SlideShare a Scribd company logo
Temporal Analysis of Forest Cover Using a Hidden Markov Model Arnt-Børre Salberg and Øivind Due Trier  Norwegian Computing Center Oslo, Norway IGARSS 2011,  July 27
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction: Change detection ,[object Object],[object Object],[object Object],[object Object],[object Object]
Hidden Markov Model (HMM) ,[object Object],[object Object],[object Object]
Hidden Markov Model (HMM) ,[object Object],[object Object],class 1 class 2 P(1|1) P(2|1) class 3 P(3|1)
Class sequence estimation ,[object Object],[object Object],[object Object]
Most likely class sequence (MLCS) ,[object Object],[object Object],[object Object]
Viterbi algorithm Forest Sparse forest Grass Soil Forest Sparse forest Grass Soil Possible states at time  t Possible states at time  t +1 Most probable sequence of previous states for each state at time  t The best sequence ending at state c, given the observations x 1 , …, x t The probability of jumping from state c to state k (this is dependent on the time interval) The probability of observing the actual observation, given that the state is k
Minimum probability of class error ,[object Object],[object Object],[object Object]
Class transition probabilities ,[object Object],[object Object],[object Object],[object Object]
Clouds ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data distributions, class transition probabilities, co-registration ,[object Object],[object Object],[object Object],[object Object],[object Object]
Atmospheric disturbance ,[object Object],[object Object],Ground surface  reflectance Atmosphere Top of the  atmosphere reflectance
Landsat 5 TM images (166/63) Amani, Tanzania 1985-03-09 1986-08-19 1986-06-16 1986-10-06 1987-08-06 1995-02-17 1995-05-24 2008-06-12 2009-11-22 2009-11-06 2009-12-08 2009-07-01 2010-02-10 1995-02-01
Results - Forest cover maps 2010-02-10 1995-02-17 1986-06-16 Worldview-2  2010-03-04 Forest Sparse forest Soil Grass
Results - Forest cover change  2010-02-10 1995-02-17 1986-06-16 1995-02-01 1986-10-06 1986-08-19 2009-07-01 2009-11-22 2009-12-08 WV2 2010-03-25 Clouded observation Clouded observation Clouded observation
Landsat 5 TM images (227-062) Santarém, Brazil 1988-09-04 1989-08-22 1992-07-29 1993-05-29 1995-06-04 1996-07-08 2004-08-31 2005-07-01 2005-07-17 2006-08-05 2007-06-21 2008-06-23 2008-09-11 2009-07-12 2009-07-28
Results - Forest cover maps Santarém, Brazil 2008-06-23 1997-07-27 1986-07-29 2007-06-23
Results - Forest cover change maps Santarém, Brazil 2008-06-23 1997-07-27 1986-07-29 2007-06-23
Multsensor possibilities ,[object Object],[object Object],[object Object]
Temporal forest cover sequence ,[object Object],CLASSES  t  t-1  t+1 y t y t-2  t-2 y t y t+1 y t-1 Optical Optical Optical Optical SAR SAR TIME OBSERVATIONS
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Acknowledgements ,[object Object]

More Related Content

What's hot

Ramos, almeida: artificial ant colonies in digital image habitats – a mass be...
Ramos, almeida: artificial ant colonies in digital image habitats – a mass be...Ramos, almeida: artificial ant colonies in digital image habitats – a mass be...
Ramos, almeida: artificial ant colonies in digital image habitats – a mass be...
ArchiLab 7
 
Computational Physics Final Report (MATLAB)
Computational Physics Final Report (MATLAB)Computational Physics Final Report (MATLAB)
Computational Physics Final Report (MATLAB)
Ricardo Fritzke
 
cloud_futures_2.0_Papazachos
cloud_futures_2.0_Papazachoscloud_futures_2.0_Papazachos
cloud_futures_2.0_Papazachos
George Spyrou
 
