presentazione_IGARSS2011.ppt

Automatic features extraction in Automatic features extraction in sub-urban landscape using very high sub-urban landscape using very high

resolution Cosmo-SkyMed SAR imagesresolution Cosmo-SkyMed SAR images

Fabio Del Frate, Fabio Del Frate, Chiara Pratola, Giovanni Chiara Pratola, Giovanni Schiavon, Domenico SoliminiSchiavon, Domenico Solimini

IGARSS 2011 – International Geoscience And Remote Sensing Symposium IGARSS 2011 – International Geoscience And Remote Sensing Symposium

Tor Vergata University, Rome - ItalyTor Vergata University, Rome - ItalyEarth Observation LaboratoryEarth Observation Laboratory

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COstellation of Small Satellites COstellation of Small Satellites for the Mediterranean basin for the Mediterranean basin

Observation Observation

• Italian Space Agency (ASI) mission

• Constellation of four satellites COSMO (1-4) equipped with X-band SAR sensors with global coverage of the planet

COSMO-SKYMED MISSIONCOSMO-SKYMED MISSION

• Spotlight: 1 m Spotlight: 1 m spatial resolution

IGARSS 2011 – 24-29 July 2011 Vancouver, Canada IGARSS 2011 – 24-29 July 2011 Vancouver, Canada

• Observations of an area of interest can be repeated several times a day in all-weather conditions (revisit time of about 140 min)

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TEST SITETEST SITE

Tor Vergata area in Rome, ItalyTor Vergata area in Rome, Italy

World View 2 image, 10World View 2 image, 10th th February 2010February 2010


Cosmo-SkyMed image, 9Cosmo-SkyMed image, 9th th July 2010July 2010

Central area of the Central area of the University Campus University Campus

Residential areaResidential area

Business buildingBusiness buildingShopping mallShopping mall

BuildingsBuildings

4IGARSS 2011 – 24-29 July 2011 Vancouver, Canada IGARSS 2011 – 24-29 July 2011 Vancouver, Canada

StreetsStreets

MotorwayMotorwayMedium-size streetMedium-size streetNarrow streetNarrow street

TEST SITETEST SITE


World View 2 image, 10World View 2 image, 10th th February 2010February 2010 Cosmo-SkyMed image, 9Cosmo-SkyMed image, 9th th July 2010July 2010


Natural areasNatural areas

GrasslandGrasslandCultivated FieldCultivated FieldTreesTreesBare soilBare soil

TEST SITETEST SITE


World View 2 image, 10World View 2 image, 10th th February 2010February 2010 Cosmo-SkyMed image, 9Cosmo-SkyMed image, 9th th July 2010July 2010

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CLASSIFICATION ALGORITHMCLASSIFICATION ALGORITHM

Pixel-based NN classifierPixel-based NN classifier


NNs are recognized as rather competitive algorithms NNs are recognized as rather competitive algorithms but one has to be very careful to avoid overfitting in but one has to be very careful to avoid overfitting in the training phasethe training phase

HIDDEN LAYERS

CLASSIFICATION ALGORITHMCLASSIFICATION ALGORITHM

WHAT IN INPUT ? WHAT IN OUTPUT ?

•Backscattering Intensity

• Local textural parameters

•GLCM texture information

•Backscattering Intensity

• Local textural parameters

•GLCM texture information

Asphalt

Short vegetation + bare soil

Tall vegetation + trees

Manmade

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GLCMGLCM

Gray Level Co-Occurrence Matrix (Haralick et al., 1973)Gray Level Co-Occurrence Matrix (Haralick et al., 1973)


0 0 1 1

0 0 1 1

0 2 2 2

2 2 3 3

The matrix is computed with reference to a predefined box in the image, to a predefined number of gray levels, pixel distance (d) and direction (q)

4 gray levels (0, 1, 2, 3)d=1 and q=0°

2100

1601

0042

0124

)0,1(P

The element Pij of the matrix says how many times the element with gray level i is distant d pixels, in q direction, from an element with gray level j

0 1 2 30

1

2

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GLCM TEXTURE INFORMATIONGLCM TEXTURE INFORMATION

GLCM parametersGLCM parameters

Shift

Direction

Quantization levels

Investigation on the most suitable parametersInvestigation on the most suitable parameters

