for real-world images. In experiments with five public datasets, we show that the proposed approach delivers up to 15% improved hyperspectral super-resolution.

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Section: Effect Of the Sparsity Termsupporting

confidence: 90%

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

2017

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“…The low Signal-to-noise Ratio of (SNR) of the selected PS image prompts the application of the Unmixingbased denoising (UBD) as a Pre-processing step. UBD has been recently proposed to selectively retrieve spectral bands characterised by a low SNR by exploiting their correlation with non-corrupted pixels across the whole spectral dimension in hyperspectral images (Cerra et al, 2014). Spectral unmixing is the process which decomposes a hyperspectral image element into a linear combination of signals representing the backscattered solar radiation in each spectral band.…”

Section: Unmixing-based Denoisingmentioning

confidence: 99%

Cubesat-Derived Detection of Seagrasses Using Planet Imagery Following Unmixing-Based Denoising: Is Small the Next Big?

Traganos

Cerra

Reinartz

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Seagrasses are one of the most productive and widespread yet threatened coastal ecosystems on Earth. Despite their importance, they are declining due to various threats, which are mainly anthropogenic. Lack of data on their distribution hinders any effort to rectify this decline through effective detection, mapping and monitoring. Remote sensing can mitigate this data gap by allowing retrospective quantitative assessment of seagrass beds over large and remote areas. In this paper, we evaluate the quantitative application of Planet high resolution imagery for the detection of seagrasses in the Thermaikos Gulf, NW Aegean Sea, Greece. The low Signal-to-noise Ratio (SNR), which characterizes spectral bands at shorter wavelengths, prompts the application of the Unmixing-based denoising (UBD) as a pre-processing step for seagrass detection. A total of 15 spectral-temporal patterns is extracted from a Planet image time series to restore the corrupted blue and green band in the processed Planet image. Subsequently, we implement Lyzenga's empirical water column correction and Support Vector Machines (SVM) to evaluate quantitative benefits of denoising. Denoising aids detection of Posidonia oceanica seagrass species by increasing its producer and user accuracy by 31.7% and 10.4%, correspondingly, with a respective increase in its Kappa value from 0.3 to 0.48. In the near future, our objective is to improve accuracies in seagrass detection by applying more sophisticated, analytical water column correction algorithms to Planet imagery, developing time-and cost-effective monitoring of seagrass distribution that will enable in turn the effective management and conservation of these highly valuable and productive ecosystems.

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“…In the case of hyperspectral data, the high spectral dimensionality of each image element can be exploited in order to derive a pixel-based denoising exploiting both spatial and spectral information. Unmixing-based Denoising (UBD) is a supervised methodology for the recovery of bands characterized by a low SNR in a hyperspectral scene (Cerra, 2014), which is described in next section.…”

Section: Introductionmentioning

confidence: 99%

Unmixing-Based Denoising as a Pre-Processing Step for Coral Reef Analysis

Cerra

Traganos

Gege

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Coral reefs, among the world's most biodiverse and productive submerged habitats, have faced several mass bleaching events due to climate change during the past 35 years. In the course of this century, global warming and ocean acidification are expected to cause corals to become increasingly rare on reef systems. This will result in a sharp decrease in the biodiversity of reef communities and carbonate reef structures. Coral reefs may be mapped, characterized and monitored through remote sensing. Hyperspectral images in particular excel in being used in coral monitoring, being characterized by very rich spectral information, which results in a strong discrimination power to characterize a target of interest, and separate healthy corals from bleached ones. Being submerged habitats, coral reef systems are difficult to analyse in airborne or satellite images, as relevant information is conveyed in bands in the blue range which exhibit lower signal-to-noise ratio (SNR) with respect to other spectral ranges; furthermore, water is absorbing most of the incident solar radiation, further decreasing the SNR. Derivative features, which are important in coral analysis, result greatly affected by the resulting noise present in relevant spectral bands, justifying the need of new denoising techniques able to keep local spatial and spectral features. In this paper, Unmixing-based Denoising (UBD) is used to enable analysis of a hyperspectral image acquired over a coral reef system in the Red Sea based on derivative features. UBD reconstructs pixelwise a dataset with reduced noise effects, by forcing each spectrum to a linear combination of other reference spectra, exploiting the high dimensionality of hyperspectral datasets. Results show clear enhancements with respect to traditional denoising methods based on spatial and spectral smoothing, facilitating the coral detection task.

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Noise Reduction in Hyperspectral Images Through Spectral Unmixing

Cited by 57 publications

References 17 publications

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

Cubesat-Derived Detection of Seagrasses Using Planet Imagery Following Unmixing-Based Denoising: Is Small the Next Big?

Unmixing-Based Denoising as a Pre-Processing Step for Coral Reef Analysis

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