Barber_TU2.T03_hk_mb_casa_fg_hk_FINAL.pptx
Barber_TU2.T03_hk_mb_casa_fg_hk_FINAL.pptxBarber_TU2.T03_hk_mb_casa_fg_hk_FINAL.pptx
Barber_TU2.T03_hk_mb_casa_fg_hk_FINAL.pptx
grssieee
 
A Testbed for Image Synthesis
A Testbed for Image SynthesisA Testbed for Image Synthesis
A Testbed for Image Synthesis
Ben Trumbore
 
Trumbore_pittcon13_v2
Trumbore_pittcon13_v2Trumbore_pittcon13_v2
Trumbore_pittcon13_v2
Ben Trumbore
 

What's hot (20)

Improving Physical Parametrizations in Climate Models using Machine Learning
Improving Physical Parametrizations in Climate Models using Machine LearningImproving Physical Parametrizations in Climate Models using Machine Learning
Improving Physical Parametrizations in Climate Models using Machine Learning
 
Integration of flux tower data and remotely sensed data into the SCOPE simula...
Integration of flux tower data and remotely sensed data into the SCOPE simula...Integration of flux tower data and remotely sensed data into the SCOPE simula...
Integration of flux tower data and remotely sensed data into the SCOPE simula...
 
Ramos, almeida: artificial ant colonies in digital image habitats – a mass be...
Ramos, almeida: artificial ant colonies in digital image habitats – a mass be...Ramos, almeida: artificial ant colonies in digital image habitats – a mass be...
Ramos, almeida: artificial ant colonies in digital image habitats – a mass be...
 
Understanding climate model evaluation and validation
Understanding climate model evaluation and validationUnderstanding climate model evaluation and validation
Understanding climate model evaluation and validation
 
Estimation of global solar radiation by using machine learning methods
Estimation of global solar radiation by using machine learning methodsEstimation of global solar radiation by using machine learning methods
Estimation of global solar radiation by using machine learning methods
 
Computational Physics Final Report (MATLAB)
Computational Physics Final Report (MATLAB)Computational Physics Final Report (MATLAB)
Computational Physics Final Report (MATLAB)
 
cloud_futures_2.0_Papazachos
cloud_futures_2.0_Papazachoscloud_futures_2.0_Papazachos
cloud_futures_2.0_Papazachos
 
Tnos
TnosTnos
Tnos
 
Analysis_molf
Analysis_molfAnalysis_molf
Analysis_molf
 
Barber_TU2.T03_hk_mb_casa_fg_hk_FINAL.pptx
Barber_TU2.T03_hk_mb_casa_fg_hk_FINAL.pptxBarber_TU2.T03_hk_mb_casa_fg_hk_FINAL.pptx
Barber_TU2.T03_hk_mb_casa_fg_hk_FINAL.pptx
 
QMC: Operator Splitting Workshop, Using Sequences of Iterates in Inertial Met...
QMC: Operator Splitting Workshop, Using Sequences of Iterates in Inertial Met...QMC: Operator Splitting Workshop, Using Sequences of Iterates in Inertial Met...
QMC: Operator Splitting Workshop, Using Sequences of Iterates in Inertial Met...
 
DEEP LEARNING BASED MULTIPLE REGRESSION TO PREDICT TOTAL COLUMN WATER VAPOR (...
DEEP LEARNING BASED MULTIPLE REGRESSION TO PREDICT TOTAL COLUMN WATER VAPOR (...DEEP LEARNING BASED MULTIPLE REGRESSION TO PREDICT TOTAL COLUMN WATER VAPOR (...
DEEP LEARNING BASED MULTIPLE REGRESSION TO PREDICT TOTAL COLUMN WATER VAPOR (...
 
