haze detection and removal in sentinel 3 olci level 1b imagery …€¦ · • haze can affect...
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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
Sa
telli
te im
age
s fro
m N
AS
A
3 27-July-2018 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 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
4 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
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)
6 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
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)
7 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
• 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
8 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
Method
• Haze detection derive 𝑅ℎ𝑎𝑧𝑒
1.Detect Haziness Bandextrapol
Bandextrapol = (Band443nm + (Band443nm − 0.95 ∗ Band490nm)) > 0
9 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
Method
• Haze detection derive 𝑅ℎ𝑎𝑧𝑒
1.Detect Haziness Bandextrapol
2.Generate HTM for the whole image HTMimage
HTMimage = MINRadiance(Bandextrapol , window3x3)
10 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
Method
• Haze detection derive 𝑅ℎ𝑎𝑧𝑒
1.Detect Haziness Bandextrapol
2.Generate HTM for the whole image HTMimage
3.Optimize HTMimage HTMcorrect
HTMcorrect=triangular_interpolation(HTMimage,*bright_objects)
* can be derived from Sentinel3 OLCI L1B flag dataset ‘bright pixels’
11 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
Method
• Haze detection derive 𝑅ℎ𝑎𝑧𝑒
1.Detect Haziness Bandextrapol
2.Generate HTM for the whole image HTMimage
3.Optimize HTMimage HTMcorrect
4.Separate haze pixels and non haze pixels HTMmask
HTMmask = MINRadiance(Bandextrapol , window21x21)
12 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
Method
• Haze detection derive 𝑅ℎ𝑎𝑧𝑒
1.Detect Haziness Bandextrapol
2.Generate HTM for the whole image HTMimage
3.Optimize HTMimage HTMcorrect
4.Separate haze pixels and non haze pixels HTMmask
5. Generate HTM for each band HTMbandi
HTMBandi= MINRadiance(Bandi , window3x3)
13 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
Method
• Haze detection derive 𝑅ℎ𝑎𝑧𝑒
1.Detect Haziness Bandextrapol
2.Generate HTM for the whole image HTMimage
3.Optimize HTMimage HTMcorrect
4.Separate haze pixels and non haze pixels HTMmask
5. Generate HTM for each band HTMbandi
6. Calculate regression coefficient k
k = regression_fit(HTMimage, HTMBandi) ! k is decreased with band wavelength
14 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
Method
• Haze detection derive 𝑅ℎ𝑎𝑧𝑒
1.Detect Haziness Bandextrapol
2.Generate HTM for the whole image HTMimage
3.Optimize HTMimage HTMcorrect
4.Separate haze pixels and non haze pixels HTMmask
5. Generate HTM for each band HTMbandi
6. Calculate regression coefficient k
7. Calculate haze radiation contribution 𝑅ℎ𝑎𝑧𝑒
𝑅ℎ𝑎𝑧𝑒 = 𝑓(𝐻𝑇𝑀𝑖𝑚𝑎𝑔𝑒 , 𝐻𝑇𝑀𝑏𝑎𝑛𝑑𝑖) = k ∗ HTMcorrect
15 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
Method
• Haze detection derive 𝑅ℎ𝑎𝑧𝑒
1.Detect Haziness Bandextrapol
2.Generate HTM for the whole image HTMimage
3.Optimize HTMimage HTMcorrect
4.Separate haze pixels and non haze pixels HTMmask
5. Generate HTM for each band HTMbandi
6. Calculate regression coefficient k
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
16 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
• 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
17 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
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
18 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
• Haze removal including aerosol compensation
» Single band e.g. Oa2
Results
Band2 HTMcorrect Band2_dehazed Band2_compensated
19 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
• Haze removal including aerosol compensation
» RGB composite: R(Oa10), G(Oa6), B(Oa3)
Results
Hazy RGB image Dehazed RGB image
Legend
Cloud mask
20 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
• 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
21 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
• 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
22 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
• 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
23 27-July-2018 SCISYS - Haze Detection and Removal in Sentinel 3 OLCI Level 1B
Thank you for your attention