SlideShare uma empresa Scribd logo
1 de 45
Shaunak De
Centre of Studies In Resources Engineering – IIT Bombay
14-Jun-13 1
14-Jun-13 2
14-Jun-13 3
14-Jun-13 4
• Input Product Directory
• Sub-Directories and Files will be detected
Automatically
• Output Directory
• Exported products will be placed here
Multilook Factor and other
information displayed here
Leader files, Scenes, Data Files, Grid
Files, Meta Files are listed here
• Enter the desired Range multilook
• Azimuth multilook is automatically
calculated
Choose the desired
outputs and click “Run”
14-Jun-13 5
• Two channel data – i.e. RH and RV
• Supplied as I,Q (complex) 16 bit
integer values
• After conversion to float C2 is calculated:
14-Jun-13 6
RGB Color Composition 2
Blue = C22
Green = C11 +2 Re(C12) + C22
Red = C11
RGB Color Composition 1
Blue = C11
Green = C11 +2 Re(C12) + C22
Red= C22
14-Jun-13 7
• The “I” and “Q” values for each pixel are
supplied as 16 bit integers
• Converted to complex floating point
• Radiometric correction of data
• The calibration constant (KdB) is supplied
RH 0 (db) - Mumbai
14-Jun-13 8
14-Jun-13 9
3
14-Jun-13 10
3
• Select filter type to be applied:
• Boxcar Filter
• Refined Lee Filter
Select Filter
Size
Set the input and
output directories
Click Run to apply filter
14-Jun-13 11
Unfiltered 5x5 Refined Lee Filtered
Crop of cFRS-1 mode acquired over Mumbai by RISAT - 1
14-Jun-13 12
m-delta ( ) Decomposition:
)1(
2
sin1
2
sin1
0
0
0
mSfG
mSfR
mSfB
diffused
even
odd
m-chi ( ) Decomposition:
)1(
2
2sin1
2
2sin1
0
0
0
mSfG
mSfR
mSfB
diffused
even
odd
m-alpha ( ) Decomposition:
)1(
2
2cos1
2
2cos1
0
0
0
mSfG
mSfR
mSfB
volume
dihedral
surface
Cloude, et al., IEEE GRS Letters, 9(1), Jan 2012Raney et al., JGR, Vol. 117, E00H21, 2012
Stokes Parameters: (RH-RV Case, BSA Convention)
0
2
3
2
2
2
1
S
SSS
m
2
31
tan(deg)
S
S
0
3
2sin
mS
S
Degree of Polarization: Relative RH-RV Phase:
Degree of Circularity:
3
2
2
2
11
tan
2
1
(deg)
S
SS
Scattering mechanism:
Stokes Vector: (RH-RV Case) - BSA
22
1
22
0
RVRHS
RVRHS
*
3
*
2
2
2
RVRHS
RVRHS
14-Jun-13 13
Decomposition techniques applied to cFRS-1 Image acquired over Mumbai by RISAT-1
14-Jun-13 14
• Ratio of the Same Sense to
Opposite Sense echo
powers
• Indicator of the degree
of wavelength scale surface
and/or near subsurface
roughness
14-Jun-13 15
11 11 22 12
13 11 22 12
22 11 22 12
31 11 22 12
33 11 22 12
7 2Im
6
2Im
6
7 2Im
C J J J
C J J j J
C J J J
C J J j J
C J J J
CP
CP
CC
C
CC
C
3331
22
1311
0
00
0
Where :
Reflection and Rotation
symmetry assumptions
2221
1211*
)2/()2/(2/
2
1
*
JJ
JJ
kkJ
T
14-Jun-13 16
14-Jun-13 17
Set Input Directory
Set Output Directory
Run the process
14-Jun-13 18
Reference: Gunter Schreier(Ed.) SAR Geocoding Data and Systems (Publisher: Wichman)
• Nearest Neighbor
14-Jun-13 19
14-Jun-13 20
Select the file to be geocoded
Any floating point bin file is supportedProvide the “infoRH.txt” file
This is in the directory where the product was
extracted
Choose the resampling method Run the process
Output will be automatically
opened in OpenEV
S-Curve Stretching gives good
visualization
14-Jun-13 21
Geocoded C11 image of Mumbai, India
Acquired by RISAT-1 in cFRS-1 mode
15-NOV-2012
Scene Center
Longitude:72.930005
Latitude :19.220882
14-Jun-13 22
ALOS L Band – Fully Polarimetric Image is used for this comparison
14-Jun-13 23
Pauli RGB Freeman 3 component
14-Jun-13 24
VanZyl 3 component Yamaguchi 3 component
14-Jun-13 25
Arii ANNED 3 component Arii NNED 3 component
14-Jun-13 26
Y4O Y4R
14-Jun-13 27
G4U1 G4U2
14-Jun-13 28
S4R Y4R
