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

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

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

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

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