SlideShare una empresa de Scribd logo
1 de 10
OSGEO-India: FOSS4G 2012- First National Conference "Open Source Geospatial Resources
     to Spearhead Development and Growth” 25-27th October 2012, @ IIIT Hyderabad




     Object Based Image Analysis
          Tools for Opticks
                    Mohit Kumar, KS Rajan, Dustan Adkins




                                http://osgeo.in/foss4g2012                              1
OPTICKS ?
    •   Opticks is an open source, remote sensing application that supports imagery,
        video (motion imagery), Synthetic Aperture Radar (SAR), multi-spectral, hyper-
        spectral, and other types of remote sensing data.
    •   Opticks can also be used as a remote sensing software development framework.
        Developers can extend Opticks functionality using its plug-in architecture and
        public application programming interface
    •   http://opticks.org


                     Why object-based?
•   Object based approach is better than conventional per-pixel analysis as it deals
    with considerably reduced number of units. This approach is not that much
    sensitive to noise and hence is spatially consistent.




                                     http://osgeo.in/foss4g2012                          2
Workflow




http://osgeo.in/foss4g2012   3
Image Segmentation (Meanshift)
                                                            Input image
                                                           (CIELAB colour          5 Dimensional
 Input image(RGB)                                                                  feature space
                                                               space)


                                             sing           Modes(local
                                      o lla p
                               space c ect.                                          Clustering
                         ature      ob j
                                                             maximas)
                    in fe form an
              i n ts o d e
           Po
                    em
            to o n


 Objects formed
                                                            Pruning (spatial     Pruning (Spectral
(backtracking the
                                                              Bandwidth)            Bandwidth)
     modes)




                                                                                 Pruning ( Minimum
                                                                                    region area)

                                                    http://osgeo.in/foss4g2012                       4
Object attribution
• Calculating textural, geometric and spectral features for the objects
  made in the Segmentation step in a feature vector.
• Area, Perimeter, Roundness, Compactness, Centroid, Contrast,
  Coarseness, Direction, Roughness, Mean red, Mean green, Mean
  blue, std. deviation Red, std. deviation Green, std. deviation Blue.


     Segmented Image                            For every object in the image



                                                Initialize a vector having all 16
                                                             features


                                               Calculate value for every feature
                                                    and save in the vector.

                            http://osgeo.in/foss4g2012                              5
Classification
• Mahalanobis Distance
• Di,j2 = (x-µj)`S-1(x-µj)
• The class which has the least Mahalanobis
  distance to the object i is the class of that object.

                      Vectorization
 • Creates vector polygons for all connected regions of pixels in the
   object image sharing a common pixel value.
 • Polygon features are created on the output layer, with polygon
   geometries representing the polygons.
 • The class which has the least Mahalanobis distance to the object i is
   the class of that object.


                          http://osgeo.in/foss4g2012                 6
The Input Orbview3 (4m) data of a part of delhi (500X500)                    The output of the objects with area less than 100.




                                                                               The shapefile(.shp) displaying the objects
Output of object having area 100-200 and classified as building.


                                                      http://osgeo.in/foss4g2012                                                   7
Performance Analysis

 Image Size         Number of          Running time
                     objects              (sec)
  512 X 512           153                  3.1

 1024 X 1024            435                  24.55


                Table 1 : Image segmentation


Image Size       Number of objects       Running time
                                            (sec)
 256 X 256               49                 0.249
 512 X 512               193                    1.133
1024 X1024               752                    5.359
2048 X 2048             2965                    31.60
4096 X 4096            11922                    286.59

              Table 2 : Object Attribution



                   http://osgeo.in/foss4g2012            8
Source Code
• https://github.com/mohitkharb/Opticks_GSO
  C2012
• http://opticks.org/confluence/display/~mohit
  kharb/Workflow+of+the+pluggin
• http://code.google.com/soc/




                  http://osgeo.in/foss4g2012     9
Any Questions?




