Cloud Probability Mask for PROBA/CHRIS Hyperspectral Imagesearth.esa.int/workshops/4th_chris_proba/CHRIS_WS4_GUANER_L.pdf · 4th ESA CHRIS Proba Workshop, ESA-ESRIN, September 19-21,

4th ESA CHRIS Proba Workshop, ESA-ESRIN, September 19-21, 2006

Cloud Probability Mask for PROBA/CHRIS Hyperspectral Images

L. Gómez-Chovaa, G. Camps-Vallsa, J. Amorós-Lópeza, J. Calpea, L. Alonsob, L. Guanterb, J.C. Forteab, and J. Morenob

a GPDS, Electronics Engineering Dep.b LEO, Earth Sciences and Thermodynamics Dep.

University of Valencia, Spain


OutlineINTRODUCTION METHODS MATERIAL RESULTS CONCLUSIONS

• Motivation

• Proposed cloud screening method

• CHRIS/PROBA hyperspectral images

• Results

• Conclusions

• Further Work


Aim of the WorkINTRODUCTION METHODS MATERIAL RESULTS CONCLUSIONS

• Undetected clouds are the most significant source of error for true ground reflectance estimation.

• CHRIS acquisition plan tries to avoid cloud covers, but from time to time images are partially affected by clouds.

• This work presents a methodology to identify the location and abundance of clouds in CHRIS hyperspectral images.

- The method takes advantage of the specific CHRIS image characteristics (spectral range, number of bands, etc)

- The method provides probability and cloud abundance rather than flags, which can be used to describe detected clouds (subpixel coverage, cloud type, height) more accurately.


• The method combines the information of the spectral signature with specific cloud-discriminant features.

Method SchemeINTRODUCTION METHODS MATERIAL RESULTS CONCLUSIONS

RAW Data




RAW DataTOA

Reflectance




RAW DataTOA

Reflectance

Cloud Features

Feature Extraction




RAW DataTOA

Reflectance

Cloud Features

Feature Extraction

Clustering of Features

Cluster Centers

Probabilistic Membership




RAW DataTOA

Reflectance

Cloud Features

Feature Extraction

Spectral Unmixing

Endmember Abundances


Cluster Centers





RAW DataTOA

Reflectance

Cloud Features

Feature Extraction

Spectral Unmixing



Cluster Centers


Labeling of Cloud Clusters

Cloud Abundance

Cloud Probability




RAW DataTOA

Reflectance

Cloud Features

Feature Extraction

Spectral Unmixing



Cluster Centers



Cloud Abundance

Cloud Probability

Cloud Product




RAW DataTOA

Reflectance

Cloud Features

Feature Extraction

Spectral Unmixing



Cluster Centers



Cloud Abundance

Cloud Probability

Cloud Product

Spectral Signature

Cloud Features

1

2

4

3


Pre-processing• CHRIS noise correction to obtain accurate data:

- Drop-out noise (wrong values in odd pixels)

- Vertical stripping (multiplicative noise pattern)

• Top Of Atmosphere (TOA) apparent reflectance is estimated to be independent on the illumination conditions (day of the year and angular configuration):

INTRODUCTION METHODS MATERIAL RESULTS CONCLUSIONS

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Normalized Atmospheric Transmittance in the 760.625nm absorption band

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Spectral efficiency of sensor channels

wavelength (nm)400 450 500 550 600 650 700 750 800 850 900

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0.5Estimated absorption at 760.625nm

wavelength (nm)

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Oxygen absorption

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Fig. 1. Intermediate products used to estimate the optical path, d, from the O2 absorption band. Left: effective atmospheric vertical transmittance, exp(!!(")),estimated for the MERIS channels. Center: the interpolated, #0("), and measured, #("), reflectances inside the oxygen band, and the estimated reflectanceat the maximum absorption ("=760.625 nm). Right: Estimation of the optical path from the oxygen absorption band.

and observation geometry information and a digital elevationmodel (DEM). Both MERIS and CHRIS products are providedin top of the atmosphere (TOA) radiance (radiometricallycalibrated data). However, the method must work under manysituations. Therefore, TOA reflectance is estimated to removethe dependence on particular illumination conditions (day ofthe year and angular configuration) and illumination effectsdue to rough terrain (cosine correction). TOA apparent re-flectance is estimated according to:

!(") =# · L(")

cos($I) · I("), (1)

where L(") is the provided at sensor upward TOA radiance,I(") is the extraterrestrial instantaneous solar irradiance, and$I is the angle between the illumination direction and thevector perpendicular to the surface.

