slide1 n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
4. IMAGE CLUSTERING PowerPoint Presentation
Download Presentation
4. IMAGE CLUSTERING

Loading in 2 Seconds...

play fullscreen
1 / 1

4. IMAGE CLUSTERING - PowerPoint PPT Presentation


  • 50 Views
  • Uploaded on

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about '4. IMAGE CLUSTERING' - elata


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

Monitoring oil spills at sea with optical satellite sensors: the PRIMI project Optical Observation ModulePisano, Andrea1 ; Bignami, Francesco1; Colella, Simone1; Evans, Robert, H.2; Santoleri, Rosalia1 1CNR-ISAC, Via Fosso del Cavaliere 100, 00133 Rome, Italy. e-mail: andrea.bignami@artov.isac.cnr.it;

2The Rosenstiel School of Marine and Atmospheric Science, 4600 Rickenbacker Causeway, Miami, FL 33149-1098, USA. e-mail: revans@rsmas.miami.edu

ABSTRACT The PRIMI project funded by the Italian Space Agency (ASI) has implemented an observation and forecast system to monitor marine pollution from hydrocarbon oil spills (OS) in the Italian Seas. The system consists of four components, two of which for OS detection via multi-platform SAR and optical satellite imagery, an OS displacement forecast subsystem based on numerical circulation models and a central archive that provides WEB-GIS services to users. The system also provides meteorological, oceanographic and ship detection information. The Optical Observation Module, based on MODIS and MERIS imagery, is described here. The idea of combining wide swath optical observations with SAR monitoring arises from the necessity to overcome the SAR reduced coverage of the monitoring area. This can be done now, given the MODIS and MERIS higher spatial resolution with respect to older sensors (250-300 m vs. 1 km), which consents the identification of smaller spills deriving from illicit discharge at sea. The procedure to obtain identifiable spills in optical reflectance images involves removal of natural variability to enhance slick - clean water contrast, image segmentation/clustering and a set of criteria for the elimination of those features which look like spills (look-alikes). The final result is a classification of oil spill candidate regions by means of a score based on the above criteria.

OIL SPILLS ARE DETECTABLE IN OPTICAL IMAGERY

MERIS Imagery:

L1B full resolution (300 m) TOA radiance bands:

λ = (443, 560, 665, 681, 865 nm)

MODIS Imagery:

L1B high and medium resolution (250 and 500 m) TOA radiance bands:

λ = (469, 555, 645, 859, 1240, 1640, 2130 nm)

First criterion for OS detectability:

High Glint Condition (specular reflection): OS is brighter than its background

Low Glint Condition (no specluar reflection):

OS is darker than its background

t(469)

t(645)

t(555)

High Glint Condition

Low Glint Condition

t(1240)

t(2130)

t(1640)

t(859)

t(443)

t(865)

t(681)

t(560)

t(665)

MODIS TERRA true color image

August 8, 2008

OS north of Algerian coast

MERIS true color image, August 2, 2006, OS on Lebanon coast

Striping effect

OIL SPILL DETECTION ALGORITHM

Input images:

MODIS & MERIS L2 remapped TOA reflectance t()

MODIS

  • DESTRIPING (MODIS only):
  • Eliminationof “striping” effect via
  • detector ramprectification & mirror side
  • Equalization
  • 2. CLOUD MASKING
  • (using SST qualityflags + cloudedgeexpansion)
  • 3. IMAGE FLATTENING
  • (eliminationofnaturalvariability)
  • Subtract Rayleigh reflectance r from t, all bands;
  • Subtract water-leaving reflectance (w) from t (MODIS:469, 555 nm only;MERIS: 443, 560 nmonly);
  • Subtract aerosol contribution using red band (645 nm).

5. OS CANDIDATE SELECTION

Discard or retain cluster regions according to:

Region Area (A): retain only regions within threshold values;

Region Shape (S): computed combining A and perimeter P; (McGarigal and Marks 1995); retain only elongated regions;

Region-water contrast (C=Region reflectance/water bbox reflectance): retain regions with C<1 (C>1) for low (high) glint cases;

Cloud vicinity: elimination of regions made more brilliant by nearby cloud straylight, particularly important for high glint cases where brighter slicks may be confused with straylight affected regions

Region and water reflectance histogram peak distance (dbe): retain regions with histogram peak reflectance sufficiently lower (higher) than surrounding water histogram peak reflectance for low (high) glint cases.

Segmented images

MERIS

Flattened images ()

MODIS

4. IMAGE CLUSTERING

Classification/grouping of ocean pixels, using mean shift segmentation (Comaniciu and Meer, 2002): image pixels are sorted out into clusters, each of which is defined by a reflectance mode value.

