Haze Detection and Removal in Sentinel 3 OLCI Level 1B Imagery …€¦ · • Haze can affect human’s visibility and health. • Haze degrades optical remote sensing images by

Haze Detection and Removal in Sentinel 3 OLCI Level 1B Imagery

Using a New Multispectral Data Dehazing Method

Xinxin Busch Li, Stephan Recher, Peter Scheidgen

July 27th , 2018

• Introduction » Why do we need to remove HAZE from Sentinel 3 OLCI

data?

• Method » How can we remove HAZE from Sentinel 3 OLCI Level 1B

data?

• Results » How is hazy Sentinel 3 OLCI image restored step by step?

• Evaluation » How can we evaluate the results of dehazing?

• Conclusion » What have we gained from dehazing processing?

Outline

2 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B

• HAZINESS is originated from fractions of water vapor, ice, fog, sand, dust, smoke, or other small particles in the atmosphere.

• Haze can affect human’s visibility and health.

• Haze degrades optical remote sensing images by its scattering.

Introduction

Singapore, affected by severe smoke haze

due to forest fires in the region periodically ( Reuters, 2013)

western Africa, dust haze eastern USA, morning haze

Italy, air pollution haze China, burning and biomass haze

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telli

te im

age

s fro

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AS

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• Haziness on optical image: Sentinel 3 OLCI Level 1B

Introduction

Time: 08/12/2016, UTC 7:19

Time: 04/12/2016, UTC 7:23 S3 OLCI RGB image (R:Oa10; G:Oa6; B:Oa3)

Haze free area: land/ocean surface

Haze area: semi transparent white overlay +

land/ocean surface

possible to restore land/ocean surface

Cloud area : non-transparent white overlay

not possible to restore land/ocean

surface


5 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B

• Haziness on optical image: Sentinel 3 OLCI Level 1B

Introduction

Time: 08/12/2016, UTC 7:19

Time: 04/12/2016, UTC 7:23

possible view after restoration

geolocated

27-July-2018

𝑅𝑇𝑂𝐴 = 𝑅𝑇𝑂𝐴0+ 𝑅ℎ𝑎𝑧𝑒

Hazy image Haze detection

𝑅ℎ𝑎𝑧𝑒 = 𝑓(𝐻𝑇𝑀𝑖𝑚𝑎𝑔𝑒 , 𝐻𝑇𝑀𝑏𝑎𝑛𝑑𝑖)

𝑅𝑇𝑂𝐴, Top of Atmosphere (TOA) radiance of hazy image;

𝑅𝑇𝑂𝐴0, TOA radiance of restored hazy image;

𝑅ℎ𝑎𝑧𝑒, radiation contribution from haze;

HTMimage, Haze Thickness Map of the hazy image, and HTMbandi, HTM per band;

𝑅𝑇𝑂𝐴0 averaged TOA radiation of haze-free pixels in restored hazy image;

𝑅𝑇𝑂𝐴 averaged TOA radiation of haze-free pixels in hazy image

𝑅′𝑇𝑂𝐴 dehazed image with aerosol compensation

Haze removal

𝑅𝑇𝑂𝐴0= 𝑅𝑇𝑂𝐴 − 𝑅ℎ𝑎𝑧𝑒

Aerosol compensation

𝑅′𝑇𝑂𝐴 = 𝑅𝑇𝑂𝐴 + 𝑅𝑇𝑂𝐴0

− 𝑅𝑇𝑂𝐴

Method

• A new multispectral data dehazing method (A. Makarau et

al., 2014)


𝑅𝑇𝑂𝐴 = 𝑅𝑇𝑂𝐴0+ 𝑅ℎ𝑎𝑧𝑒

Hazy image Haze detection

𝑅ℎ𝑎𝑧𝑒 = 𝑓(𝐻𝑇𝑀𝑖𝑚𝑎𝑔𝑒 , 𝐻𝑇𝑀𝑏𝑎𝑛𝑑𝑖)

𝑅𝑇𝑂𝐴, TOA radiance of hazy image;

𝑅𝑇𝑂𝐴0, TOA radiance of restored hazy image;

𝑅ℎ𝑎𝑧𝑒, radiation contribution from haze;

HTMimage, Haze Thickness Map of the hazy image, and HTMbandi, HTM per band;

𝑅𝑇𝑂𝐴0 averaged TOA radiation of haze-free pixels in restored hazy image;

𝑅𝑇𝑂𝐴 averaged TOA radiation of haze-free pixels in hazy image

𝑅′𝑇𝑂𝐴 dehazed image with aerosol compensation

Haze removal

𝑅𝑇𝑂𝐴0= 𝑅𝑇𝑂𝐴 − 𝑅ℎ𝑎𝑧𝑒

Aerosol compensation

𝑅′𝑇𝑂𝐴 = 𝑅𝑇𝑂𝐴 + 𝑅𝑇𝑂𝐴0

− 𝑅𝑇𝑂𝐴

Method

• A new multispectral data dehazing method (A. Makarau et

al., 2014)


• Haze detection » Haze Optimized Transform (HOT) (Y. Zhang et al., 2003)

