ICARE-HS: atmospheric correction of airborne hyperspectral urban images using 3D information

Ceamanos, Xavier; Briottet, Xavier; Roussel, Guillaume; Gilardy, Hugo

doi:10.1117/12.2241799

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Indeed it appears that the classification performances obtained with a ground sample distance equal to 8 m (the planned spatial resolution of HYPXIM) are significantly better than those obtained with a ground sample distance equal to 10 m (the planned spatial resolution of SHALOM). Future work will consider the addition of the ICARE icare_XC (Ceamanos et al 2016) atmospheric correction algorithm to the comparison process. Such a method, which uses a digital elevation model as an a priori, may perform even better than the empirical method on high spatial resolution data sets due to the consideration of 3D data on the surface.…”

Section: Discussionmentioning

confidence: 99%

Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution

Roussel

Weber

Briottet

et al. 2017

International Journal of Remote Sensing

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For remote-sensing applications such as spectra classification or identification, atmospheric correction constitutes a very important pre-processing step, especially in complex urban environments where a lot of phenomenons alter the shape of the signal. The objective of this article is to compare the efficiency of two atmo-spheric correction algorithms, COCHISE (atmospheric COrrection Code for Hyperspectral Images of remote-sensing SEnsors) and an empirical method, on hyperspectral data and for classification applications. Classification is carried out on several simulated spaceborne data sets with different spatial resolutions (from 1.6 to 9.6 m). Four classifiers are considered in the study: a k-means, a Support Vector Machine (SVM), and a sun/ shadow version of each of them, which processes sunlit and shadowed pixels separately. Results show that the most relevant atmospheric method for classification depends on the spatial resolution of the processed data set. Indeed, if the empirical method performs better on high-resolution data sets (up to 4%), its superiority fades out as the spatial resolution decreases, especially with the lower spatial reso-lution where COCHISE can be 10% more accurate than the empiri-cal method.

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Section: Discussionmentioning

confidence: 99%

Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution

Roussel

Weber

Briottet

et al. 2017

International Journal of Remote Sensing

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“…Unlike other shadow correction methods such as radiometric enhancement and multisource data fusion methods [10,11], they are physically based and convert the image radiance units into reflectance ones. They rely on the modeling of all the radiative contributions (irradiance and radiance terms) coming from the atmosphere and the surroundings with their 3D geometric attributes in order to process shaded areas [12][13][14][15][16][17]. Good performances in surface reflectance retrieval are achieved in shadows cast by buildings because they behave as opaque materials and their shadows mainly receive diffuse irradiance.…”

Section: Introductionmentioning

confidence: 99%

Impact of Tree Crown Transmittance on Surface Reflectance Retrieval in the Shade for High Spatial Resolution Imaging Spectroscopy: A Simulation Analysis Based on Tree Modeling Scenarios

et al. 2021

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With the advancement of high spatial resolution imaging spectroscopy, an accurate surface reflectance retrieval is needed to derive relevant physical variables for land cover mapping, soil, and vegetation monitoring. One challenge is to deal with tree shadows using atmospheric correction models if the tree crown transmittance Tc is not properly taken into account. This requires knowledge of the complex radiation mechanisms that occur in tree crowns, which can be provided by coupling the physical modeling of canopy radiative transfer codes (here DART) and the 3D representations of trees. First in this study, a sensitivity analysis carried out on DART simulations with an empirical 3D tree model led to a statistical regression predicting Tc from the tree leaf area index (LAI) and the solar zenith angle with good performances (RMSE ≤ 4.3% and R2 ≥ 0.91 for LAI ≤ 4 m2.m-2). Secondly, more realistic 3D voxel-grid tree models derived from terrestrial LiDAR measurements over two trees were considered. The comparison of DART-simulated Tc from these models with the previous predicted Tc over 0.4–2.5 µm showed three main sources of inaccuracy quoted in order of importance: 1) the global tree geometry shape (mean bias up to 21.5%), 2) the transmittance fraction associated to multiple scattering, Tscat (maximum bias up to 13%), and 3) the degree of realism of the tree representation (mean bias up to 7.5%). Results showed that neglecting Tc leads to very inaccurate reflectance retrieval (mean bias > 0.04), particularly if the background reflectance is high, and in the near and shortwave infrared – NIR and SWIR – due to Tscat. The transmittance fraction associated to the non-intercepted transmitted light, Tdir, can reach up to 95% in the SWIR, and Tscat up to 20% in the NIR. Their spatial contributions computed in the tree shadow have a maximum dispersion of 27% and 8% respectively. Investigating how to approximate Tdir and Tscat spectral and spatial variability along with the most appropriate tree 3D modeling is crucial to improve reflectance retrieval in tree shadows when using atmospheric correction models.

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ICARE-HS: atmospheric correction of airborne hyperspectral urban images using 3D information

Cited by 2 publications

References 8 publications

Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution

Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution

Impact of Tree Crown Transmittance on Surface Reflectance Retrieval in the Shade for High Spatial Resolution Imaging Spectroscopy: A Simulation Analysis Based on Tree Modeling Scenarios

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