  http://osgeo.in/foss4g2012   10

Más contenido relacionado

La actualidad más candente

Chap. 10 computational photography
Chap. 10 computational photographyChap. 10 computational photography
Chap. 10 computational photographyduckleek
 
Edge detection using evolutionary algorithms new
Edge detection using evolutionary algorithms newEdge detection using evolutionary algorithms new
Edge detection using evolutionary algorithms newPriyanka Sharma
 
Comparison between Blur Transfer and Blur Re-Generation in Depth Image Based ...
Comparison between Blur Transfer and Blur Re-Generation in Depth Image Based ...Comparison between Blur Transfer and Blur Re-Generation in Depth Image Based ...
Comparison between Blur Transfer and Blur Re-Generation in Depth Image Based ...Norishige Fukushima
 
CS 354 Acceleration Structures
CS 354 Acceleration StructuresCS 354 Acceleration Structures
CS 354 Acceleration StructuresMark Kilgard
 
Yonsei Data Science Lab - Computer Vision
Yonsei Data Science Lab - Computer VisionYonsei Data Science Lab - Computer Vision
Yonsei Data Science Lab - Computer VisionDataScienceLab
 
Feature based ghost removal in high dynamic range imaging
Feature based ghost removal in high dynamic range imagingFeature based ghost removal in high dynamic range imaging
Feature based ghost removal in high dynamic range imagingijcga
 
Fuzzy Logic Based Edge Detection
Fuzzy Logic Based Edge DetectionFuzzy Logic Based Edge Detection
Fuzzy Logic Based Edge DetectionDawn Raider Gupta
 
Real-time Shadowing Techniques: Shadow Volumes
Real-time Shadowing Techniques: Shadow VolumesReal-time Shadowing Techniques: Shadow Volumes
Real-time Shadowing Techniques: Shadow VolumesMark Kilgard
 
image segmentation
image segmentationimage segmentation
image segmentationarpanmankar
 
Shiny Pixels and Beyond: Real-Time Raytracing at SEED
Shiny Pixels and Beyond: Real-Time Raytracing at SEEDShiny Pixels and Beyond: Real-Time Raytracing at SEED
Shiny Pixels and Beyond: Real-Time Raytracing at SEEDElectronic Arts / DICE
 
Seminar report on edge detection of video using matlab code
Seminar report on edge detection of video using matlab codeSeminar report on edge detection of video using matlab code
Seminar report on edge detection of video using matlab codeBhushan Deore
 
03 digital image fundamentals DIP
03 digital image fundamentals DIP03 digital image fundamentals DIP
03 digital image fundamentals DIPbabak danyal
 
ALGORITHM AND TECHNIQUE ON VARIOUS EDGE DETECTION: A SURVEY
ALGORITHM AND TECHNIQUE ON VARIOUS EDGE DETECTION: A SURVEYALGORITHM AND TECHNIQUE ON VARIOUS EDGE DETECTION: A SURVEY
ALGORITHM AND TECHNIQUE ON VARIOUS EDGE DETECTION: A SURVEYsipij
 

La actualidad más candente (20)

Chap. 10 computational photography
Chap. 10 computational photographyChap. 10 computational photography
Chap. 10 computational photography
 
Edge Detection
Edge Detection Edge Detection
Edge Detection
 
Edge detection using evolutionary algorithms new
Edge detection using evolutionary algorithms newEdge detection using evolutionary algorithms new
Edge detection using evolutionary algorithms new
 
Computer vision
Computer visionComputer vision
Computer vision
 
[DL輪読会]ClearGrasp
[DL輪読会]ClearGrasp[DL輪読会]ClearGrasp
[DL輪読会]ClearGrasp
 
Comparison between Blur Transfer and Blur Re-Generation in Depth Image Based ...
Comparison between Blur Transfer and Blur Re-Generation in Depth Image Based ...Comparison between Blur Transfer and Blur Re-Generation in Depth Image Based ...
Comparison between Blur Transfer and Blur Re-Generation in Depth Image Based ...
 