14-Jun-13 29
Classified image of cFRS-1 scene acquired over Mumbai, India.
Urban Forest Mangroves Water Wetland
14-Jun-13 30
Wishart Supervised Classification
Lee Refined Filter – 5x5 window
Co-Register datasets
Multilook to match other images
Import – C2 matrix
ALOS-PALSAR RADARSAT-2 TerraSAR-X RISAT-1
Sensors Band Polarization
Pixel
Size (m)
RISAT-1 C Hybrid Pol 2.5
RADARSAT-2 C Full Pol 8.0
ALOS-PALSAR L Full Pol 24.0
TerraSAR-X X HH and HV 3.0
14-Jun-13 31
Classifier Wishart Classifier
Classes
TerraSAR-X
band Dual Pol.
RISAT-1-C band
Compact Pol
RADARSAT-C
band simulated
Compact Pol
RADARSAT-C
band Full Pol.
ALOS-PALSAR-L
band Full Pol.
Water 99.66 81.03 100 100 87.94
Mangroves 74.93 88.06 77.49 89.86 91.78
Urban 98.17 91.53 97.03 98.63 100
Forest 60.26 81.00 77.42 82.19 93.49
Saltpan 63.77 68.6 95.89 96.38 85.99
Wetland 89.96 89.96 97.77 97.54 73.44
Grassland 83.78 90.54 85.59 92.34 90.54
Accuracy % 81.84 83.40 89.96 93.40 89.38
14-Jun-13 32
ALOS-PALSAR RADARSAT-2 TerraSAR-X RADARSAT-2 RISAT-1
Full pol Full Pol Dual Pol Simulated Hyb. Hyb.pol
Water Mangroves Urban Forest Saltpans Wetland Grassland
14-Jun-13 33
Classification
SVM Wishart
Decomposition
m-δ m- SPAN CPR
Co-Register Datasets
5x5 Refined Lee Filter
Multilook to match resolution
3:3 Multilook 1:1 Multilook
Import and Prepare Data
RISAT-1 (Hybrid) RADARSAT-2 (Simulated Hyb)
Sensors Band Polarization
Pixel
Size (m)
RISAT-1 C Hybrid Pol 2.5
RADARSAT-
2
C Full Pol 8.0
14-Jun-13 34
Class
RISAT-I RADARSAT-2
Wishart
m-δ,CPR,SPAN
(SVM)
m-χ,
CPR,SPAN
(SVM)
Wishart
m-δ,CPR-
SPAN (SVM)
m-χ,
CPR,SPAN
(SVM)
Water 78.59 84.32 84.00 99.95 98.91 99.05
Mangrov
es
60.60 88.92 88.99 75.25 71.85 70.72
Urban 56.74 60.99 63.43 91.73 92.06 92.25
Forest 56.15 81.54 81.54 52.87 54.94 56.88
Wetland 60.22 84.22 84.78 97.09 97.75 98.26
Overall
Acc.%
64.17 81.53 81.86 81.35 80.68 80.91
14-Jun-13 35
Classified RISAT-1 image acquired over
Mumbai in cFRS-1 mode.
SVM classifier used on m-
decomposed image along with SPAN
and CPR
Urban Forest Mangroves Water Wetland
14-Jun-13 36
Convert RISAT to Pseudo Full pol
Signature analysis Backscatter Analysis
Wishart Classification
Intensity Complex
Co-Register Datasets
5x5 Refined Lee Filter
Multilook to reduce speckle
2:2 Multilook
Import and Prepare Data
RISAT-1 (HybridPol) RADARSAT-2 (Full Pol)
Sensors Band Polarization
Pixel
Size (m)
RISAT-1 C Hybrid Pol 2.5
RADARSAT-
2
C Full Pol 8.0
14-Jun-13 37
Variation of H/AlphaMean and Standard Deviation of σ0
• Urban features: hybrid σ0 shows higher value with more standard deviation
• Urban features can be clearly discriminated
• The other features are not discriminable – low dynamic range (within 3dB)
• Channel Imbalance observed
14-Jun-13 38
Linear Pol
FCC RGB
Hybrid Pol
FCC RGB
LinearComplexLinearIntensity
HybridComplexHybridIntensity
Urban
Water
Forest
Mangroves
Wetland
Field Work
14-Jun-13 39
Polarization
/Class
Linear(HH,HV)
Intensity
Circular(RH,RV)
Intensity
Linear(HH, HV)
Complex
Circular(RH,RV)
Complex
Urban 70.77 85.27 70.68 82.50
Forest 73.50 52.95 78.85 87.41
Water 95.19 99.45 94.99 99.83
Mangroves 80.09 59.78 82.88 91.74
Wetland 62.57 91.55 73.11 98.24
Accuracy
(%)
83.48 79.60 86.38 91.79
14-Jun-13 40
RADARSAT-2 RISAT-1
Urban_Copol
Urban_Crospol
14-Jun-13 41
RADARSAT-2 RISAT-1
Water_Copol
Water_crosspol
14-Jun-13 42
Saoner Test Site
• Scene center:
• 21 23′09″N Latitude
• 78 55′12″E Longitude
• Major Crops:
• Paddy
• Sugarcane
• Wheat
• Gram
14-Jun-13 43
0%
57.5%
0%
41.4%
m-delta m-chi m-alpha
14-Jun-13 44
Thank
You!
14-Jun-13 45