Forest Change Detection in incomplete satellite images with deep neural networks
Forest Change Detection in incomplete satellite images with deep neural networksForest Change Detection in incomplete satellite images with deep neural networks
Forest Change Detection in incomplete satellite images with deep neural networks
 
A Testbed for Image Synthesis
A Testbed for Image SynthesisA Testbed for Image Synthesis
A Testbed for Image Synthesis
 
pdf_2076
pdf_2076pdf_2076
pdf_2076
 
Fractional approaches in dielectric broadband spectroscopy
Fractional approaches in dielectric broadband spectroscopyFractional approaches in dielectric broadband spectroscopy
Fractional approaches in dielectric broadband spectroscopy
 
1998278
19982781998278
1998278
 
Spectroscopic confirmation of_the_existence_of_large_diffuse_galaxies_in_the_...
Spectroscopic confirmation of_the_existence_of_large_diffuse_galaxies_in_the_...Spectroscopic confirmation of_the_existence_of_large_diffuse_galaxies_in_the_...
Spectroscopic confirmation of_the_existence_of_large_diffuse_galaxies_in_the_...
 
A Prediction Technique for Chaotic Time Series
A Prediction Technique for Chaotic Time SeriesA Prediction Technique for Chaotic Time Series
A Prediction Technique for Chaotic Time Series
 
Trumbore_pittcon13_v2
Trumbore_pittcon13_v2Trumbore_pittcon13_v2
Trumbore_pittcon13_v2
 

Similar to temporal_analysis_forest_cover_using_hmm.ppt

2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
grssieee
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
grssieee
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
grssieee
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
grssieee
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
grssieee
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
grssieee
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
grssieee
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
grssieee
 
AT_MB_MM_IGARSS2011.ppt
AT_MB_MM_IGARSS2011.pptAT_MB_MM_IGARSS2011.ppt
AT_MB_MM_IGARSS2011.ppt
grssieee
 

Similar to temporal_analysis_forest_cover_using_hmm.ppt (20)

lost_valley_search.pdf
lost_valley_search.pdflost_valley_search.pdf
lost_valley_search.pdf
 
Inversão com sigmoides
Inversão com sigmoidesInversão com sigmoides
Inversão com sigmoides
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
 
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
 
Rocca.ppt
Rocca.pptRocca.ppt
Rocca.ppt
 
Quantifying Landscape Changes through Land Cover Transition Potential Analysi...
Quantifying Landscape Changes through Land Cover Transition Potential Analysi...Quantifying Landscape Changes through Land Cover Transition Potential Analysi...
Quantifying Landscape Changes through Land Cover Transition Potential Analysi...
 
Theories and Applications of Spatial-Temporal Data Mining and Knowledge Disco...
Theories and Applications of Spatial-Temporal Data Mining and Knowledge Disco...Theories and Applications of Spatial-Temporal Data Mining and Knowledge Disco...
Theories and Applications of Spatial-Temporal Data Mining and Knowledge Disco...
 
AT_MB_MM_IGARSS2011.ppt
AT_MB_MM_IGARSS2011.pptAT_MB_MM_IGARSS2011.ppt
AT_MB_MM_IGARSS2011.ppt
 
Landsat calibration summary_rse
Landsat calibration summary_rseLandsat calibration summary_rse
Landsat calibration summary_rse
 
Landsat calibration summary_rse
Landsat calibration summary_rseLandsat calibration summary_rse
Landsat calibration summary_rse
 
A Land Data Assimilation System Utilizing Low Frequency Passive Microwave Rem...
A Land Data Assimilation System Utilizing Low Frequency Passive Microwave Rem...A Land Data Assimilation System Utilizing Low Frequency Passive Microwave Rem...
A Land Data Assimilation System Utilizing Low Frequency Passive Microwave Rem...
 
PhD defence - Steven Vanonckelen
PhD defence - Steven VanonckelenPhD defence - Steven Vanonckelen
PhD defence - Steven Vanonckelen
 
A beamforming comparative study of least mean square, genetic algorithm and g...
A beamforming comparative study of least mean square, genetic algorithm and g...A beamforming comparative study of least mean square, genetic algorithm and g...
A beamforming comparative study of least mean square, genetic algorithm and g...
 