› limited by water bodies and man-made features

› Haze detection: one blue band and one red band (Nicholas Pringle et al., 2015):

HOTtest=Band490nm−0.5∗ Band560nm−0.08>0

» Haze Thickness Map (HTM) (A. Makarau et al., 2014)

› precise detection for an inhomogeneous and structured haziness

› Haze detection: two blue bands

Bandextrapol = (Band443nm + (Band443nm − 0.95 ∗ Band490nm)) > 0

• Haze removal » Dark-object Subtraction (DOS) (G. Dal Moro et al., 2007)

› subtraction of the haze thickness based on several pixels

» Improved DOS(A. Makarau et al., 2014)

› subtraction of the haze thickness based on all pixels

Method


Method

• Haze detection derive 𝑅ℎ𝑎𝑧𝑒

1.Detect Haziness Bandextrapol

Bandextrapol = (Band443nm + (Band443nm − 0.95 ∗ Band490nm)) > 0


Method



2.Generate HTM for the whole image HTMimage

HTMimage = MINRadiance(Bandextrapol , window3x3)


Method




3.Optimize HTMimage HTMcorrect

HTMcorrect=triangular_interpolation(HTMimage,*bright_objects)

* can be derived from Sentinel3 OLCI L1B flag dataset ‘bright pixels’


Method





4.Separate haze pixels and non haze pixels HTMmask

HTMmask = MINRadiance(Bandextrapol , window21x21)


Method






5. Generate HTM for each band HTMbandi

HTMBandi= MINRadiance(Bandi , window3x3)


Method







6. Calculate regression coefficient k

k = regression_fit(HTMimage, HTMBandi) ! k is decreased with band wavelength


Method








7. Calculate haze radiation contribution 𝑅ℎ𝑎𝑧𝑒

𝑅ℎ𝑎𝑧𝑒 = 𝑓(𝐻𝑇𝑀𝑖𝑚𝑎𝑔𝑒 , 𝐻𝑇𝑀𝑏𝑎𝑛𝑑𝑖) = k ∗ HTMcorrect


Method








7. Calculate haze radiation contribution 𝑅ℎ𝑎𝑧𝑒

1. Bandextrapol = (Band443nm + (Band443nm − 0.95 ∗ Band490nm)) > 0

2. HTMimage = MINRadiance(Bandextrapol , window3x3)

3. HTMcorrect=triangular_interpolation(HTMimage,*bright_objects)

4. HTMmask = MINRadiance(Bandextrapol , window21x21)

5. HTMBandi= MINRadiance(Bandi , window3x3)

6. k = regression_fit(HTMimage, HTMBandi) ! k is decreased with band wavelength

7. 𝑅ℎ𝑎𝑧𝑒 = 𝑓(𝐻𝑇𝑀𝑖𝑚𝑎𝑔𝑒 , 𝐻𝑇𝑀𝑏𝑎𝑛𝑑𝑖) = k ∗ HTMcorrect

* can be derived from Sentinel3 OLCI L1B flag dataset: bright pixels


• Data source: live Sentinel 3 OLCI Level 1B data

from EUMETcast

• Data collection time period: Dec. 2016

• Hazy image is detected on 08.12.2016, UTC 7:19

Results


Results

• Haze detection: image HTM and band-dependent HTM

Detect haze: Bandextrapol Estimate haze thickness: HTMimage Segment HTM: HTMmask Correct HTM: HTMcorrect

Band1 Estimate haze thickness at Band 1:

HTMBand1 Band9 Estimate haze thickness at Band 9:

HTMBand9


• Haze removal including aerosol compensation

» Single band e.g. Oa2

Results

Band2 HTMcorrect Band2_dehazed Band2_compensated


• Haze removal including aerosol compensation

» RGB composite: R(Oa10), G(Oa6), B(Oa3)

Results

Hazy RGB image Dehazed RGB image

Legend

Cloud mask


• Compare hazy image with haze free image

» Minimal difference of the image acquisition conditions:

time and Sun/sensor geometry

Evaluation

Data acquisition time: 08/12/2016 UTC 07:19 04/12/2016 UTC 07:23


• Compare hazy image with haze free image » Averaged TOA radiance of hazy/haze-free region on hazy

image and haze-free reference image, covering the spectrum from 400 to 1020 nm

Evaluation

Haze-free region: all the non-haze

and cloud-free pixels of the hazy

image

Hazy region: all the hazy and cloud-

free pixels of the hazy image


• The new haze detection and removal method

succeeded in processing haziness of Sentinel 3

OLCI L1B image data.

• The algorithm can be easily integrated with other

data processors in remotely sensed data processing

chain.

• Based on this algorithm, a dehazing processor for

multispectral meteorological satellite data will be

further developed and can be applied on original

data digital numbers (DNs) counts or calibrated

radiance data.

Conclusions


Thank you for your attention

Documents

Haze Detection and Removal in Sentinel 3 OLCI Level 1B Imagery …€¦ · • Haze can affect human’s visibility and health. • Haze degrades optical remote sensing images by