Mais conteúdo relacionado

Mais procurados

Low power & area efficient carry select adder
Low power & area efficient carry select adderLow power & area efficient carry select adder
Low power & area efficient carry select adder
Sai Vara Prasad P
 
Load testing of HELIDEM geo-portal: an OGC open standards interoperability ex...
Load testing of HELIDEM geo-portal: an OGC open standards interoperability ex...Load testing of HELIDEM geo-portal: an OGC open standards interoperability ex...
Load testing of HELIDEM geo-portal: an OGC open standards interoperability ex...
Massimiliano Cannata
 
Implementation of Area Effective Carry Select Adders
Implementation of Area Effective Carry Select AddersImplementation of Area Effective Carry Select Adders
Implementation of Area Effective Carry Select Adders
Kumar Goud
 
Friedrich - LiDAR CADD Engr. Design
Friedrich - LiDAR CADD Engr. DesignFriedrich - LiDAR CADD Engr. Design
Friedrich - LiDAR CADD Engr. Design
Jose A. Hernandez
 
Land Use/Land Cover Detection
Land Use/Land Cover DetectionLand Use/Land Cover Detection
Land Use/Land Cover Detection
Wansoo Im
 

Mais procurados (20)

Low power & area efficient carry select adder
Low power & area efficient carry select adderLow power & area efficient carry select adder
Low power & area efficient carry select adder
 
Load testing of HELIDEM geo-portal: an OGC open standards interoperability ex...
Load testing of HELIDEM geo-portal: an OGC open standards interoperability ex...Load testing of HELIDEM geo-portal: an OGC open standards interoperability ex...
Load testing of HELIDEM geo-portal: an OGC open standards interoperability ex...
 
A Novel Efficient VLSI Architecture Modified 16-B SQRT Carry Select Adder
A Novel Efficient VLSI Architecture Modified 16-B SQRT Carry Select AdderA Novel Efficient VLSI Architecture Modified 16-B SQRT Carry Select Adder
A Novel Efficient VLSI Architecture Modified 16-B SQRT Carry Select Adder
 
Automated change detection in grass gis
Automated change detection in grass gisAutomated change detection in grass gis
Automated change detection in grass gis
 
Design and Verification of Area Efficient Carry Select Adder
Design and Verification of Area Efficient Carry Select AdderDesign and Verification of Area Efficient Carry Select Adder
Design and Verification of Area Efficient Carry Select Adder
 
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
 
Implementation of Area Effective Carry Select Adders
Implementation of Area Effective Carry Select AddersImplementation of Area Effective Carry Select Adders
Implementation of Area Effective Carry Select Adders
 
High Speed Carryselect Adder
High Speed Carryselect AdderHigh Speed Carryselect Adder
High Speed Carryselect Adder
 