CS 354 Acceleration Structures
CS 354 Acceleration StructuresCS 354 Acceleration Structures
CS 354 Acceleration Structures
 
Thesis presentation
Thesis presentationThesis presentation
Thesis presentation
 
Yonsei Data Science Lab - Computer Vision
Yonsei Data Science Lab - Computer VisionYonsei Data Science Lab - Computer Vision
Yonsei Data Science Lab - Computer Vision
 
Feature based ghost removal in high dynamic range imaging
Feature based ghost removal in high dynamic range imagingFeature based ghost removal in high dynamic range imaging
Feature based ghost removal in high dynamic range imaging
 
Canny edge detection
Canny edge detectionCanny edge detection
Canny edge detection
 
Fuzzy Logic Based Edge Detection
Fuzzy Logic Based Edge DetectionFuzzy Logic Based Edge Detection
Fuzzy Logic Based Edge Detection
 
Final Review
Final ReviewFinal Review
Final Review
 
Real-time Shadowing Techniques: Shadow Volumes
Real-time Shadowing Techniques: Shadow VolumesReal-time Shadowing Techniques: Shadow Volumes
Real-time Shadowing Techniques: Shadow Volumes
 
image segmentation
image segmentationimage segmentation
image segmentation
 
Shiny Pixels and Beyond: Real-Time Raytracing at SEED
Shiny Pixels and Beyond: Real-Time Raytracing at SEEDShiny Pixels and Beyond: Real-Time Raytracing at SEED
Shiny Pixels and Beyond: Real-Time Raytracing at SEED
 
Background subtraction
Background subtractionBackground subtraction
Background subtraction
 
Seminar report on edge detection of video using matlab code
Seminar report on edge detection of video using matlab codeSeminar report on edge detection of video using matlab code
Seminar report on edge detection of video using matlab code
 
03 digital image fundamentals DIP
03 digital image fundamentals DIP03 digital image fundamentals DIP
03 digital image fundamentals DIP
 
ALGORITHM AND TECHNIQUE ON VARIOUS EDGE DETECTION: A SURVEY
ALGORITHM AND TECHNIQUE ON VARIOUS EDGE DETECTION: A SURVEYALGORITHM AND TECHNIQUE ON VARIOUS EDGE DETECTION: A SURVEY
ALGORITHM AND TECHNIQUE ON VARIOUS EDGE DETECTION: A SURVEY
 

Destacado

ისტ ისგამოყენება
ისტ ისგამოყენებაისტ ისგამოყენება
ისტ ისგამოყენებაatojanraisa
 
Protagonistes de la 19a passejada
Protagonistes de la 19a passejadaProtagonistes de la 19a passejada
Protagonistes de la 19a passejadaAnimals del Maresme
 
2014年 忘年会 歳出
2014年 忘年会 歳出2014年 忘年会 歳出
2014年 忘年会 歳出Tatsuya Suzuki
 
ისტ ისგამოყენება
ისტ ისგამოყენებაისტ ისგამოყენება
ისტ ისგამოყენებაatojanraisa
 
ACAAD Maresme - Balanç 2013-2014
ACAAD Maresme - Balanç 2013-2014ACAAD Maresme - Balanç 2013-2014
ACAAD Maresme - Balanç 2013-2014Animals del Maresme
 
Inversions i activitats 2013-2015
Inversions i activitats 2013-2015Inversions i activitats 2013-2015
Inversions i activitats 2013-2015Animals del Maresme
 
โครงการระบบดูแล ปี 55
โครงการระบบดูแล  ปี 55โครงการระบบดูแล  ปี 55
โครงการระบบดูแล ปี 55somchaitumdee50
 