Essentially, the cloud-detection algorithm is constituted bythe following steps:

1) Feature extraction: physically-inspired features are ex-tracted to increase separability of clouds and surface.

2) Region of interest: growing maps are built from cloud-like pixels in order to select regions which potentiallycould contain clouds.

3) Image clustering and labeling: an unsupervised cluster-ing is applied to the extracted features and the result-ing clusters are subsequently labeled into geo-physicalclasses according to their spectral signatures.

4) Spectral unmixing: a spectral unmixing is applied to thesegmented image in order to obtain an abundance mapof the cloud content in the cloud pixels.

A. Feature Extraction

The measured spectral signature depends on the illumina-tion, the atmosphere, and the surface. Spectral bands free fromatmospheric absorptions contain information about the surfacereflectance, while others are mainly affected by the atmo-sphere. Physically-inspired features that increase separabilityof clouds and surface covers can be extracted independentlyfrom the bands that are free from atmospheric effects and fromthe bands affected by the atmosphere.

Regarding the reflectance of the surface, a characteristic ofclouds is that they present bright and white spectra.

• A bright spectrum means that the intensity of the spectralcurve (related to the albedo) should present relatively highvalues. Therefore, cloud brightness is calculated as theintegral of spectrum,

!!(")d".

• A white spectrum means that the first derivative ofthe spectral curve should present low values. For thepresent method, the average of the spectral derivative,!d!(")/d"", has been chosen as one of the features.

Regarding the atmospheric absorptions present in the spec-trum of a pixel, another meaningful feature is the fact thatclouds are at a higher altitude than the surface. It is worthnoting that atmospheric absorption depends on the atmosphericcomponents and the optical path. Since light reflected on highclouds crosses a shorter section of the atmosphere, the conse-quence would be an abnormally short optical path, thus weakeratmospheric absorption features. Atmospheric oxygen or watervapour absorptions (at 760 nm and 940 nm respectively) canbe used to estimate this optical path.

The light transmitted through a non-dispersive medium canbe expressed by (Bouguer-Lambert-Beer law):

L(") = L0(") ·exp(#%/µ) = L0(") ·exp(#%(") ·d/µ) , (2)

where L0(") is the initial light, the term exp(#%/µ) is thetransmittance factor, 1/µ is the optical mass, % is the opticaldepth, %(") is the atmospheric optical depth for a verticalpath, and d is a factor accounting for the path crossed by theradiation1. Assuming that there are no horizontal variations inthe atmospheric component concentrations, one can considerthat the optical path only depends on the height. Therefore, anestimation of the atmospheric absorption provides a measureof the optical path:

d = µ# ln(L(")/L0("))

%("), (3)

where the effective atmospheric vertical transmittance,exp(#%(")), is estimated for the channels of the instrument

1d can be interpreted as a product of the component concentration and thedistance crossed by the radiation that will be approximately 1 when the lightcrosses one atmosphere with a vertical path

flat landscape and constant illumination angle (Solar Zenith Angle)


Vertical Stripping• Non-uniformities in the CCD response and slit width produce a

vertical pattern in the resulting image known as ‘Vertical Stripping’.

• A noise reduction must be performed in the pre-processing phase since some extracted cloud features are affected by the stripping.

• New technique1 to remove vertical stripping:

- Excluding surface patterns with spatial frequencies of the same order than vertical stripping noise.

- Using a low pass filter robust to outliers and estimating the most adequate cut-off frequency for each image band.

[1] “Modelling spatial and spectral systematic noise patterns on CHRIS/PROBA hyperspectral data”, L. Gómez-Chova, L. Alonso, L. Guanter, G. Camps-Valls, J. Calpe, and J. Moreno. SPIE Remote Sensing Symposium 2006, Stockholm, Sweden, 11-14 September 2006.



Vertical StrippingINTRODUCTION METHODS MATERIAL RESULTS CONCLUSIONS


Feature Extraction• Physically-inspired features are extracted to increase separability

of clouds vs. any-other surface type.

