Introduction on Some Efforts on Optical Image Optimization ... · 3body+3median+2interface How to optimization with weak object signal ... resolution), seabed types, α,β……Sowhynotwefocusonsuchdata

Expert meeting on Marine Biodiversity and Eutrophication in the Northwest Pacific Region5 6 August 2013 Toyama Japan5-6 August, 2013, Toyama, JapanAgenda Item 6 - Seagrass

Introduction on Some Efforts on Optical Image Optimization in Limited Water Area for

Seagrass Information Extraction

Yongchao ZhaoYongchao Zhao

Institute of Electronics (IECAS)Chinese Academy of Sciences, Chinay ,

[email protected]+86-13501162025

+86 10 58887208 8703

8/22/2013

+86-10-58887208-8703

Outline

Background: limited/shallow coastal water area, seagrass/water/benthalBackground: limited/shallow coastal water area, seagrass/water/benthal

Our efforts: Method/Model/Software

Denoise and Destripe (SRSSHF+VRadCor)Denoise and Destripe (SRSSHF+VRadCor)Water Correction (WRC)Denegative for reflectanceWave removing and extractingSeagrass mapping

Current or future works: M th d/S ft /E i tCurrent or future works: Method/Software/Equipment

Precise water spectrum measurement: methods and toolsInformation response and extraction: model/algorithm/softwareInformation response and extraction: model/algorithm/softwareSignal fractionization: water, surface, seagrass/seaweed, seabed

Background: About us

Background: About us

Some works for water-seagrass remote sensing

Water-seagrass information transfer understanding and experimental

g g

validation

Precise water spectrum measurement: methods and equipments

Water image optimization: algorithm and software

Special water-seagrass information extraction: seagrass mapping, water

depth……

Project: CAS: water polarization spectrum featuresProject: CAS: water polarization-spectrum features

NASA: seagrass information extraction in Tampa Bay

HS2IS

field experiments with long-time water spectrum measurement: >100,000

BackgroundNASA ROSES-2008 (NNX09AT51G):NASA’s Earth science for decision making:S s sc e ce o dec s o g:Gulf of Mexico Region: “ Mapping andCharacterization of Seagrass Habitats UsingSpacecraft Observations”

Clearwater Harbor/ St. Joseph Sound(CLW/STJS), Boca Ciega Bay (BCB).Pinellas, FL,

Objects: Seagrass information extraction-Environmental change detection-decisionmaking systemmaking system

HYPERION、ALI、TM、IKONOS…

Thalassia, Syringodium, Halodule; Algae

Key words: limited shallow water area,complex background, weak signal,hyperspectral, mixing, seagrass informationextraction

Background: 2 key problems meet in the project

Image-inforamtion extractionImage inforamtion extractionimage information extractioninformation extraction

Spectral acquirement

Data Inforamtion I /I M (RS)Image/Images------------- -----------------Maps (RS)Spectra---------------- ------------Properties (Field works)

Seagrass/water


Problems of imagelit bl--not only quality problems

Background: image problem


The comparison between Hyperion images in the same areaLeft: seems the surface are quiet, Right: seems a strong MTF effect


The surface effect of wave (Isky) in IKONOS


Problems of information extraction

Background: information extraction problem

Sun illuminationDirectional

IFOV

Atm. IlluminationHemisphere

AtmosphereGas body( ) IITrTII atm2atm2objatm1atm1sun =++

Surgeless or idealWater surfaceLiquid-gas surface

Real water surfacewith wave and foamLiquid-gas surface

Gas body( ) atm2atm2objatm1atm1sun

q g

WaterLiquid body

( )[ ]Natural sea bottomTerrain effectliquid-solid surface

Submerged vegetationF t lik ith t di

Canopy of submerged vegetationwater mediumSooth the submerged terrain

( )[ ] atm-interfaceinterface2water2water2seagrasswater1water1interface1obj rKrTrrTKr +++=

Forest like with water mediumLiquid-vegetation surfaceLiquid + vegetation bodyΩλt

Background: problem

Limited water area：shallow：shallow：

seagrass：Decsion making system：

Effect of wave：Effect of wave：

Image=(seagrass+seabed) +(water suface+wave)+benthal terrain+water

body(+sun)+atm.+noise

3body+3median+2interface3body 3median 2interface

How to optimization with weak object signal？VRadCor+SRSSHF+WaterDenegative---VRadCor+SRSSHF+WaterDenegative

How to extracting weak seagrass information under complicated background？---WaterRadCor(WRC)

Remote sensing image seagrass mapping

Our efforts

Our efforts

VRadCor: destripe but not changethe spectral

SRSSHF：denoise but keep theboundary

FLAASH- atmospheric correctionp

DeNegative:

WRC t tiWRC-water correction

Wave removing and extracting

Seagrass mapping

Our efforts: VRadCor-destripep


处理前HJ-1A hyperspectral


HJ-1A 1band

Our efforts: VRadCor-destripe

2050

2100

p

1900

1950

2000

2050

lue

1750

1800

1850

raw

VRadCor

Va

1700

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281

Sample

600

700

Raw 1R image

200

300

400

500

Raw 1R image

After VRadCor

ce(*100

00)

-200

-100

0

100

400 450 500 550 600 650 700 750 800 850 900

Refletanc

-300

00

Wavelength(nm)

