1

Analysis of Polarimetric SAR data for Land cover mapping in Mountainous Landscape

John Richard Otukei

Prof. Dr. Thomas Blaschke (Uni. Salzburg)Prof. Dr. Michael Collins (Uni. Calgary)

Center for GeoInformatics University of Salzburg

PRESENTATION OUTLINE

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations

2

3

COSPAR (BR)

MSC (GIS), UK

PhD, AT

PGD (GIS& RS), NG

MSC (GEOMATICS, SA

BSC(SUR), UG

Uganda

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Land cover is a fundamental variable that impacts on and links with many parts of human and physical Environment (Foody, 2002) and is strongly associated with climatic change (Skole, 1994)

Despite the significant role land cover plays, our knowledge of land cover especially in Sub-Saharan Africa is lacking (Otukei& Blaschke, 2009)

The limited knowledge can be attributed to a number of factors:

Weak Government support to Mapping agencies and research institutions

Expensive hardware and software Resistance to changes from traditionalists in

the field of mapping Brain drain Lack of experts in Mapping sciences

4

Why bother with Land Cover?

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Because of aforementioned factors, “most developing countries lack up-to-date information, a key component for environmental monitoring and understanding”

The traditional approaches for collecting environmental information, notably, aerial photography, although accurate are:

Laborious Expensive Done infrequently

In the case of Uganda, the existing reference maps are based on the 1954 aerial photography carried by the ordnance survey of GB. These maps were re-printed in 1978 and 2000.

5

The problem statement

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Since up-to-date information is critical for increased awareness of environmental issues and sustainable use of natural resources, alternative and suitable approaches especially those based on earth observation are desired

As a result this study is motivated by the desire to explore advancements in the field remote sensing for land cover mapping i.e.

Analysis of Polarimetric SAR data for Land cover mapping in Mountainous Landscape

6

The motivation for the study

The BiG Point: What information can be obtained from analysis of SAR data in such environments???

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

The overall goal of this study is to exploit the inherent high spatial, high textured and multi-polarised SAR data for tropical mountain forest cover mapping in the BINP and its immediate surroundings

7

Overall goal

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

UNESCO’S world heritage site Diversity of fauna and flora Social benefits Ecological benefits Economic benefits

8

WHY BWINDI????

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

To carry out an in-depth study of the use of SAR data for land cover mapping and in particular examining the effect of different polarisations as well as derived spatial features such as image texture for improved land cover mapping

To critically examine the potential of combining high spatial SAR data and high spectral optical data for improved land cover mapping

9

Research Objectives

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Hypothesis 1:

A multi polarised SAR data with high

spatial resolution provides better land

cover identification and mapping

compared with single SAR channels.

Hypothesis 2: Inclusion of derived features such as

image texture provides improved

land cover information extraction

compared to processing of original

multi-polarised bands or Landsat TM

data only.

Hypothesis 3:

The fusion of the high textured and high

resolution SAR data with high spectral

but medium spatial resolution Landsat

TM provides better results compared to

those obtained using either SAR or

Landsat TM alone. 10

Research hypothesis

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

What land cover types can be identified and mapped using SAR data.

To what extent does the inclusion of image texture improve the identification and mapping of land cover types? And

Which image textures are suitable for the land cover identification and mapping?

Does the fusion of SAR and Landsat TM data improve the identification and mapping of the land cover classes? If so, what extent does it improve the classification results

11

Research questions

12

Location of Study Area

13

DataSwamps

Processed data

Target data

Transformed data

Knowledge patterns

Knowledge

Data selection

DataPre-

processing

Data Transformation

Data mining

Interpretation

Cleaning, geo-referencing,Sub-setting, mosaicking...

Looking for:• A research

problem(s)• Objectives• questions e.t.c

Textures

ClassificationDTs, OBIA,

• Maps• Tables• Statistic

s

Research Framework

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

SAR DATA:

Dual Polarised TerraSAR-X data, StripMap Mode.

HH & VV Polarised channels

Quad polarised ALOS-PALSAR

Category-1 proposal number 7583

14

Data Selection

Courtesy of UNESCO-DLR agreement Project ID: LAN0599

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Optical Data:

4 Band IKONOS data ( 2005, 2006, 2007)

Landsat ETM+ (2008)

15

Data Selection

Courtesy of GeoEye Foundation Project ID: 152118

Other Data sets: ASTER DEM

SRTM Aerial photos/other Shapefiles

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusions

Recommendations

Ortho-rectification ( IKONOS data) Landsat (ETM+) gap filling Mosaicking, sub-setting, layer stacking

16

Pre-processing

17

December 15 HH/VV image

Dec 04 HH/VV image

Mosaicking

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Texture derivations.

3 different classes of textures were

computed: SAR specific textures (4) Textures based on Grey level Co-

occurance Matrices (GLCM)(9) Textures based on image histogram

(12) Big points:

How to determine the appropriate window size (W)?

How to determine the grey level value (R)to the computation of GLCM

18

Data transformation

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Pixel based approaches (DTs and WMLC)

OBIA/GEOBIA

19

Polararimtric data analysis

BUT…. BUT …..BUT: There was need for some ground truth here…

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendation20

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusions

Recommendations

A classification scheme was established using

21

Got ground truth data, what next?

AFRICOVER LULC classification system

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

22

Polarimetry analysis (DTs)

HH VV HH+VV HH+VV+VI

HH+VV+GLCM

HH+VV+Histex

All0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

Overa

ll K

appa

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

23

Polarimetry analysis (Wishart)

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

24

Image fusion (Pixel)

OW DW VW PS SL DEGF DHF TP MR BF MF DF OK0

0.2

0.4

0.6

0.8

1

1.2

HFP PCA PCAW

Land cover classes

Kappa s

tati

sti

cs

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

25

OBIA

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Qual polarimetric ALOS PALSAR performs better than Dual Polarimetric TerraSAR-X

The Cross polarised SAR channel has the highest contribution to overall classification accuracy

OBIA analysis shows more realistic results than pixel based method.

26

Polarimetric Analysis

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Improves the classification accuracy of the SAR data

SAR specific textures have high potential for classification especially when only single texture band is included

27

Texture analysis

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Improved classification accuracy only possible with OBIA

Terrain effects (layover, shadows and foreshortening affects image fusion using both techniques but the effect is more noticeable when using pixel based methods

28

Image Fusion

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Data... Data... Data... Data, not easy to get

Incompatible data, Most software packages have limited support for TerraSAR-X data

Some software packages that have support may not support all levels of processing of TerraSAR-X data. Case of Geomatica, PolSARPro, Photomod e.t.c

How to process large data sets ( case of high spatial resolution TerraSAR-X

29

Some Challenges ..

Introduction

Problem statement

Motivation

Overall goal

Research objectives

Research hypothesis

Research questions

Study area

Research framework

Results

Conclusion

Recommendations

Full quad polarised TerraSAR-X is desirable BUT cross-polarised channel is essential.

OBIA and analysis based on Wishart probability are recommended for analysis of ALOS-PALSAR and TerraSAR-X data

For mountainous areas, image fusion in the context of OBIA is recommended.

High demands on data processing can be minimised using batch processing

30

Recommendations

31

Ackowledgements

32

I THANK

YOUFOR

LISTENING