• The extraction takes into account that the measured spectral signature depends on:

- Illumination → partially removed in TOA apparent reflectance

- Atmosphere → cloud-pixels present lower atmospherical absorptions

- Surface → cloud-pixels are bright and white


Mode 1 CHRIS spectral bandsAtmospheric transmissionVegetation spectral signature


Feature Extraction• Regarding surface reflectance: clouds present a bright spectrum

(high intensity) and a white spectrum (null spectral derivative).


Spectral intensity VIS

0.05

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0.15

0.2

0.25

Mean spectral derivative VIS

!8

!6

!4

!2

0

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x 10!4

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1.2


wavelength (nm)400 450 500 550 600 650 700 750 800 850 900

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wavelength (nm)

TO

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efle

ctan

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Oxygen absorption

0

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ρ(λ) =π · L(λ)

cos(θI) · I(λ), (1)

where L(λ) is the provided at sensor upward TOA radiance,I(λ) is the extraterrestrial instantaneous solar irradiance, andθI is the angle between the illumination direction and thevector perpendicular to the surface.










!ρ(λ)dλ.

• A white spectrum means that the first derivative ofthe spectral curve should present low values. For thepresent method, the average of the spectral derivative,!dρ(λ)/dλ", has been chosen as one of the features.



L(λ) = L0(λ) ·exp(#τ/µ) = L0(λ) ·exp(#τ(λ) ·d/µ) , (2)

where L0(λ) is the initial light, the term exp(#τ/µ) is thetransmittance factor, 1/µ is the optical mass, τ is the opticaldepth, τ(λ) is the atmospheric optical depth for a verticalpath, and d is a factor accounting for the path crossed by theradiation1. Assuming that there are no horizontal variations inthe atmospheric component concentrations, one can considerthat the optical path only depends on the height. Therefore, anestimation of the atmospheric absorption provides a measureof the optical path:

d = µ# ln(L(λ)/L0(λ))

τ(λ), (3)

where the effective atmospheric vertical transmittance,exp(#τ(λ)), is estimated for the channels of the instrument


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wavelength (nm)400 450 500 550 600 650 700 750 800 850 900

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wavelength (nm)

TO

A r

efle

ctan

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Oxygen absorption

0

0.1

0.2

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0.5

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Fig. 1. Intermediate products used to estimate the optical path, d, from the O2 absorption band. Left: effective atmospheric vertical transmittance, exp(!τ(λ)),estimated for the MERIS channels. Center: the interpolated, ρ0(λ), and measured, ρ(λ), reflectances inside the oxygen band, and the estimated reflectanceat the maximum absorption (λ=760.625 nm). Right: Estimation of the optical path from the oxygen absorption band.


!(") =# · L(")

cos($I) · I("), (1)











!!(")d".




L(") = L0(") ·exp(#%/µ) = L0(") ·exp(#%(") ·d/µ) , (2)


d = µ# ln(L(")/L0("))

%("), (3)




Feature Extraction• Regarding atmospheric absorptions: clouds are at a higher altitude

than the surface and light reflected on high clouds crosses a shorter section of the atmosphere short optical path.


755 760 765 770 7750

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1

Normalized Atmospheric Transmisivity in the Oxygen absorption band

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1

1.2Spectral efficiency of CHRIS channels

wavelength (nm)400 500 600 700 800 900 1000

0.05

0.1

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0.35

0.4

0.45

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0.55Oxygen absorption estimation

wavelength (nm)

TOA

refle

ctan

ce

Optical Path

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750 755 760 765 770 775 780 785

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wavelength (nm)400 450 500 550 600 650 700 750 800 850 900

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wavelength (nm)

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Oxygen absorption

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!(") =# · L(")

cos($I) · I("), (1)











!!(")d".




L(") = L0(") ·exp(#%/µ) = L0(") ·exp(#%(") ·d/µ) , (2)


d = µ# ln(L(")/L0("))

%("), (3)



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wavelength (nm)400 450 500 550 600 650 700 750 800 850 900

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wavelength (nm)

TO

A r

efle

ctan

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Oxygen absorption

0

0.1

0.2

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!(") =# · L(")

cos($I) · I("), (1)











!!(")d".