โครงการส่งเสริมคุณธรรมจริยธรรม ค่านิยมที่พึงประสงค์.54
โครงการส่งเสริมคุณธรรมจริยธรรม ค่านิยมที่พึงประสงค์.54โครงการส่งเสริมคุณธรรมจริยธรรม ค่านิยมที่พึงประสงค์.54
โครงการส่งเสริมคุณธรรมจริยธรรม ค่านิยมที่พึงประสงค์.54somchaitumdee50
 
โครงการระบบดูแล ปี 55
โครงการระบบดูแล  ปี 55โครงการระบบดูแล  ปี 55
โครงการระบบดูแล ปี 55somchaitumdee50
 
คำสั่งของโรงเรียอาจารย์ที่ปรึกษา.Doc ปี 2555
คำสั่งของโรงเรียอาจารย์ที่ปรึกษา.Doc ปี 2555คำสั่งของโรงเรียอาจารย์ที่ปรึกษา.Doc ปี 2555
คำสั่งของโรงเรียอาจารย์ที่ปรึกษา.Doc ปี 2555somchaitumdee50
 
Els protagonistes de la 21a passejada
Els protagonistes de la 21a passejadaEls protagonistes de la 21a passejada
Els protagonistes de la 21a passejadaAnimals del Maresme
 
แผน พล ม. 1 ภาค 1 ปี 54 ปิงปอง
แผน พล ม. 1 ภาค  1 ปี 54  ปิงปองแผน พล ม. 1 ภาค  1 ปี 54  ปิงปอง
แผน พล ม. 1 ภาค 1 ปี 54 ปิงปองsomchaitumdee50
 

Destacado (18)

Revista Adopta'm
Revista Adopta'mRevista Adopta'm
Revista Adopta'm
 
Hivern 2012 - 2013
Hivern 2012 - 2013Hivern 2012 - 2013
Hivern 2012 - 2013
 
ისტ ისგამოყენება
ისტ ისგამოყენებაისტ ისგამოყენება
ისტ ისგამოყენება
 
Revista juny 2013
Revista juny 2013Revista juny 2013
Revista juny 2013
 
Protagonistes de la 19a passejada
Protagonistes de la 19a passejadaProtagonistes de la 19a passejada
Protagonistes de la 19a passejada
 
Animaladda 2012
Animaladda 2012Animaladda 2012
Animaladda 2012
 
2014年 忘年会 歳出
2014年 忘年会 歳出2014年 忘年会 歳出
2014年 忘年会 歳出
 
Revista febrer 2013
Revista febrer 2013Revista febrer 2013
Revista febrer 2013
 
ისტ ისგამოყენება
ისტ ისგამოყენებაისტ ისგამოყენება
ისტ ისგამოყენება
 
ACAAD Maresme - Balanç 2013-2014
ACAAD Maresme - Balanç 2013-2014ACAAD Maresme - Balanç 2013-2014
ACAAD Maresme - Balanç 2013-2014
 
Presentació Animaladda 2014
Presentació Animaladda 2014Presentació Animaladda 2014
Presentació Animaladda 2014
 
Inversions i activitats 2013-2015
Inversions i activitats 2013-2015Inversions i activitats 2013-2015
Inversions i activitats 2013-2015
 
โครงการระบบดูแล ปี 55
โครงการระบบดูแล  ปี 55โครงการระบบดูแล  ปี 55
โครงการระบบดูแล ปี 55
 
โครงการส่งเสริมคุณธรรมจริยธรรม ค่านิยมที่พึงประสงค์.54
โครงการส่งเสริมคุณธรรมจริยธรรม ค่านิยมที่พึงประสงค์.54โครงการส่งเสริมคุณธรรมจริยธรรม ค่านิยมที่พึงประสงค์.54
โครงการส่งเสริมคุณธรรมจริยธรรม ค่านิยมที่พึงประสงค์.54
 
โครงการระบบดูแล ปี 55
โครงการระบบดูแล  ปี 55โครงการระบบดูแล  ปี 55
โครงการระบบดูแล ปี 55
 