MODIS

MERIS

MERIS

4 clusters

  • Each color  reflectance mode valuedefining a cluster
  • Each cluster composedbymanyregions
  • Reducedsignalvariabilityfrom input images
  • Stripingpartiallyremoved

3 clusters

SCORE LOOK-UP TABLE (LUT) COMPUTATION

the following parameters (P) are used to define scores (S) for OS candidates:

dbe: see OS candidate selection;

d4r: OS candidate reflectance histogram integral, from the minimum reflectance to a fourth of the reflectance

total range (region + surrounding water) histograms;

l4r: same as d4r, from 3/4 to the maximum reflectance of the above range;

d4w and l4w: The same for the surrounding water reflectance histogram;

(d4r – d4w)/max(d4r, d4w): This ratio is equal to 1 when the darkest quarter of the reflectance range is only

populated by the OS candidate region histogram; no surrounding water in this range implies d4w = 0.

Contrarily, the ratio is equal to -1 when only populated by surrounding water (thus, d4r = 0).

In practice this parameter tells us how much more the darker range of the histogram is populated by the OS

candidate than by water which means that, in the case of no glint conditions, then candidate is a probable oil spill;

(l4r – l4w)/max(l4r, l4w):The same for the brighter quarter of the histogram reflectance range;

values close to 1 in a high glint case probably indicate an OS;

dref: Average reflectance distance between the OS candidate and water histogram integral curves;

this parameter indicates how much an OS candidate region is “globally” darker or brighter than surrounding water.

An “in situ certified”OS and look-alike database (64) was used as to compute score look-up tables (LUT),

for each of the above parameters.

Score table computation:

Each of the above parameters was estimated for each OS or lookalike of our database.

Histograms (H) of each parameter distribution were computed, both for known OS’s (HOS(P)) and look-alikes (HLA(P);)

A score LUT for each parameter P was computed as S(P) = HOS(P)/(HOS(P)+ HLA(P)) .

Candidate oil spills

Output images

  • 6. SCORE ASSIGNMENT TO SELECTED OS CANDIDATES
  • For each selected OS candidate in a new image:
  • values P are computed for all score parameters;
  • Score values are found in each P LUT;
  • Final score is issued as a linear combination of single-P scores.

MODIS

MERIS

score

score

The finalresultis a classificationof oil spill candidate regionsbymeansof a score (from 0 to 1) based on the abovecriteria

Many regions are eliminated

but OS are not yet inequivocably distinguishable from look-alikes

MODIS

MERIS

REFERENCES

Comaniciu and D., P. Meer, 2002. “Mean shift: a robust analysis of feature spaces: color image segmentation”.

IEEE Trans. On pattern analysis and machine intelligence, 24, 5, 1-18.

Gumley, L., R. Frey and C. Moeller, 2005. “Destriping of MODIS L1B 1KM Data for Collection 5 Atmosphere

Algorithms”. http://modis.gsfc.nasa.gov/sci_team/meetings/200503/posters/atmos/gumley1.pdf.

Weinreb, M. P., R. Xie, J. H. Lienesch and D. S. Crosby, 1989. “Destriping GOES Images by Matching Empirical

Distribution Functions”. Remote Sens. Environ., 29, 185-195.

McGarigal and Marks, 1995. “Spatial pattern analysis program for quantifying landscape structure”.

http://www.umass.edu/landeco/pubs/mcgarigal.marks.1995.pdf

  • CONCLUSIONS AND FUTURE WORK
  • Imagedestriping, flattening (eliminationofoceanic and atmosphericnaturalvariabilityfrom the input
  • image), clustering and OS classification (bymeansof a set ofcriteria) are the basisof the OS detection
  • algorithmproposedhere.
  • It can beseenthat the end productstillpresentsambiguities in OS pinpointing, whichcallsfor future
  • work, towards a fullyautomatized OS detection Algorithm
  • Imagedestriping: notyetfullyperformant, requiresfutherdevelopment;
  • Score computation: ongoingrefinementof the score parameterswillhopefullyleadto a threshold score
  • whichautomaticallyeliminateslook-alikes.

Example of evaluation parameter distribution in OS’s (green) and look- alikes (black) of the OS-lookalike database.

(d4w, d4r)

(dbe)

(l4w, l4r)

dref

Histogram integrals and dref.

OS candidate (green) and surrounding water histogram (black), along with dark (d4w, d4r) and light (d4w, d4r) quarters of the histogram and dbe.

Example of score LUT for an evaluation parameter, as function of the parameter value