L(") = L0(") ·exp(#%/µ) = L0(") ·exp(#%(") ·d/µ) , (2)


d = µ# ln(L(")/L0("))

%("), (3)




Feature Extraction• In CHRIS images atmospheric oxygen and water vapor

absorptions can be used to estimate this short optical path.


Normalized Index (882 945 nm) of Water Vapour absorption

0

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Oxygen absorption

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Clustering of Features• After obtaining a set of features that provides good discrimination

between clouds and the “background”, the objective is to find clouds in the analyzed image.

x Static thresholds applied to every pixel in the image can fail due to subpixel clouds, sensor calibration, variation of the spectral response of clouds with cloud type and height, etc.

• Unsupervised segmentation find clusters of similar pixels

1. Region of interest (ROI): Growing maps are built from cloud-like pixels in order to select regions which potentially could contain cloud covers.

2. Image clustering: An unsupervised Expectation-Maximization clustering algorithm is applied using all extracted features in order to obtain all the existing clusters over the previously identified areas and a probabilistic membership of pixels to each cluster.



Clustering of FeaturesINTRODUCTION METHODS MATERIAL RESULTS CONCLUSIONS

Water and Cloud Mask

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Region of InterestEM clustering

Clustering of selected areas

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The Expectation-Maximization (EM) algorithm considers the full relationship among variables and provides probability maps for each cluster

when applying a Gaussian Maximum Likelihood (GML) classifier.


Spectral Unmixing• In order to obtain a cloud abundance map for every pixel in the

image, rather than flags or a binary classification, a spectral unmixing algorithm is applied using full spectral information.

• The spectral signatures of the cluster centers are considered as the representative pixels (endmembers) of the covers present in the scene (including cloud clusters)

• Fully Constrained Linear Spectral Unmixing (FCLSU) algorithm: ρk = M· ak + noise , where M is the matrix of endmembers

• The vector ak contains the abundances for the pixel k of each cluster spectral signature i (abundances are constrained to be aik ∈ [0, 1] and ∑ aik = 1).



Labeling of Cloud Clusters•


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1.4Cluster centers

wavelength (nm)

TOA

Appa

rent

Ref

lect

ance

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5 67

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Labeling

Classification of selected areas

0:background

1:bright clouds

2:bright clouds

3:soil

4:bright clouds

5:cirrus

6:clouds

7:vegetation

8:shadows

The obtained clusters are labeled into geo-physical classes taking

into account the spectral signature and location of the cluster centers.

-by the user-spectral library-classification


Cloud Products• Once we know the clusters corresponding to cloud covers its

possible to merge all the cloud-clusters (clusters belonging to a cloud type) in both types of information:

• Using the clustering of the extracted features:

- The EM algorithm provides posterior probabilities (Pik ∈ [0, 1] , ∑ Pik = 1)

and a probabilistic cloud index can be computed as the sum of the posteriors of the cloud-clusters: Cloud Probability = ∑ Pik ∀i classified as cloud

• Using the unmixing of the spectral signatures:

- Similarly, FCLSU provides abundances for all clusters (aik ∈ [0, 1] , ∑ aik = 1)

and the abundance of cloud is computed as the sum of the abundances of the cloud-clusters: Cloud Fraction = ∑ aik ∀i classified as cloud



Cloud ProductsINTRODUCTION METHODS MATERIAL RESULTS CONCLUSIONS

Cloud Probability

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Unmixing Cloud Abundances

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Cloud Abundance

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Product

An improved cloud probability mask is obtained by multiplying the Cloud Probability (membership

obtained from the extracted features) and the Cloud Abundance (mixing obtained from the spectra) That is, combining two complementary sources of information processed by independent methods


CHRIS/PROBA• The Compact High Resolution Imaging Spectrometer (CHRIS) is

mounted on board the European Space Agency (ESA) small satellite platform called PROBA (Project for On Board Autonomy).

• CHRIS provides multi-angular hyperspectral images in the 400 - 1050 nm spectral range with a maximum spatial resolution of 17 or 34 m at nadir depending on the acquisition mode.



CHRIS Images• Six Mode1 images acquired over 4 core sites of the PROBA

mission are considered:

- Barrax (BR, Spain), Oahu (OH, Hawaii), Lake Argyle (LG, Australia), and Reynold’s Creek (RC, United States).