Taramelli Melelli
Taramelli MelelliTaramelli Melelli
Taramelli Melelli
 

More from grssieee

Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
grssieee
 
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODELSEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
grssieee
 
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
grssieee
 
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIESTHE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
grssieee
 
GMES SPACE COMPONENT:PROGRAMMATIC STATUS
GMES SPACE COMPONENT:PROGRAMMATIC STATUSGMES SPACE COMPONENT:PROGRAMMATIC STATUS
GMES SPACE COMPONENT:PROGRAMMATIC STATUS
grssieee
 
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETERPROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
grssieee
 
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
grssieee
 
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
grssieee
 
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
grssieee
 
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
grssieee
 
test 34mb wo animations
test  34mb wo animationstest  34mb wo animations
test 34mb wo animations
grssieee
 
2011_Fox_Tax_Worksheets.pdf
2011_Fox_Tax_Worksheets.pdf2011_Fox_Tax_Worksheets.pdf
2011_Fox_Tax_Worksheets.pdf
grssieee
 
DLR open house
DLR open houseDLR open house
DLR open house
grssieee
 
DLR open house
DLR open houseDLR open house
DLR open house
grssieee
 
DLR open house
DLR open houseDLR open house
DLR open house
grssieee
 
Tana_IGARSS2011.ppt
Tana_IGARSS2011.pptTana_IGARSS2011.ppt
Tana_IGARSS2011.ppt
grssieee
 
Solaro_IGARSS_2011.ppt
Solaro_IGARSS_2011.pptSolaro_IGARSS_2011.ppt
Solaro_IGARSS_2011.ppt
grssieee
 

More from grssieee (20)

Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
 
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODELSEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
 
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
 
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIESTHE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
 
GMES SPACE COMPONENT:PROGRAMMATIC STATUS
GMES SPACE COMPONENT:PROGRAMMATIC STATUSGMES SPACE COMPONENT:PROGRAMMATIC STATUS
GMES SPACE COMPONENT:PROGRAMMATIC STATUS
 
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETERPROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
 
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
 
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
 
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
 
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
 
Test
TestTest
Test
 
test 34mb wo animations
test  34mb wo animationstest  34mb wo animations
test 34mb wo animations
 
Test 70MB
Test 70MBTest 70MB
Test 70MB
 
Test 70MB
Test 70MBTest 70MB
Test 70MB
 
2011_Fox_Tax_Worksheets.pdf
2011_Fox_Tax_Worksheets.pdf2011_Fox_Tax_Worksheets.pdf
2011_Fox_Tax_Worksheets.pdf
 
DLR open house
DLR open houseDLR open house
DLR open house
 
DLR open house
DLR open houseDLR open house
DLR open house
 
DLR open house
DLR open houseDLR open house
DLR open house
 
Tana_IGARSS2011.ppt
Tana_IGARSS2011.pptTana_IGARSS2011.ppt
Tana_IGARSS2011.ppt
 
Solaro_IGARSS_2011.ppt
Solaro_IGARSS_2011.pptSolaro_IGARSS_2011.ppt
Solaro_IGARSS_2011.ppt
 

Recently uploaded

Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo DiehlFuture Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Peter Udo Diehl
 
Search and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical FuturesSearch and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical Futures
Bhaskar Mitra
 
Essentials of Automations: Optimizing FME Workflows with Parameters
Essentials of Automations: Optimizing FME Workflows with ParametersEssentials of Automations: Optimizing FME Workflows with Parameters
Essentials of Automations: Optimizing FME Workflows with Parameters
Safe Software
 

Recently uploaded (20)

To Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMsTo Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
 
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
 
Speed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in MinutesSpeed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in Minutes
 
PLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. StartupsPLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. Startups
 
Introduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG EvaluationIntroduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG Evaluation
 
