A Knowledge-based Approach for Reducing Cloud and Shadow

A Knowledge-based Approach for Reducing Cloud and Shadow

Mingjun Song and Daniel L. Civco

Laboratory for Earth Resources Information Systems (LERIS)Department of Natural Resources Management & Engineering

The University of ConnecticutU-4087, 1376 Storrs Road

Storrs, CT 06269-4087

ProblemProblemProblemProblem

Completely cloud-free remotely sensed images are not always available, especially in tropical, neo-tropical, or humid climates, posing complications and perhaps serious constraints to image analysis

ObjectiveObjectiveObjectiveObjective

Develop a knowledge-based method to produce a cloud and cloud shadow-free multitemporal image composite of neo-contemporary images

MethodologyMethodologyMethodologyMethodology

Topographical normalization

Multi-date Brightness correction

Main image versus secondary image

To detect those areas which are covered with cloud and shadow in the main image, and not having cloud or shadow in the secondary image

Replace

First Study AreaFirst Study Area

EasternMadagascarEasternMadagascar

December 15, 2000December 15, 2000April 22, 2001April 22, 2001

Topographical NormalizationTopographical Normalization

)/1( meanorigorignorm DNDNDN

Normalization ResultNormalization Result

Multi-date Brightness CorrectionMulti-date Brightness Correction

maindmainddcorr meanSDSDmeanDNDN secsecsec /)(

CriteriaCriteria

Band 1: Cloud

Band 4: Shadow

Band DifferenceRationale: If the difference is less than a

threshold, it should be the same object in two dates images

Criteria: Shape InformationCriteria: Shape Information

Shadow and stream. eCognition, length/width

Length/width: )2,1( lwlwMinlw

A

bfalw

222 )1(1

Bounding Box:

),(

),(2

21

21

eigveigvMin

eigveigvMaxlw

)(

)(

)(

)(

YXCov

YVar

XVar

XYCov

Covariance Matrix:

Knowledge BaseKnowledge Base

Parameter Cloud Shadow

Output Value 2 1

Band 1 (Main Image) > 41 >= 0

Band 4 (Main Image) >= 0 < 35

Band 1 (Secondary Image) < 33 < 33

Band 4 (Secondary Image) >= 0 > 27

Band 1 Difference > 10 >= 0

Band 4 Difference >= 0 > 10

Length-to-Width Ratio >= 0 < 9

Imagine Spatial ModelImagine Spatial Model

Cloud and Shadow DetectionCloud and Shadow Detection

Expert01_band1

00_band1

01_band401_band4

00_band400_band4

Band1_dif Band4_dif

Obj_lw

CompositeComposite

Replace

Second Study AreaSecond Study Area

ETM23 April 2001

ETM23 April 2001

ETM 26 March 2000

ETM 26 March 2000

Central-EasternConnecticut

Central-EasternConnecticut

ThamesRiver

ThamesRiver

UConnUConn

Multi-date Brightness CorrectionMulti-date Brightness Correction

Sample_01 Sample_00 Sample_00_corr 00_corr

CriteriaCriteriaCriteriaCriteria

Band 1: Cloud

Band 4: Shadow

Band Difference

Contextual InformationContextual Information

Difficulty:

Cloud edge with urban area

Shadow with water area

Rationale:

Cloud edge and shadow area should be accompanied by clear cloud area

Buffer the clear cloud area

Knowledge BaseKnowledge Base

Parameter Cloud Shadow

Output Value 2 1

Band 1 (Main Image) > 95 >= 0

Band 4 (Main Image) >=0 < 46

Band 1 (Secondary Image) < 221 < 221

Band 4 (Secondary Image) >= 0 >=0

Band 1 Difference > 10 >= 0

Band 4 Difference >= 0 > 10

Contexture in Clear Cloud ==1 ==1

Cloud and Shadow DetectionCloud and Shadow Detection01_band1

00_band1

Band1_difContext

Band4_dif

00_band4

01_band4Expert

CompositeComposite

2001 ETM2001 ETM 2000 ETM2000 ETM

Cloud &Shadow

Area

Cloud &Shadow

Area

ConclusionConclusion

• Procedure of mosaic

• Knowledge-based

• Spectral, shape, contextural

• Easy and efficient

• Flexible

• Additional image needed for overlap areas

AcknowledgementAcknowledgementAcknowledgementAcknowledgement

National Aeronautics and Space Administration Grant NAG13-99001/NRA-98-OES-08 RESAC-NAUTILUS, Better Land Use Planning for the Urbanizing Northeast: Creating a Network of Value-Added Geospatial Information, Tools, and Education for Land Use Decision Makers.

Northeast Applications of Useable Technology In Land planning for Urban Sprawl

This presentationis available at

resac.uconn.edu

A Knowledge-based Approach for Reducing Cloud

and Shadow

A Knowledge-based Approach for Reducing Cloud

and ShadowMingjun Song and Daniel L. Civco

Laboratory for Earth Resources Information Systems (LERIS)

Department of Natural Resources Management & Engineering

The University of ConnecticutU-4087, 1376 Storrs Road

Storrs, CT 06269-4087