Digital Landscape Architecture, Dessau – Bernburg 26 – 29. 05. 2011

kolleg.loel.hs.anhalt.de

Digital Landscape Architecture, Dessau – Bernburg 26 – 29. 05. 2011

Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Detecting Greenery in Near Infrared Images

of Ground-level Scenes

Piotr Łabędź

Agnieszka Ozimek

Institute of Computer Science

Cracow University of Technology


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Problems concerning ground-level views


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Three components of the image:

• man-made substance (building, infrastructure),

• natural substance (plants, greenery),

• the background.


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Automatic background detection


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Colour-based sky characteristics


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Intensity in RGB colour channels

300

250

200

Value

150

R

G

B

100

50

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

sample number

Colour-based sky characteristics


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

A binary image with the man-made objects marked white


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Colour similarity between vegetation and man-made objects


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Spectral characteristics of leaves


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Normalized Difference Vegetation Index (NDVI)

NDVI=

NIR

NIR


+

R

R

where:

NIR – near infrared channel

R – red channel


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Enhanced Vegetation Index (EVI)

EVI=

G

NIR

NIR−

R

+ C R + C B

1 2

+

L

where:

NIR/R/B – colour channels: near infrared, red and blue, respectively,

L - the canopy background,

C 1 and C 2 – coefficients, considering aerosol resistance in the

atmosphere,

G – gain factor,

L – soil adjusted factor.


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

MODIS EVI

(Moderate Resolution Imaging Spectroradiometer)

MODIS

EVI=

2,5

NIR

+

NIR−

R

6 ∗R

+ 7,5 * B

+ 1


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

1.2

SUNLIGHT

Relative sensitivity

0.8

0.4

0

CCD

HUMAN VISION

200 300 400 500 600 700 800 900 1000 1100 1200 1300

wavelenght

[nm]

UV

IR – (infrared)


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Reading an image in the visible light

Red channel separation

Reading an image in infrared NIR

NDVI

calculation

Numerator calculation

= difference NIR - R

Denominator calculation

= sum NIR + R

Quotient calculation

numerator / denominator

Histogram calculation

Binarization using Otsu algorithm

Y

Image closing

Resultant image writing

Is the result satisfying?

N

Manual choice of threshold

An algorithm of

greenery

detection basing

on NDVI


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Reading an image in the visible light

Reading an image in infrared NIR

Red and blue channels separation

Numerator calculation

= difference NIR - R

Modis EVI

calculation

Denominator calculation

= sum NIR + 6.5*R + 7*B + 1

Calculation of the equation:

2.5*(numerator / denominator)

Histogram calculation T

Binarization using Otsu algorithm

Y

Is the result satisfying?

N

Image closing

Resultant image writing

Manual choice of threshold

An algorithm of

greenery

detection basing

on EVI


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

An example of a photograph in the visible light


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Blue and green channels


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Red and infrared channels


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

NDVI versus MODIS EVI


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

EVI without calibrating coefficients

and the difference between it and NDVI


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

The background and greenery filtered out


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Various lighting conditions


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Various lighting conditions


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Various lighting conditions – a difference in plants detection


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

„Man-made” objects detected using the algorithm proposed


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Variations in the colour of vegetation with changes in season


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Greenery detection


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

„Cultural” components of the analysed scene


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Advantages of the method

of automatic greenery detection:

• Effective distinction between the verdure and green man-made objects.

• Clear distinction of plants parts located in the shade - high level of

reflectance in near infrared, and low in the red channel.

• Correct results in distant parts of the view.

• Effectiveness of Otsu algorithm of binarization.


Digital Landscape Architecture, DessauBernburg 2629. 05. 2011

Errors occurring in the resultant images:

• Tree trunks and branches, dry grass - the absence of chlorophyll.

• The other natural elements (soil, rocks, water) - detection impossible,

(the lack of the chlorophyll).

• Yellow leaves – increasing reflectance in the red channel.

• Inaccuracies in the objects contours, - the consequence of pixels

values interpolation (antialiasing).

• Reflections in the glossy surfaces – spectral similarity with plants.

• Photographs taken against the light, the branches not fully covered

with leaves.

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