Friedrich - LiDAR CADD Engr. Design
Friedrich - LiDAR CADD Engr. DesignFriedrich - LiDAR CADD Engr. Design
Friedrich - LiDAR CADD Engr. Design
 
Meteo I/O Introduction
Meteo I/O IntroductionMeteo I/O Introduction
Meteo I/O Introduction
 
Area–delay–power efficient carry select adder
Area–delay–power efficient carry select adderArea–delay–power efficient carry select adder
Area–delay–power efficient carry select adder
 
Area Delay Power Efficient and Implementation of Modified Square-Root Carry S...
Area Delay Power Efficient and Implementation of Modified Square-Root Carry S...Area Delay Power Efficient and Implementation of Modified Square-Root Carry S...
Area Delay Power Efficient and Implementation of Modified Square-Root Carry S...
 
High speed and energy-efficient carry skip adder operating under a wide range...
High speed and energy-efficient carry skip adder operating under a wide range...High speed and energy-efficient carry skip adder operating under a wide range...
High speed and energy-efficient carry skip adder operating under a wide range...
 
Processing and Retrieval of Geotagged Unmanned Aerial System Telemetry
Processing and Retrieval of Geotagged Unmanned Aerial System TelemetryProcessing and Retrieval of Geotagged Unmanned Aerial System Telemetry
Processing and Retrieval of Geotagged Unmanned Aerial System Telemetry
 
Land Use/Land Cover Detection
Land Use/Land Cover DetectionLand Use/Land Cover Detection
Land Use/Land Cover Detection
 
Design and development of carry select adder
Design and development of carry select adderDesign and development of carry select adder
Design and development of carry select adder
 
Use FME To Efficiently Create National-Scale Vector Contours From High-Resolu...
Use FME To Efficiently Create National-Scale Vector Contours From High-Resolu...Use FME To Efficiently Create National-Scale Vector Contours From High-Resolu...
Use FME To Efficiently Create National-Scale Vector Contours From High-Resolu...
 
AI models for Ice Classification - ExtremeEarth Open Workshop
AI models for Ice Classification - ExtremeEarth Open WorkshopAI models for Ice Classification - ExtremeEarth Open Workshop
AI models for Ice Classification - ExtremeEarth Open Workshop
 
A Novel Route Optimized Cluster Based Routing Protocol for Pollution Controll...
A Novel Route Optimized Cluster Based Routing Protocol for Pollution Controll...A Novel Route Optimized Cluster Based Routing Protocol for Pollution Controll...
A Novel Route Optimized Cluster Based Routing Protocol for Pollution Controll...
 
1 catchment delineation.ppt
1 catchment delineation.ppt1 catchment delineation.ppt
1 catchment delineation.ppt
 

Semelhante a PolSDP - 29May13

Drury 1975 atc-39_ww-15318
Drury 1975 atc-39_ww-15318Drury 1975 atc-39_ww-15318
Drury 1975 atc-39_ww-15318
Saipradeepci
 
Backscatter Working Group Software Inter-comparison Project Requesting and Co...
Backscatter Working Group Software Inter-comparison ProjectRequesting and Co...Backscatter Working Group Software Inter-comparison ProjectRequesting and Co...
Backscatter Working Group Software Inter-comparison Project Requesting and Co...
Giuseppe Masetti
 
Research Inventy : International Journal of Engineering and Science
Research Inventy : International Journal of Engineering and ScienceResearch Inventy : International Journal of Engineering and Science
Research Inventy : International Journal of Engineering and Science
researchinventy
 
Hailey_Database_Performance_Made_Easy_through_Graphics.pdf
Hailey_Database_Performance_Made_Easy_through_Graphics.pdfHailey_Database_Performance_Made_Easy_through_Graphics.pdf
Hailey_Database_Performance_Made_Easy_through_Graphics.pdf
cookie1969
 
BWC Supercomputing 2008 Presentation
BWC Supercomputing 2008 PresentationBWC Supercomputing 2008 Presentation
BWC Supercomputing 2008 Presentation
lilyco
 

Semelhante a PolSDP - 29May13 (20)

Data Quality Analysis of Different Receivers Based on Static Base Station
Data Quality Analysis of Different Receivers Based on Static Base StationData Quality Analysis of Different Receivers Based on Static Base Station
Data Quality Analysis of Different Receivers Based on Static Base Station
 