Our efforts: SRSSHF-denoise


400

300 Raw

SRSSHF

800

900

Raw

200

SRSSHF

ce(*10000)

500

600

700Raw

SRSSHF 5

SRSSHF 20

00)

Image ID Raw VRadCor SRSSHF1 SRSSHF5 SRSSHF20 VRadCor SRSSHF1 SRSSHF5 SRSSHF20

EO1H0170402010140110PF 131.9 131.6 356.9 1620.2 3848.3 1.0 2.7 12.3 29.2

S/N Ratio of S/N

0

100

Reflectanc

300

400

500

ctance(*1000EO1H0170402010140110PF 131.9 131.6 356.9 1620.2 3848.3 1.0 2.7 12.3 29.2

EO1H0170412009296110KF 124.1 124.1 302.4 868.5 1143.6 1.0 2.4 7.0 9.2

EO1H0170412009294110KF 141.6 142.2 389.1 1451.7 3479.2 1.0 2.7 10.3 24.6

EO1H0170412010073110PF 152.5 152.6 453.3 2353.8 4657.5 1.0 3.0 15.4 30.6

-100

0

100 110 120 130 140 150 160 170 180 190 200

100

200

ReflecEO1H0170402009281110KF 142.1 142.2 301.9 1301.6 4631.8 1.0 2.1 9.2 32.6

Average 138.4 138.6 360.7 1519.2 3552.1 1.0 2.6 10.8 25.2

-200 Samples

0

400 450 500 550 600 650 700 750 800 850

Wavelength(nm)


channels

channel

Our efforts: DeNegativeg

Our efforts: Wate correction (WRC)( )

Image=(seagrass+seabed) +(water suface+wave)+benthal topographty+水

Study background:

water body(+sun)+atm.+noise

Achievements：Lee (1994,1999), Mumby, P. J.(2002), Dekker, A. J.(2005), Mishra, D. R.(2005), ……

Basic works and data：Depath, water absorption/scatter properties and spectral extensionp p p p p

Austin, P. (1986), Baker, S.(1982), ……

Status: no tools

Model incomplete, approximate expression, water surface, water-solid interface, solar-sky light effect.

Key technologies/problem：

Model incomplete, approximate expression, water surface, water solid interface, solar sky light effect.

Αβestimation and spectral extension

Consider too more on the components (HydroLight)

N f l lNo useful tools

Validation problems: the methods of Mumby, Lee—only for multispectral

Our efforts: Wate correction (WRC)

modelsSun illuminationDirectional

IFOV

( )



WaterLiquid body


AtmosphereGas body

Natural sea bottomTerrain effectliquid-solid surface

Submerged vegetationForest like with water mediumLiquid-vegetation surfaceLiquid + vegetation body


Our models



IFOV

( )

Surgeless or idealReal water surfacewith wave and foam


AtmosphereGas body

Water surfaceLiquid-gas surface

with wave and foamLiquid-gas surface



WaterLiquid body



Our model and softwaretool

( )

Our model and softwaretoolFeatures

Emphasize to remove the effect of water surface( wave—skylight)Angle effect of mirror reflect—hot pointFor Absorption feature, the whole body is considered but not



IFOVthe component in detailCombined with ACResidual effect (difference) of solar and sky light after GC



WaterLiquid body

AtmosphereGas body

( ) y gDepth estimation with background effectAvailable for iterative to optimize the input parametersSpectral extension with nonlinear method




Liquid bodySpectral extension with nonlinear methodConsider imaging geometry


WRC software

( )


Water surface and sky light correction

( )


Parameter estimation Field validation

( )

0.5-3m，depth absorption coefficient scatter coefficient


Parameter estimation

( )

α coefficient -spectrum β coefficient-spectrum


Back scatter correction (path radiance of water)

( )

Back scatter estimation

After correction


Water depth correction

( )

before after


Water depth correction

( )

校正前后光谱对比


Water correction

( )

before after


Water correction

( )

Our efforts: Wate correction (WRC)( )


Validation：

( )

Our efforts: Wave removing

Our efforts: Wave removing

Our efforts: seagrass information extraction

Classifications


mixing of seagrass andand epiphyte

Slope intercept C.C Composed ΔR

B329,218,107


RR

ΔR

CSAM

Slope

Problems of spectral measurement

Current or future works: Precise water spectrum measurementmeasurement

Problems of spectral measurementp

Traditional:

Ours:

Refo=Rado (Refr/Radr) Refo=Rado (Kp*Refr/Radr)


Problems of spectral measurementProblems of spectral measurement


On boat




Suggestion

For seagrass information extraction from remote sensing images,g g gprecise preprocessing including optimization is necessary. Ouralgorithm or tools may be helpful for application.Maybe we can pay some attention on the method/tool/criterion review,at least in remote sensing seagrass information extraction, for thefuture working procedure discussion but not only focus on the datafuture working procedure discussion, but not only focus on the datacollection. data useful? ---tools or technology useful?For seagrass information extraction via remote sensing images, somekey/basic parameters are very important, such as depth (with certainresolution), seabed types, α,β…… So why not we focus on such data

ll icollection?The change!

Thank you!

Documents

Introduction on Some Efforts on Optical Image Optimization ... · 3body+3median+2interface How to optimization with weak object signal ... resolution), seabed types, α,β……Sowhynotwefocusonsuchdata