• Images have been selected in order to take into account their different characteristics:

- geographic location (latitude/longitude);

- date and season;

- type of cloud (cumulus, cirrus, stratocumulus);

- surface type (soil, vegetation, sand, ice, snow, rivers, sea, etc).



CHRIS ImagesINTRODUCTION METHODS MATERIAL RESULTS CONCLUSIONS

BR-2

003-

07-1

4

BR-2

004-

05-2

7

BR-2

005-

07-1

7

OH

-200

5-01

-08

LG-2

004-

02-2

9

RC

-200

4-04

-23


Results

• All the method has been implemented in Matlab programming language and it is capable to handle directly the CHRIS HDF original files and process them automatically saving images and results in ENVI standard format.

• As no ground truth is available, we could merely analyze the performance of the proposed method by visual inspection.

• The selected test images present the most usual cloud detection problems but also some critical problems of cloud detection: cirrus clouds, sand, sea waves, ice and snow.



Results• The three images over the Barrax (BR, Spain) site are a good

example of an easy cloud detection problem, when clear clouds are well contrasted with soil and vegetation.

• Standard thresholding methods would provide acceptable results



Results• However, thresholding methods are less accurate (the correlation

among features is not considered).

• Moreover they only provide a hard classification (flag) indicating “cloud” or “non-cloud”.



ResultsINTRODUCTION METHODS MATERIAL RESULTS CONCLUSIONS

Zoom of the probabilistic cloud mask


Results• The role of the region of interest (ROI) selection can be seen in

BARRAX images, being more important in BR-2003-07-14 where small clouds can be mixed in a cluster with other classes if the whole image is considered (clouds must be representative in the analyzed area when using statistical methods).

•




• The use of thresholds to select the ROI can exclude thin or small clouds from the ROI. A solution is to relax thresholds at the risk of considering the whole image.

• Even with no ROI selection results are good if clouds cover a sufficient area or the number of clusters is high enough.



• At OH-2005-01-08 (Oahu, Hawaii), waves and sand pixels belong to a cluster labeled as cloud due to their high reflectance and whiteness, but they present low probabilities and abundances.

• In addition, cirrus over land and sea are well detected since a specific cluster describe them and have been correctly classified.



• One of the critical issues in cloud detection is the presence of ice/snow in the surface (similar reflectance behavior than clouds).

• However, the atmospheric absorption is lower at clouds due to the their height, thus different clusters are found for these two classes in the image.

• Therefore, ice/snow present low Cloud Probability (clustering) although Cloud Abundance (unmixing) can be relatively high due to the spectral similarities.


Conclusions• We have presented a new technique that faces the problem of

accurately identifying the location and abundance of clouds in VNIR multispectral images.

• The main contributions of the work consist on:

- Better discrimination of clouds by extracting features that consider both the reflectance and atmospheric characteristics of clouds.

- To combine two complementary sources of information (extracted features & spectral signature) which are processed by independent methods (unsupervised clustering & spectral unmixing).

• Proposed method outperforms standard methods in terms of:

- Overall cloud detection accuracy

- Correct classification of difficult pixels (cirrus, sand, ice, snow)

- Generation of a cloud probability mask instead of a binary flag



Further Work• Final maps can be further analyzed using textural and contextual

information.

• Co-registration and geo-coding of images are critical in order to introduce a series of improvements:

- To use a DEM to correct the image and estimate heights

- To combine multi-angular information of the five images

- To take into account sun position to relate clouds and shadow

• The general nature of the procedure allows its application to other spectral sensors in the range VNIR. At present, we are refining the proposed scheme and testing its performance on ENVISAT/MERIS data.


ACKNOWLEDGMENTS: authors wish to thank ESA and SIRA Technology Ltd. for the availability of the image database and the assistance provided.

4th ESA CHRIS Proba Workshop, ESA-ESRIN, September 19-21, 2006

Thanks for your attention!


Documents

Cloud Probability Mask for PROBA/CHRIS Hyperspectral Imagesearth.esa.int/workshops/4th_chris_proba/CHRIS_WS4_GUANER_L.pdf · 4th ESA CHRIS Proba Workshop, ESA-ESRIN, September 19-21,