คำสั่งของโรงเรียอาจารย์ที่ปรึกษา.Doc ปี 2555
คำสั่งของโรงเรียอาจารย์ที่ปรึกษา.Doc ปี 2555คำสั่งของโรงเรียอาจารย์ที่ปรึกษา.Doc ปี 2555
คำสั่งของโรงเรียอาจารย์ที่ปรึกษา.Doc ปี 2555
 
Els protagonistes de la 21a passejada
Els protagonistes de la 21a passejadaEls protagonistes de la 21a passejada
Els protagonistes de la 21a passejada
 
แผน พล ม. 1 ภาค 1 ปี 54 ปิงปอง
แผน พล ม. 1 ภาค  1 ปี 54  ปิงปองแผน พล ม. 1 ภาค  1 ปี 54  ปิงปอง
แผน พล ม. 1 ภาค 1 ปี 54 ปิงปอง
 

Similar a Object based image analysis tools for opticks

Image Restoration for 3D Computer Vision
Image Restoration for 3D Computer VisionImage Restoration for 3D Computer Vision
Image Restoration for 3D Computer VisionPetteriTeikariPhD
 
Computer Vision: Visual Extent of an Object
Computer Vision: Visual Extent of an ObjectComputer Vision: Visual Extent of an Object
Computer Vision: Visual Extent of an ObjectIOSR Journals
 
Currency recognition on mobile phones
Currency recognition on mobile phonesCurrency recognition on mobile phones
Currency recognition on mobile phoneshabeebsab
 
Visibility Optimization for Games
Visibility Optimization for GamesVisibility Optimization for Games
Visibility Optimization for GamesUmbra
 
Visibility Optimization for Games
Visibility Optimization for GamesVisibility Optimization for Games
Visibility Optimization for GamesSampo Lappalainen
 
Object detection with deep learning
Object detection with deep learningObject detection with deep learning
Object detection with deep learningSushant Shrivastava
 
Computer vision series
Computer vision seriesComputer vision series
Computer vision seriesPerry Lea
 
DEEP LEARNING TECHNIQUES POWER POINT PRESENTATION
DEEP LEARNING TECHNIQUES POWER POINT PRESENTATIONDEEP LEARNING TECHNIQUES POWER POINT PRESENTATION
DEEP LEARNING TECHNIQUES POWER POINT PRESENTATIONSelvaLakshmi63
 
Project Matsu: Elastic Clouds for Disaster Relief
Project Matsu: Elastic Clouds for Disaster ReliefProject Matsu: Elastic Clouds for Disaster Relief
Project Matsu: Elastic Clouds for Disaster ReliefRobert Grossman
 
MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)
MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)
MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)npinto
 
A Brief History of Object Detection / Tommi Kerola
A Brief History of Object Detection / Tommi KerolaA Brief History of Object Detection / Tommi Kerola
A Brief History of Object Detection / Tommi KerolaPreferred Networks
 
Object Based Image Analysis
Object Based Image Analysis Object Based Image Analysis
Object Based Image Analysis Kabir Uddin
 
OBJECT DETECTION AND RECOGNITION: A SURVEY
OBJECT DETECTION AND RECOGNITION: A SURVEYOBJECT DETECTION AND RECOGNITION: A SURVEY
OBJECT DETECTION AND RECOGNITION: A SURVEYJournal For Research
 
IISc Internship Report
IISc Internship ReportIISc Internship Report
IISc Internship ReportHarshilJain26
 
Action Genome: Action As Composition of Spatio Temporal Scene Graphs
Action Genome: Action As Composition of Spatio Temporal Scene GraphsAction Genome: Action As Composition of Spatio Temporal Scene Graphs
Action Genome: Action As Composition of Spatio Temporal Scene GraphsSangmin Woo
 