Open Analytics Environment
Open Analytics EnvironmentOpen Analytics Environment
Open Analytics Environment
 
I/ITSEC2009 Best Tutorial
I/ITSEC2009 Best TutorialI/ITSEC2009 Best Tutorial
I/ITSEC2009 Best Tutorial
 
Klessydra t - designing vector coprocessors for multi-threaded edge-computing...
Klessydra t - designing vector coprocessors for multi-threaded edge-computing...Klessydra t - designing vector coprocessors for multi-threaded edge-computing...
Klessydra t - designing vector coprocessors for multi-threaded edge-computing...
 
Drury 1975 atc-39_ww-15318
Drury 1975 atc-39_ww-15318Drury 1975 atc-39_ww-15318
Drury 1975 atc-39_ww-15318
 
Backscatter Working Group Software Inter-comparison Project Requesting and Co...
Backscatter Working Group Software Inter-comparison ProjectRequesting and Co...Backscatter Working Group Software Inter-comparison ProjectRequesting and Co...
Backscatter Working Group Software Inter-comparison Project Requesting and Co...
 
RichardPughspatial.ppt
RichardPughspatial.pptRichardPughspatial.ppt
RichardPughspatial.ppt
 
⭐⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine lea...
⭐⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine lea...⭐⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine lea...
⭐⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine lea...
 
Research Inventy : International Journal of Engineering and Science
Research Inventy : International Journal of Engineering and ScienceResearch Inventy : International Journal of Engineering and Science
Research Inventy : International Journal of Engineering and Science
 
IRJET- Survey on Adaptive Routing Algorithms
IRJET- Survey on Adaptive Routing AlgorithmsIRJET- Survey on Adaptive Routing Algorithms
IRJET- Survey on Adaptive Routing Algorithms
 
NDGISUC2017 - Development of an Open Source Alternative Climate Database Utility
NDGISUC2017 - Development of an Open Source Alternative Climate Database UtilityNDGISUC2017 - Development of an Open Source Alternative Climate Database Utility
NDGISUC2017 - Development of an Open Source Alternative Climate Database Utility
 
Thesis
ThesisThesis
Thesis
 
Thesis
ThesisThesis
Thesis
 
Pollution
PollutionPollution
Pollution
 
Carrier recovery and clock recovery for qpsk demodulation
Carrier recovery and clock recovery for qpsk demodulationCarrier recovery and clock recovery for qpsk demodulation
Carrier recovery and clock recovery for qpsk demodulation
 
MetOp Satellites Data Processing for Air Pollution Monitoring in Morocco
MetOp Satellites Data Processing for Air Pollution Monitoring in Morocco MetOp Satellites Data Processing for Air Pollution Monitoring in Morocco
MetOp Satellites Data Processing for Air Pollution Monitoring in Morocco
 
Developing digital signal clustering method using local binary pattern histog...
Developing digital signal clustering method using local binary pattern histog...Developing digital signal clustering method using local binary pattern histog...
Developing digital signal clustering method using local binary pattern histog...
 
Hailey_Database_Performance_Made_Easy_through_Graphics.pdf
Hailey_Database_Performance_Made_Easy_through_Graphics.pdfHailey_Database_Performance_Made_Easy_through_Graphics.pdf
Hailey_Database_Performance_Made_Easy_through_Graphics.pdf
 
BWC Supercomputing 2008 Presentation
BWC Supercomputing 2008 PresentationBWC Supercomputing 2008 Presentation
BWC Supercomputing 2008 Presentation
 
Thesis_Presentation
Thesis_PresentationThesis_Presentation
Thesis_Presentation
 

Mais de Shaunak De

Classification Accuracy for RISAT-1 Hybrid Polarimetric Data
Classification Accuracy for RISAT-1 Hybrid Polarimetric DataClassification Accuracy for RISAT-1 Hybrid Polarimetric Data
Classification Accuracy for RISAT-1 Hybrid Polarimetric Data
Shaunak De
 
Glacial mass balance changes in the karakoram
Glacial mass balance changes in the karakoramGlacial mass balance changes in the karakoram
Glacial mass balance changes in the karakoram
Shaunak De
 

Mais de Shaunak De (6)

Predicting the Popularity of Instagram Posts for a Lifestyle Magazine Using D...
Predicting the Popularity of Instagram Posts for a Lifestyle Magazine Using D...Predicting the Popularity of Instagram Posts for a Lifestyle Magazine Using D...
Predicting the Popularity of Instagram Posts for a Lifestyle Magazine Using D...
 
