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A_Framework_of_Ontology-Based_Knowledge_Information_Processing_for_Change_Detection_in_Remote_Sensing_Data.ppt

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  • 1. A Framework of Ontology-Based Knowledge Information Processing for Change Detection in Remote Sensing Data Shutaro Hashimoto 1 , Takeo Tadono 1,2 , Masahiko Onosato 1 , Masahiro Hori 1,2 , and Takashi Moriyama 1,2 1 Graduate School of Information Science and Technology, Hokkaido University 2 Earth Observation Research Center, Japan Aerospace Exploration Agency July 28, 2011 IGARSS 2011 TH4.T09.2
  • 2. Background
    • Needs for automatic image interpretation
      • especially change detection
      • to handle large amount of data
    • Humanlike interpretation requires:
      • high cognitive ability
      • versatility
    July 28, 2011 IGARSS 2011 TH4.T09.2 Mudslides Floods ?
  • 3. Solution
    • Emulating manual interpretation using knowledge information processing
    • We propose a framework for change detection
      • using ontology-based knowledge to recognize and understand targets
      • input data: optical multispectral data
    July 28, 2011 IGARSS 2011 TH4.T09.2 Knowledge Mudslide
  • 4. Framework for Change Detection July 28, 2011 IGARSS 2011 TH4.T09.2 Day 1 Satellite Data Inference Results Day 2 Auxiliary Data e.g. DSM Bayesian Network Query for Target e.g. “mudslide” Information Extraction Inference Analysis of Target Pixel-Based/ Object-Based Image Analysis Bayesian Inference Evidences Synthesis of Knowledge Knowledge Based on Ontology
  • 5. Requirements for Knowledge Representation July 28, 2011 IGARSS 2011 TH4.T09.2 “ Vegetation has high NDVI values” “ Roads are long and narrow” “ Buildings are usually located along Road ” “ Artificial Forests are often located along River ” “ Mountains are often covered by Forest ”
    • Knowledge representation requires:
    • uncertainty
    • modularity and scalability
    • implicit structural definition of concepts
    Knowledge Based on Ontology
  • 6. Remote Sensing Ontology
    • Heavyweight
    • ontology in
    • remote sensing
      • 420 concepts
    • Definition Structures
    • Inheritance (B is-a A)
    • Slot (B part-of / attribute-of A)
    July 28, 2011 IGARSS 2011 TH4.T09.2 A B soil p/o 1.. leaf chlorophyll component substance Any water chlorophyll a/o 1 p/o 1.. density density cluster Any component river field slope continuant entity p/o 1.. a/o 1 geographical object component structure structural attr. Any contextual change p/o 1.. subevent change event p/o 0.. p/o 0.. Any before Any after superficial change p/o 1.. component Any geographical feature p/o 1.. soil layer soil component p/o 1.. water layer water component wood p/o 1.. trunk wood component p/o 1.. p/o 1.. tree leaf component trunk component sea mountain occurrent p/o 1 soil appearance soil layer after water appearance p/o 1 water layer after a/o 1 p/o 1 location slope mudslide subevent soil appearance substrate p/o 1.. forest tree component change event Main Categories p/o 1 Slot 1 B Slot 2 C a/o 1 A
  • 7. Knowledge Based on Ontology
    • Describing relations among some concepts
    • Using Bayesian probability to express uncertainty
    July 28, 2011 IGARSS 2011 TH4.T09.2 (1) Concept-Slot Relation (2) Concept-Evidence Relation (3) Co-Occurrence 2 Concepts 3 Concepts B C A
  • 8. Analysis of Target & Synthesis of Knowledge (2) July 28, 2011 IGARSS 2011 TH4.T09.2 Bayesian Network (1) p/o 0 p/o 1 soil appearance mudslide soil layer before a/o 1 location slope p/o 1 p/o 1 soil layer after p/o 1 subevent soil component soil component Ontology slope angle slope soil layer soil soil layer soil appearance soil layer soil appearance mudslide slope hue soil saturation soil value soil NDVI soil Knowledge Day 2 soil appearance mudslide slope angle slope Day 1 Auxiliary Data soil layer soil hue value NDVI saturation soil layer soil hue value NDVI saturation (3)