Deferred Pixel Shading on the PLAYSTATION®3
Deferred Pixel Shading on the PLAYSTATION®3Deferred Pixel Shading on the PLAYSTATION®3
Deferred Pixel Shading on the PLAYSTATION®3Slide_N
 

Similar a Object based image analysis tools for opticks (20)

Image Restoration for 3D Computer Vision
Image Restoration for 3D Computer VisionImage Restoration for 3D Computer Vision
Image Restoration for 3D Computer Vision
 
Computer Vision: Visual Extent of an Object
Computer Vision: Visual Extent of an ObjectComputer Vision: Visual Extent of an Object
Computer Vision: Visual Extent of an Object
 
conv_nets.pptx
conv_nets.pptxconv_nets.pptx
conv_nets.pptx
 
Currency recognition on mobile phones
Currency recognition on mobile phonesCurrency recognition on mobile phones
Currency recognition on mobile phones
 
Visibility Optimization for Games
Visibility Optimization for GamesVisibility Optimization for Games
Visibility Optimization for Games
 
Visibility Optimization for Games
Visibility Optimization for GamesVisibility Optimization for Games
Visibility Optimization for Games
 
Object detection with deep learning
Object detection with deep learningObject detection with deep learning
Object detection with deep learning
 
Computer vision series
Computer vision seriesComputer vision series
Computer vision series
 
DEEP LEARNING TECHNIQUES POWER POINT PRESENTATION
DEEP LEARNING TECHNIQUES POWER POINT PRESENTATIONDEEP LEARNING TECHNIQUES POWER POINT PRESENTATION
DEEP LEARNING TECHNIQUES POWER POINT PRESENTATION
 
N046047780
N046047780N046047780
N046047780
 
Project Matsu: Elastic Clouds for Disaster Relief
Project Matsu: Elastic Clouds for Disaster ReliefProject Matsu: Elastic Clouds for Disaster Relief
Project Matsu: Elastic Clouds for Disaster Relief
 
MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)
MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)
MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)
 
A Brief History of Object Detection / Tommi Kerola
A Brief History of Object Detection / Tommi KerolaA Brief History of Object Detection / Tommi Kerola
A Brief History of Object Detection / Tommi Kerola
 
Poster cs543
Poster cs543Poster cs543
Poster cs543
 
Object Based Image Analysis
Object Based Image Analysis Object Based Image Analysis
Object Based Image Analysis
 
OBJECT DETECTION AND RECOGNITION: A SURVEY
OBJECT DETECTION AND RECOGNITION: A SURVEYOBJECT DETECTION AND RECOGNITION: A SURVEY
OBJECT DETECTION AND RECOGNITION: A SURVEY
 
IISc Internship Report
IISc Internship ReportIISc Internship Report
IISc Internship Report
 
ICRA 2015 Spotlight
ICRA 2015 SpotlightICRA 2015 Spotlight
ICRA 2015 Spotlight
 
Action Genome: Action As Composition of Spatio Temporal Scene Graphs
Action Genome: Action As Composition of Spatio Temporal Scene GraphsAction Genome: Action As Composition of Spatio Temporal Scene Graphs
Action Genome: Action As Composition of Spatio Temporal Scene Graphs
 
Deferred Pixel Shading on the PLAYSTATION®3
Deferred Pixel Shading on the PLAYSTATION®3Deferred Pixel Shading on the PLAYSTATION®3
Deferred Pixel Shading on the PLAYSTATION®3
 

Último

Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptxQ4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptxlancelewisportillo
 
Keynote by Prof. Wurzer at Nordex about IP-design
Keynote by Prof. Wurzer at Nordex about IP-designKeynote by Prof. Wurzer at Nordex about IP-design
Keynote by Prof. Wurzer at Nordex about IP-designMIPLM
 
Earth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatEarth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatYousafMalik24
 
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfInclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfTechSoup
 
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATIONTHEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATIONHumphrey A Beña
 
