Different colours of shadows: classification of UAV images

Milas, Anita Šimić; Arend, Kristin K.; Mayer, Christine M.; Simonson, Martin A.; Mackey, Scudder D.

doi:10.1080/01431161.2016.1274449

Cited by 56 publications

(39 citation statements)

References 30 publications

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“…In contrast, other studies showed that the inclusion of shadows into the training samples improved classification performances (Milas et al. ; Ishida et al. ).…”

Section: Discussionmentioning

confidence: 83%

“…Therefore, careful consideration regarding acquisition time of the day is particularly important, as during some parts of the day shadows can cover a large part of the area of interest (Milas et al. ).…”

Section: Introductionmentioning

confidence: 99%

“…In practice, shadows can lead to either a reduction or a total loss of the spectral signal of a canopy (Zhang et al 2015), affecting the success of classification tasks (Saha et al 2005;Liu and Yamazaki 2012). Therefore, careful consideration regarding acquisition time of the day is particularly important, as during some parts of the day shadows can cover a large part of the area of interest (Milas et al 2017).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

Lopatin

Dološ

Kattenborn

2019

Remote Sens Ecol Conserv

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Plant invasions can result in serious threats for biodiversity and ecosystem functioning. Reliable maps at very‐high spatial resolution are needed to assess invasions dynamics. Field sampling approaches could be replaced by unmanned aerial vehicles (UAVs) to derive such maps. However, pixel‐based species classification at high spatial resolution is highly affected by within‐canopy variation caused by shadows. Here, we studied the effect of shadows on mapping the occurrence of invasive species using UAV‐based data. MaxEnt one‐class classifications were applied to map Acacia dealbata, Ulex europaeus and Pinus radiata in central‐south Chile using combinations of UAV‐based spectral (RGB and hyperspectral), 2D textural and 3D structural variables including and excluding shaded canopy pixels during model calibration. The model accuracies in terms of area under the curve (AUC), Cohen's Kappa, sensitivity (true positive rate) and specificity (true negative rate) were examined in sunlit and shaded canopies separately. Bootstrapping was used for validation and to assess statistical differences between models. Our results show that shadows significantly affect the accuracies obtained with all types of variables. The predictions in shaded areas were generally inaccurate, leading to misclassification rates between 65% and 100% even when shadows were included during model calibration. The exclusion of shaded areas from model calibrations increased the predictive accuracies (especially in terms of sensitivity), decreasing false positives. Spectral and 2D textural information showed generally higher performances and improvements when excluding shadows from the analysis. Shadows significantly affected the model results obtained with any of the variables used, hence the exclusion of shadows is recommended prior to model calibration. This relatively easy pre‐processing step enhances models for classifying species occurrences using high‐resolution spectral imagery and derived products. Finally, a shadow simulation showed differences in the ideal acquisition window for each species, which is important to plan revisit campaigns.

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“…In contrast, other studies showed that the inclusion of shadows into the training samples improved classification performances (Milas et al. ; Ishida et al. ).…”

Section: Discussionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

Lopatin

Dološ

Kattenborn

2019

Remote Sens Ecol Conserv

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show abstract

“…moving trees, occlusions, shadows, images radiometric quality, etc. ), which seriously affect the image matching procedure, and thus DSM quality [23,25,40,41].…”

Section: Resultsmentioning

confidence: 99%

The Evaluation of Photogrammetry-Based DSM from Low-Cost UAV by LiDAR-Based DSM

Gašparović

Seletković

Berta

et al. 2017

SEEFOR

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“…The use of region growing approaches to get the correct shadow area borders [37] is a viable addition to these methods. Methods directly using the shadow properties are either based on thresholds to make use of the relative darkness of shadows [38,39], or based on the color properties of shadows [40,41]. Using a blackbody radiation model may further increase the reliability of shadow detection [42].…”

Section: Cast Shadow Detection Methodsmentioning

confidence: 99%

Cast Shadow Detection to Quantify the Aerosol Optical Thickness for Atmospheric Correction of High Spatial Resolution Optical Imagery

Schläpfer¹,

Hueni

Richter

2018

Remote Sensing

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Abstract:The atmospheric correction of optical remote sensing data requires the determination of aerosol and gas optical properties. A method is presented which allows the detection of the aerosol scattering effects from optical remote sensing data at spatial sampling intervals below 5 m in cloud-free situations from cast shadow pixels. The derived aerosol optical thickness distribution is used for improved atmospheric compensation. In a first step, a novel spectral cast shadow detection algorithm determines the shadow areas using spectral indices. Evaluation of the cast shadow masks shows an overall classification accuracy on an 80% level. Using the such derived shadow map, the ATCOR atmospheric compensation method is iteratively applied on the shadow areas in order to find the optimum aerosol amount. The aerosol optical thickness is found by analyzing the physical atmospheric correction of fully shaded pixels in comparison to directly illuminated areas. The shadow based aerosol optical thickness estimation method (SHAOT) is tested on airborne imaging spectroscopy data as well as on photogrammetric data. The accuracy of the reflectance values from atmospheric correction using the such derived aerosol optical thickness could be improved from 3-4% to a level of better than 2% in reflectance for the investigated test cases.

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Different colours of shadows: classification of UAV images

Cited by 56 publications

References 30 publications

How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

The Evaluation of Photogrammetry-Based DSM from Low-Cost UAV by LiDAR-Based DSM

Cast Shadow Detection to Quantify the Aerosol Optical Thickness for Atmospheric Correction of High Spatial Resolution Optical Imagery

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