Orientation Angle Estimation from PolSAR Data using a Stochastic Distance
Orientation Angle Estimation from PolSAR Data using a Stochastic DistanceOrientation Angle Estimation from PolSAR Data using a Stochastic Distance
Orientation Angle Estimation from PolSAR Data using a Stochastic Distance
 
Classification Accuracy for RISAT-1 Hybrid Polarimetric Data
Classification Accuracy for RISAT-1 Hybrid Polarimetric DataClassification Accuracy for RISAT-1 Hybrid Polarimetric Data
Classification Accuracy for RISAT-1 Hybrid Polarimetric Data
 
Glacial mass balance changes in the karakoram
Glacial mass balance changes in the karakoramGlacial mass balance changes in the karakoram
Glacial mass balance changes in the karakoram
 
Speak Thousands of words!
Speak Thousands of words!Speak Thousands of words!
Speak Thousands of words!
 
How big is infinity
How big is infinityHow big is infinity
How big is infinity
 

Último

The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
heathfieldcps1
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
ciinovamais
 
Vishram Singh - Textbook of Anatomy Upper Limb and Thorax.. Volume 1 (1).pdf
Vishram Singh - Textbook of Anatomy  Upper Limb and Thorax.. Volume 1 (1).pdfVishram Singh - Textbook of Anatomy  Upper Limb and Thorax.. Volume 1 (1).pdf
Vishram Singh - Textbook of Anatomy Upper Limb and Thorax.. Volume 1 (1).pdf
ssuserdda66b
 

Último (20)

General Principles of Intellectual Property: Concepts of Intellectual Proper...
General Principles of Intellectual Property: Concepts of Intellectual  Proper...General Principles of Intellectual Property: Concepts of Intellectual  Proper...
General Principles of Intellectual Property: Concepts of Intellectual Proper...
 
How to Manage Global Discount in Odoo 17 POS
How to Manage Global Discount in Odoo 17 POSHow to Manage Global Discount in Odoo 17 POS
How to Manage Global Discount in Odoo 17 POS
 
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
 
Single or Multiple melodic lines structure
Single or Multiple melodic lines structureSingle or Multiple melodic lines structure
Single or Multiple melodic lines structure
 
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxHMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
 
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
 
Graduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - EnglishGraduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - English
 
SOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning PresentationSOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning Presentation
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
 
Sociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning ExhibitSociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning Exhibit
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
 
Google Gemini An AI Revolution in Education.pptx
Google Gemini An AI Revolution in Education.pptxGoogle Gemini An AI Revolution in Education.pptx
Google Gemini An AI Revolution in Education.pptx
 
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxBasic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
 
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdfUGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
 
Vishram Singh - Textbook of Anatomy Upper Limb and Thorax.. Volume 1 (1).pdf
Vishram Singh - Textbook of Anatomy  Upper Limb and Thorax.. Volume 1 (1).pdfVishram Singh - Textbook of Anatomy  Upper Limb and Thorax.. Volume 1 (1).pdf
Vishram Singh - Textbook of Anatomy Upper Limb and Thorax.. Volume 1 (1).pdf
 
This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.
 
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17  How to Extend Models Using Mixin ClassesMixin Classes in Odoo 17  How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
 
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptxSKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
 