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxMULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxAnupkumar Sharma
 
Daily Lesson Plan in Mathematics Quarter 4
Daily Lesson Plan in Mathematics Quarter 4Daily Lesson Plan in Mathematics Quarter 4
Daily Lesson Plan in Mathematics Quarter 4JOYLYNSAMANIEGO
 
Global Lehigh Strategic Initiatives (without descriptions)
Global Lehigh Strategic Initiatives (without descriptions)Global Lehigh Strategic Initiatives (without descriptions)
Global Lehigh Strategic Initiatives (without descriptions)cama23
 
Concurrency Control in Database Management system
Concurrency Control in Database Management systemConcurrency Control in Database Management system
Concurrency Control in Database Management systemChristalin Nelson
 
Music 9 - 4th quarter - Vocal Music of the Romantic Period.pptx
Music 9 - 4th quarter - Vocal Music of the Romantic Period.pptxMusic 9 - 4th quarter - Vocal Music of the Romantic Period.pptx
Music 9 - 4th quarter - Vocal Music of the Romantic Period.pptxleah joy valeriano
 
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTSGRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTSJoshuaGantuangco2
 
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptxECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptxiammrhaywood
 
Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Mark Reed
 
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfVirtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfErwinPantujan2
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Celine George
 
4.16.24 21st Century Movements for Black Lives.pptx
4.16.24 21st Century Movements for Black Lives.pptx4.16.24 21st Century Movements for Black Lives.pptx
4.16.24 21st Century Movements for Black Lives.pptxmary850239
 
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...Nguyen Thanh Tu Collection
 
Transaction Management in Database Management System
Transaction Management in Database Management SystemTransaction Management in Database Management System
Transaction Management in Database Management SystemChristalin Nelson
 

Último (20)

Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptxQ4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
 
Keynote by Prof. Wurzer at Nordex about IP-design
Keynote by Prof. Wurzer at Nordex about IP-designKeynote by Prof. Wurzer at Nordex about IP-design
Keynote by Prof. Wurzer at Nordex about IP-design
 
Earth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatEarth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice great
 
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfInclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
 
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATIONTHEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
 
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxMULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
 
Daily Lesson Plan in Mathematics Quarter 4
Daily Lesson Plan in Mathematics Quarter 4Daily Lesson Plan in Mathematics Quarter 4
Daily Lesson Plan in Mathematics Quarter 4
 
Global Lehigh Strategic Initiatives (without descriptions)
Global Lehigh Strategic Initiatives (without descriptions)Global Lehigh Strategic Initiatives (without descriptions)
Global Lehigh Strategic Initiatives (without descriptions)
 
Concurrency Control in Database Management system
Concurrency Control in Database Management systemConcurrency Control in Database Management system
Concurrency Control in Database Management system
 
Music 9 - 4th quarter - Vocal Music of the Romantic Period.pptx
Music 9 - 4th quarter - Vocal Music of the Romantic Period.pptxMusic 9 - 4th quarter - Vocal Music of the Romantic Period.pptx
Music 9 - 4th quarter - Vocal Music of the Romantic Period.pptx
 
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptxFINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
 
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTSGRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
 
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptxECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
 
Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)
 
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfVirtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
 
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptxYOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
 
4.16.24 21st Century Movements for Black Lives.pptx
4.16.24 21st Century Movements for Black Lives.pptx4.16.24 21st Century Movements for Black Lives.pptx
4.16.24 21st Century Movements for Black Lives.pptx
 
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
 
Transaction Management in Database Management System
Transaction Management in Database Management SystemTransaction Management in Database Management System
Transaction Management in Database Management System
 