FIXED

Window size

d = 15

q = 45°

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GLCM measuresGLCM measures

SEARCH FOR OPTIMUM VALUE/MEASURE

Transformed Divergence (TD)Transformed Divergence (TD)(Bartolucci et al, 1983)(Bartolucci et al, 1983)

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12),(jiD

ejiTD

• Di,j : divergence between classes i and j

Max[TD(i, j)] = 2

If TD(i, j) ≥ 1.9 then classes i and j are well distinguishable

•Spotlight mode: 1 m spatial resolution• Polarization: HH• Right Ascending• Incidence angle: ~ 25° • Date of acquisition: 9th July 2010• Image dimension: 5000 x 5000 pixels

TEST IMAGETEST IMAGE


BUILDINGS EXAMPLESBUILDINGS EXAMPLES


ASPHALT EXAMPLESASPHALT EXAMPLES


VEGETATION EXAMPLESVEGETATION EXAMPLES



GLCM texture information Analysis GLCM texture information Analysis

Variation of the TD measure with the window sizeVariation of the TD measure with the window size


Mean values of the GLCM measures computed on the 4 classes

GLCM texture information Analysis GLCM texture information Analysis


1. Backscattering Intensity

2. Local mean box 3x33. Mean4. Contrast

1. Backscattering Intensity

2. Local mean box 3x33. Mean4. Contrast

Class Training Validation

A 3557 1336

LV 3347 1316

T 3274 1365

MM 3338 1354

Tot 13516 5371

Overall accuracy: 81.8%

NN TOPOLOGY: 4x12x12x4NN TOPOLOGY: 4x12x12x4

CLASSIFICATION RESULTS CLASSIFICATION RESULTS

INPUTSINPUTS


1. Backscattering Intensity2. Local mean (box 3x3)3. Mean4. Contrast

1. Backscattering Intensity2. Local mean (box 3x3)3. Mean4. Contrast

1. Backscattering Intensity2. Local mean (box 3x3)3. Local standard deviation

(box 3x3)

1. Backscattering Intensity2. Local mean (box 3x3)3. Local standard deviation

(box 3x3)

NN: 4x12x12x4 (81.8%)NN: 3x12x12x4 (73.5%)



Asphalt

Low vegetation

Trees

Manmade structures

3 INPUT (UP)3 INPUT (UP)

4 INPUT (DOWN)4 INPUT (DOWN)


• Spotlight mode: 1 m spatial resolution• Polarization: HH• Right Ascending • Incidence angle: ~ 25° • Image dimension: 4230 x 2500 pixels

Fully Automatic ClassificationFully Automatic Classification


8th June 2010

9th July 2010 10th July 2010

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8th June 2010 9th July 2010

10th July 2010

TRAINING SET (4200 pixels for each image)TRAINING SET (4200 pixels for each image)andand

VALIDATION SET (1800 pixels for each image)VALIDATION SET (1800 pixels for each image)

Asphalt

Natural areas

Manmade structures

TRAINED NEURAL TRAINED NEURAL NETWORK (3x9x9x3)NETWORK (3x9x9x3)TRAINED NEURAL TRAINED NEURAL

NETWORK (3x9x9x3)NETWORK (3x9x9x3)


Overall accuracy: 80.9%

Fully Automatic ClassificationFully Automatic Classification

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CONCLUSIONSCONCLUSIONS

• In the classification of CosmoskyMed Spotlight images, considering 4 main land cover classes and a NN algorithm, an overall accuracy above 80% can be obtained using GLCM texture information.

Ongoing: Ongoing:

• Optimization of the algorithm also with regard to the other parameters on which the GLCM depends, such as the number quantization levels.


• Use of a second image to exploit coherence information.

• Extention of the network scheme in order to incorporate also the information stemming from images taken at different polarizations and/or times of acquisition.

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ACKNOWLEDGEMENTSACKNOWLEDGEMENTS

Cosmo-SkyMed images provided by ASI -AO project 1484, agreement N. I/061/09/0

FUTURE DEVELOPMENTS FUTURE DEVELOPMENTS

• Development of a suitable automatic change detection algorithm based on Neural Networks (Pacifici et al., 2007).


Old CSK Image

New CSK Image

Features Stack 1

Features Stack 2

Multi Temporal Operator

NN1 Classification MAP1

NN2 Classification MAP2

NN3 Classification CHANGE

MASK

CHANGE MAPMAP1-MAP2

ANDNAHIRI Change

Detection

presentazione_IGARSS2011.ppt

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