PolSDP - 29May13

  • 1. Shaunak De Centre of Studies In Resources Engineering – IIT Bombay 14-Jun-13 1
  • 5. • Input Product Directory • Sub-Directories and Files will be detected Automatically • Output Directory • Exported products will be placed here Multilook Factor and other information displayed here Leader files, Scenes, Data Files, Grid Files, Meta Files are listed here • Enter the desired Range multilook • Azimuth multilook is automatically calculated Choose the desired outputs and click “Run” 14-Jun-13 5
  • 6. • Two channel data – i.e. RH and RV • Supplied as I,Q (complex) 16 bit integer values • After conversion to float C2 is calculated: 14-Jun-13 6
  • 7. RGB Color Composition 2 Blue = C22 Green = C11 +2 Re(C12) + C22 Red = C11 RGB Color Composition 1 Blue = C11 Green = C11 +2 Re(C12) + C22 Red= C22 14-Jun-13 7
  • 8. • The “I” and “Q” values for each pixel are supplied as 16 bit integers • Converted to complex floating point • Radiometric correction of data • The calibration constant (KdB) is supplied RH 0 (db) - Mumbai 14-Jun-13 8
  • 11. 3 • Select filter type to be applied: • Boxcar Filter • Refined Lee Filter Select Filter Size Set the input and output directories Click Run to apply filter 14-Jun-13 11
  • 12. Unfiltered 5x5 Refined Lee Filtered Crop of cFRS-1 mode acquired over Mumbai by RISAT - 1 14-Jun-13 12
  • 13. m-delta ( ) Decomposition: )1( 2 sin1 2 sin1 0 0 0 mSfG mSfR mSfB diffused even odd m-chi ( ) Decomposition: )1( 2 2sin1 2 2sin1 0 0 0 mSfG mSfR mSfB diffused even odd m-alpha ( ) Decomposition: )1( 2 2cos1 2 2cos1 0 0 0 mSfG mSfR mSfB volume dihedral surface Cloude, et al., IEEE GRS Letters, 9(1), Jan 2012Raney et al., JGR, Vol. 117, E00H21, 2012 Stokes Parameters: (RH-RV Case, BSA Convention) 0 2 3 2 2 2 1 S SSS m 2 31 tan(deg) S S 0 3 2sin mS S Degree of Polarization: Relative RH-RV Phase: Degree of Circularity: 3 2 2 2 11 tan 2 1 (deg) S SS Scattering mechanism: Stokes Vector: (RH-RV Case) - BSA 22 1 22 0 RVRHS RVRHS * 3 * 2 2 2 RVRHS RVRHS 14-Jun-13 13
  • 14. Decomposition techniques applied to cFRS-1 Image acquired over Mumbai by RISAT-1 14-Jun-13 14
  • 15. • Ratio of the Same Sense to Opposite Sense echo powers • Indicator of the degree of wavelength scale surface and/or near subsurface roughness 14-Jun-13 15
  • 16. 11 11 22 12 13 11 22 12 22 11 22 12 31 11 22 12 33 11 22 12 7 2Im 6 2Im 6 7 2Im C J J J C J J j J C J J J C J J j J C J J J CP CP CC C CC C 3331 22 1311 0 00 0 Where : Reflection and Rotation symmetry assumptions 2221 1211* )2/()2/(2/ 2 1 * JJ JJ kkJ T 14-Jun-13 16
  • 18. Set Input Directory Set Output Directory Run the process 14-Jun-13 18
  • 19. Reference: Gunter Schreier(Ed.) SAR Geocoding Data and Systems (Publisher: Wichman) • Nearest Neighbor 14-Jun-13 19
  • 21. Select the file to be geocoded Any floating point bin file is supportedProvide the “infoRH.txt” file This is in the directory where the product was extracted Choose the resampling method Run the process Output will be automatically opened in OpenEV S-Curve Stretching gives good visualization 14-Jun-13 21
  • 22. Geocoded C11 image of Mumbai, India Acquired by RISAT-1 in cFRS-1 mode 15-NOV-2012 Scene Center Longitude:72.930005 Latitude :19.220882 14-Jun-13 22
  • 23. ALOS L Band – Fully Polarimetric Image is used for this comparison 14-Jun-13 23
  • 24. Pauli RGB Freeman 3 component 14-Jun-13 24
  • 25. VanZyl 3 component Yamaguchi 3 component 14-Jun-13 25