Object based image analysis tools for opticks

  • 1. OSGEO-India: FOSS4G 2012- First National Conference "Open Source Geospatial Resources to Spearhead Development and Growth” 25-27th October 2012, @ IIIT Hyderabad Object Based Image Analysis Tools for Opticks Mohit Kumar, KS Rajan, Dustan Adkins http://osgeo.in/foss4g2012 1
  • 2. OPTICKS ? • Opticks is an open source, remote sensing application that supports imagery, video (motion imagery), Synthetic Aperture Radar (SAR), multi-spectral, hyper- spectral, and other types of remote sensing data. • Opticks can also be used as a remote sensing software development framework. Developers can extend Opticks functionality using its plug-in architecture and public application programming interface • http://opticks.org Why object-based? • Object based approach is better than conventional per-pixel analysis as it deals with considerably reduced number of units. This approach is not that much sensitive to noise and hence is spatially consistent. http://osgeo.in/foss4g2012 2
  • 4. Image Segmentation (Meanshift) Input image (CIELAB colour 5 Dimensional Input image(RGB) feature space space) sing Modes(local o lla p space c ect. Clustering ature ob j maximas) in fe form an i n ts o d e Po em to o n Objects formed Pruning (spatial Pruning (Spectral (backtracking the Bandwidth) Bandwidth) modes) Pruning ( Minimum region area) http://osgeo.in/foss4g2012 4
  • 5. Object attribution • Calculating textural, geometric and spectral features for the objects made in the Segmentation step in a feature vector. • Area, Perimeter, Roundness, Compactness, Centroid, Contrast, Coarseness, Direction, Roughness, Mean red, Mean green, Mean blue, std. deviation Red, std. deviation Green, std. deviation Blue. Segmented Image For every object in the image Initialize a vector having all 16 features Calculate value for every feature and save in the vector. http://osgeo.in/foss4g2012 5
  • 6. Classification • Mahalanobis Distance • Di,j2 = (x-µj)`S-1(x-µj) • The class which has the least Mahalanobis distance to the object i is the class of that object. Vectorization • Creates vector polygons for all connected regions of pixels in the object image sharing a common pixel value. • Polygon features are created on the output layer, with polygon geometries representing the polygons. • The class which has the least Mahalanobis distance to the object i is the class of that object. http://osgeo.in/foss4g2012 6
  • 7. The Input Orbview3 (4m) data of a part of delhi (500X500) The output of the objects with area less than 100. The shapefile(.shp) displaying the objects Output of object having area 100-200 and classified as building. http://osgeo.in/foss4g2012 7
  • 8. Performance Analysis Image Size Number of Running time objects (sec) 512 X 512 153 3.1 1024 X 1024 435 24.55 Table 1 : Image segmentation Image Size Number of objects Running time (sec) 256 X 256 49 0.249 512 X 512 193 1.133 1024 X1024 752 5.359 2048 X 2048 2965 31.60 4096 X 4096 11922 286.59 Table 2 : Object Attribution http://osgeo.in/foss4g2012 8
  • 9. Source Code • https://github.com/mohitkharb/Opticks_GSO C2012 • http://opticks.org/confluence/display/~mohit kharb/Workflow+of+the+pluggin • http://code.google.com/soc/ http://osgeo.in/foss4g2012 9
  • 10. Any Questions? http://osgeo.in/foss4g2012 10

Notas del editor

  1. This paper describes a tool that implements Feature/Object Based This Image analysis for the Opticks remote sensing and image analysis software platform. These tools will partition remote sensing (RS) imagery into meaningful image-objects, and assess their characteristics through spatial, spectral and temporal scale. OSGEO-India: FOSS4G 2012- First National Conference "OPEN SOURCE GEOSPATIAL RESOURCES TO SPEARHEAD DEVELOPMENT AND GROWTH” 25-27TH OCTOBER 2012, @ IIIT HYDERABAD
  2. OSGEO-India: FOSS4G 2012- First National Conference "OPEN SOURCE GEOSPATIAL RESOURCES TO SPEARHEAD DEVELOPMENT AND GROWTH” 25-27TH OCTOBER 2012, @ IIIT HYDERABAD