A data-driven approach to quality assessment for hyperspectral systems

Kerr, Grégoire; Fischer, Christian; Reulke, Ralf

doi:10.1016/j.cageo.2015.07.004

Cited by 4 publications

(2 citation statements)

References 52 publications

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“…Within model 1, the R 2 values between some SVIs and NEE showed variations among the three different flights (e.g., NDVI performance was much better in flight 3 in respect to the other two flights). Such variations very likely result from uncertainties which are typically associated with both EC flux [49][50][51] and spectral measurements [52,53], and from the constrained number of tower observations per flight available in the study. However, distinctive results were shown by both model 1 and model 2.…”

Section: Chlorophyll and Structural Controls On Ecosystem Functionmentioning

confidence: 99%

Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps

et al. 2019

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The linearity and scale-dependency of ecosystem biodiversity and productivity relationships (BPRs) have been under intense debate. In a changing climate, monitoring BPRs within and across different ecosystem types is crucial, and novel remote sensing tools such as the Sentinel-2 (S2) may be adopted to retrieve ecosystem diversity information and to investigate optical diversity and productivity patterns. But are the S2 spectral and spatial resolutions suitable to detect relationships between optical diversity and productivity? In this study, we implemented an integrated analysis of spatial patterns of grassland productivity and optical diversity using optical remote sensing and Eddy Covariance data. Across-scale optical diversity and ecosystem productivity patterns were analyzed for different grassland associations with a wide range of productivity. Using airborne optical data to simulate S2, we provided empirical evidence that the best optical proxies of ecosystem productivity were linearly correlated with optical diversity. Correlation analysis at increasing pixel sizes proved an evident scale-dependency of the relationships between optical diversity and productivity. The results indicate the strong potential of S2 for future large-scale assessment of across-ecosystem dynamics at upper levels of observation.

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Section: Chlorophyll and Structural Controls On Ecosystem Functionmentioning

confidence: 99%

Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps

et al. 2019

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“…It has been shown that hyperspectral image (HSI) statistics tend to display "heavy tails" (Manolakis2003)(Theiler2005), rendering most of the projection pursuit methods hard to use. Taking into consideration the magnitude of described deviations of observed data PDFs from normal distribution, it is apparent that a priori knowledge of variance in data caused by the imaging system is to be employed in order to efficiently classify objects on HSIs (Kerr, 2015), especially in cases of wildly varying SNR. A number of attempts to describe this variance and compensating techniques has been made (Aiazzi2006), however, new data quality standards are not yet set and accounting for the detector response is made under large set of assumptions.…”

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confidence: 99%

Accounting for Variance in Hyperspectral Data Coming From Limitations of the Imaging System

Shurygin

Shestakova

Nikolenko

et al. 2016

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

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ABSTRACT:Over the course of the past few years, a number of methods was developed to incorporate hyperspectral imaging specifics into generic data mining techniques, traditionally used for hyperspectral data processing. Projection pursuit methods embody the largest class of methods empoyed for hyperspectral image data reduction, however, they all have certain drawbacks making them either hard to use or inefficient. It has been shown that hyperspectral image (HSI) statistics tend to display "heavy tails" (Manolakis2003)(Theiler2005), rendering most of the projection pursuit methods hard to use. Taking into consideration the magnitude of described deviations of observed data PDFs from normal distribution, it is apparent that a priori knowledge of variance in data caused by the imaging system is to be employed in order to efficiently classify objects on HSIs (Kerr, 2015), especially in cases of wildly varying SNR. A number of attempts to describe this variance and compensating techniques has been made (Aiazzi2006), however, new data quality standards are not yet set and accounting for the detector response is made under large set of assumptions. Current paper addresses the issue of hyperspectral image classification in the context of different variance sources based on the knowledge of calibration curves (both spectral and radiometric) obtained for each pixel of imaging camera. A camera produced by ZAO NPO Lepton (Russia) was calibrated and used to obtain a test image. A priori known values of SNR and spectral channel cross-correlation were incorporated into calculating test statistics used in dimensionality reduction and feature extraction. Expectation-Maximization classification algorithm modification for non-Gaussian model as described by (Veracini2010) was further employed. The impact of calibration data coarsening by ignoring non-uniformities on false alarm rate was studied. Case study shows both regions of scene-dominated variance and sensor-dominated variance, leading to different preprocession parameters and, ultimatively, classification results. A multilevel system for denoting hyperspectral pushbroom scanners calibration quality was proposed.

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Assessing cumulative uncertainties of remote sensing time series and telemetry data in animal-environment studies

Standfuß,

Geiß,

Senaratne

et al. 2024

Landsc Ecol

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Context Remote sensing time series (hereafter called time series) and telemetry data are widely used to study animal-environment relationships. However, both data sources are subject to uncertainties that can cause erroneous conclusions. To date, only the uncertainty of telemetry data can be estimated, e.g. through movement modelling, while information on the uncertainty of time series is often lacking. Consequently, it remains challenging to assess if and how the results of animal-environment studies are affected by cumulative uncertainties of telemetry and time series data. Objectives To address this gap, we proposed an approach to approximate time series uncertainties. Coupled with movement modelling, this allows to determine whether the results of animal-environment studies are robust to the cumulative uncertainties of time series and telemetry data. We demonstrated the procedure with a study that used time series to distinguish periods of favourable/poor prey accessibility for white storks. Our objective was to test whether the storks’ preference for fields during periods of favourable prey accessibility could be validated despite the uncertainties. Methods We estimated the telemetry data uncertainties based on continuous-time movement modelling, and approximated time series uncertainties based on data subsampling. We used Monte Carlo simulations to propagate the uncertainties and to generate several estimates of the stork habitat use and levels of prey accessibility. These data were applied in two habitat selection analyses to derive probability distributions of the analyses results, allowing us to characterise the output uncertainties. Results We found that, after accounting for uncertainty, favourable and poor prey accessibility periods were well discriminated, with storks showing the expected degree of preference/avoidance for them. However, our uncertainty analysis also showed, that compared to croplands, grasslands required more temporal NDVI samples to reliably identify these periods. Furthermore, the NDVI itself did not appear to be a coherent predictor of stork habitat selection when uncertainties were accounted for. Conclusion Our findings highlight the importance of validating results by assessing and quantifying the effect of input data uncertainties in animal-environment studies. To our knowledge, the approach presented is the first to assess the cumulative uncertainty of time series and telemetry data, hopefully raising awareness of the consequences of input data uncertainties for future studies.

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A data-driven approach to quality assessment for hyperspectral systems

Cited by 4 publications

References 52 publications

Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps

Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps

Accounting for Variance in Hyperspectral Data Coming From Limitations of the Imaging System

Assessing cumulative uncertainties of remote sensing time series and telemetry data in animal-environment studies

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