  • 26. Arii ANNED 3 component Arii NNED 3 component 14-Jun-13 26
  • 30. Classified image of cFRS-1 scene acquired over Mumbai, India. Urban Forest Mangroves Water Wetland 14-Jun-13 30
  • 31. Wishart Supervised Classification Lee Refined Filter – 5x5 window Co-Register datasets Multilook to match other images Import – C2 matrix ALOS-PALSAR RADARSAT-2 TerraSAR-X RISAT-1 Sensors Band Polarization Pixel Size (m) RISAT-1 C Hybrid Pol 2.5 RADARSAT-2 C Full Pol 8.0 ALOS-PALSAR L Full Pol 24.0 TerraSAR-X X HH and HV 3.0 14-Jun-13 31
  • 32. Classifier Wishart Classifier Classes TerraSAR-X band Dual Pol. RISAT-1-C band Compact Pol RADARSAT-C band simulated Compact Pol RADARSAT-C band Full Pol. ALOS-PALSAR-L band Full Pol. Water 99.66 81.03 100 100 87.94 Mangroves 74.93 88.06 77.49 89.86 91.78 Urban 98.17 91.53 97.03 98.63 100 Forest 60.26 81.00 77.42 82.19 93.49 Saltpan 63.77 68.6 95.89 96.38 85.99 Wetland 89.96 89.96 97.77 97.54 73.44 Grassland 83.78 90.54 85.59 92.34 90.54 Accuracy % 81.84 83.40 89.96 93.40 89.38 14-Jun-13 32
  • 33. ALOS-PALSAR RADARSAT-2 TerraSAR-X RADARSAT-2 RISAT-1 Full pol Full Pol Dual Pol Simulated Hyb. Hyb.pol Water Mangroves Urban Forest Saltpans Wetland Grassland 14-Jun-13 33
  • 34. Classification SVM Wishart Decomposition m-δ m- SPAN CPR Co-Register Datasets 5x5 Refined Lee Filter Multilook to match resolution 3:3 Multilook 1:1 Multilook Import and Prepare Data RISAT-1 (Hybrid) RADARSAT-2 (Simulated Hyb) Sensors Band Polarization Pixel Size (m) RISAT-1 C Hybrid Pol 2.5 RADARSAT- 2 C Full Pol 8.0 14-Jun-13 34
  • 35. Class RISAT-I RADARSAT-2 Wishart m-δ,CPR,SPAN (SVM) m-χ, CPR,SPAN (SVM) Wishart m-δ,CPR- SPAN (SVM) m-χ, CPR,SPAN (SVM) Water 78.59 84.32 84.00 99.95 98.91 99.05 Mangrov es 60.60 88.92 88.99 75.25 71.85 70.72 Urban 56.74 60.99 63.43 91.73 92.06 92.25 Forest 56.15 81.54 81.54 52.87 54.94 56.88 Wetland 60.22 84.22 84.78 97.09 97.75 98.26 Overall Acc.% 64.17 81.53 81.86 81.35 80.68 80.91 14-Jun-13 35
  • 36. Classified RISAT-1 image acquired over Mumbai in cFRS-1 mode. SVM classifier used on m- decomposed image along with SPAN and CPR Urban Forest Mangroves Water Wetland 14-Jun-13 36
  • 37. Convert RISAT to Pseudo Full pol Signature analysis Backscatter Analysis Wishart Classification Intensity Complex Co-Register Datasets 5x5 Refined Lee Filter Multilook to reduce speckle 2:2 Multilook Import and Prepare Data RISAT-1 (HybridPol) RADARSAT-2 (Full Pol) Sensors Band Polarization Pixel Size (m) RISAT-1 C Hybrid Pol 2.5 RADARSAT- 2 C Full Pol 8.0 14-Jun-13 37
  • 38. Variation of H/AlphaMean and Standard Deviation of σ0 • Urban features: hybrid σ0 shows higher value with more standard deviation • Urban features can be clearly discriminated • The other features are not discriminable – low dynamic range (within 3dB) • Channel Imbalance observed 14-Jun-13 38
  • 39. Linear Pol FCC RGB Hybrid Pol FCC RGB LinearComplexLinearIntensity HybridComplexHybridIntensity Urban Water Forest Mangroves Wetland Field Work 14-Jun-13 39
  • 40. Polarization /Class Linear(HH,HV) Intensity Circular(RH,RV) Intensity Linear(HH, HV) Complex Circular(RH,RV) Complex Urban 70.77 85.27 70.68 82.50 Forest 73.50 52.95 78.85 87.41 Water 95.19 99.45 94.99 99.83 Mangroves 80.09 59.78 82.88 91.74 Wetland 62.57 91.55 73.11 98.24 Accuracy (%) 83.48 79.60 86.38 91.79 14-Jun-13 40
  • 43. Saoner Test Site • Scene center: • 21 23′09″N Latitude • 78 55′12″E Longitude • Major Crops: • Paddy • Sugarcane • Wheat • Gram 14-Jun-13 43