  • 9. Change Detection July 28, 2011 IGARSS 2011 TH4.T09.2 Soil Layer Image Object Soil Hue Value Saturation NDVI Satellite Image Soil Layer Soil Appearance Day 2 Day 1 Calculate posterior probability of target using Bayesian network Inference of Substance Inference of Object Inference of Change Day 2 soil appearance mudslide slope angle slope Day 1 Auxiliary Data soil layer soil hue value NDVI saturation soil layer soil hue value NDVI saturation
  • 10. Experiment
    • To validate cognitive ability & versatility
    • Applying to three cases of practical change detection without tuning
    • Bi-temporal data
      • observed by AVNIR-2 onboard ALOS
        • 3 visible + 1 near-infrared
        • 10 m spatial resolution
      • applied image registration with geometric errors of less than 0.5 pixel
    July 28, 2011 IGARSS 2011 TH4.T09.2
  • 11. Case 1: Detection of Mudslides in Yamaguchi City, Japan July 28, 2011 IGARSS 2011 TH4.T09.2 Day 1 (14 June, 2009) Day 2 (30 July, 2009) ©JAXA ©JAXA Mudslides caused by heavy rain in 19-26 July, 2009
  • 12. Case 1: Detection of Mudslides in Yamaguchi City, Japan - Inference Results - July 28, 2011 IGARSS 2011 TH4.T09.2 Day 1 (14 June, 2009) Day 2 (30 July, 2009) ©JAXA ©JAXA soil on day 1 soil appearance mudslide slope Definition of mudslide soil on day 2 p/o 0 p/o 1 soil appearance mudslide soil layer before a/o 1 location slope p/o 1 p/o 1 soil layer after p/o 1 subevent soil component soil component
  • 13. Case 1: Detection of Mudslides in Yamaguchi City, Japan - Comparison with Human’s Result - July 28, 2011 IGARSS 2011 TH4.T09.2 small changes => more sensitive than human’s result changes in the flat area => our definition of mudslide doesn’t include changes in flat area
  • 14. Case 1: Mudslide Detection in Yamaguchi City, Japan - Comparison with Survey Data - July 28, 2011 IGARSS 2011 TH4.T09.2 Mudslides in Our Result Collapsed Slopes Mudflow Traces Debris in Survey Data (investigated by Yamaguchi Pref.)
  • 15. Case 2: Detection of Flooded Areas in Myanmar July 28, 2011 IGARSS 2011 TH4.T09.2 ©JAXA ©JAXA Day 1 (4 May, 2008) Day 2 ( 19 June, 2008 ) Water (Day 1) Water Disappearance Water (Day 2) Floods caused by Cyclone in 2-3 May, 2008
  • 16. Case 2: Detection of Flooded Areas in Myanmar - Comparison with Human’s Result - July 28, 2011 IGARSS 2011 TH4.T09.2 Our result not correctly detected due to the existence of clouds Human’s result misdetected edges of clouds
  • 17. Case 3: Detection of Flooded Areas in Pakistan July 28, 2011 IGARSS 2011 TH4.T09.2 ©JAXA ©JAXA Day 1 (14 Oct., 2009) Day 2 (1 Sept., 2010) Water (Day 1) Water Appearance Water (Day 2) Floods caused by heavy rain since late July, 2010
  • 18. Discussion
    • About 90% accuracy in mudslide detection
    • Our results were better than human’s results
      • due to using knowledge specialized on targets
    • Fairly good results in all cases without tuning
        • due to analyzing essential characteristics of each targets using heavyweight ontology
    • Possible to understand and recognize targets as humans do using rich ontology-based knowledge
    July 28, 2011 IGARSS 2011 TH4.T09.2
  • 19. Conclusions
    • We proposed a framework for change detection
      • using ontology-based knowledge to recognize and understand targets
    • The experiment showed:
      • accuracy was about 90 % in mudslide detection
      • results were better than human’s results without tuning
    • More improvements are ongoing
      • to extract various information from data, such as spatial information
      • to describe more expressive knowledge
    July 28, 2011 IGARSS 2011 TH4.T09.2
  • 20.
    • Thank you!
    July 28, 2011 IGARSS 2011 TH4.T09.2
  • 21. Probabilistic Inference with Bayesian Network July 28, 2011 IGARSS 2011 TH4.T09.2 Node: random variable Arc: probabilistic relation ・・・ (1) ・・・ (3) ・・・ (2)
  • 22. July 28, 2011 IGARSS 2011 TH4.T09.2
  • 23. July 28, 2011 IGARSS 2011 TH4.T09.2 0.3 0.4 0.5 0.6