Exploring UiPath Orchestrator API: updates and limits in 2024 🚀
Exploring UiPath Orchestrator API: updates and limits in 2024 🚀Exploring UiPath Orchestrator API: updates and limits in 2024 🚀
Exploring UiPath Orchestrator API: updates and limits in 2024 🚀
 
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo DiehlFuture Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
 
Free and Effective: Making Flows Publicly Accessible, Yumi Ibrahimzade
Free and Effective: Making Flows Publicly Accessible, Yumi IbrahimzadeFree and Effective: Making Flows Publicly Accessible, Yumi Ibrahimzade
Free and Effective: Making Flows Publicly Accessible, Yumi Ibrahimzade
 
How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...
 
Search and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical FuturesSearch and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical Futures
 
UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1
 
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomSalesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
 
IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024
 
Mission to Decommission: Importance of Decommissioning Products to Increase E...
Mission to Decommission: Importance of Decommissioning Products to Increase E...Mission to Decommission: Importance of Decommissioning Products to Increase E...
Mission to Decommission: Importance of Decommissioning Products to Increase E...
 
What's New in Teams Calling, Meetings and Devices April 2024
What's New in Teams Calling, Meetings and Devices April 2024What's New in Teams Calling, Meetings and Devices April 2024
What's New in Teams Calling, Meetings and Devices April 2024
 
Essentials of Automations: Optimizing FME Workflows with Parameters
Essentials of Automations: Optimizing FME Workflows with ParametersEssentials of Automations: Optimizing FME Workflows with Parameters
Essentials of Automations: Optimizing FME Workflows with Parameters
 
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
 
Connector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a buttonConnector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a button
 
JMeter webinar - integration with InfluxDB and Grafana
JMeter webinar - integration with InfluxDB and GrafanaJMeter webinar - integration with InfluxDB and Grafana
JMeter webinar - integration with InfluxDB and Grafana
 
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
 

temporal_analysis_forest_cover_using_hmm.ppt

  • 1. Temporal Analysis of Forest Cover Using a Hidden Markov Model Arnt-Børre Salberg and Øivind Due Trier Norwegian Computing Center Oslo, Norway IGARSS 2011, July 27
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. Viterbi algorithm Forest Sparse forest Grass Soil Forest Sparse forest Grass Soil Possible states at time t Possible states at time t +1 Most probable sequence of previous states for each state at time t The best sequence ending at state c, given the observations x 1 , …, x t The probability of jumping from state c to state k (this is dependent on the time interval) The probability of observing the actual observation, given that the state is k
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15. Landsat 5 TM images (166/63) Amani, Tanzania 1985-03-09 1986-08-19 1986-06-16 1986-10-06 1987-08-06 1995-02-17 1995-05-24 2008-06-12 2009-11-22 2009-11-06 2009-12-08 2009-07-01 2010-02-10 1995-02-01
  • 16. Results - Forest cover maps 2010-02-10 1995-02-17 1986-06-16 Worldview-2 2010-03-04 Forest Sparse forest Soil Grass
  • 17. Results - Forest cover change 2010-02-10 1995-02-17 1986-06-16 1995-02-01 1986-10-06 1986-08-19 2009-07-01 2009-11-22 2009-12-08 WV2 2010-03-25 Clouded observation Clouded observation Clouded observation
  • 18. Landsat 5 TM images (227-062) Santarém, Brazil 1988-09-04 1989-08-22 1992-07-29 1993-05-29 1995-06-04 1996-07-08 2004-08-31 2005-07-01 2005-07-17 2006-08-05 2007-06-21 2008-06-23 2008-09-11 2009-07-12 2009-07-28
  • 19. Results - Forest cover maps Santarém, Brazil 2008-06-23 1997-07-27 1986-07-29 2007-06-23
  • 20. Results - Forest cover change maps Santarém, Brazil 2008-06-23 1997-07-27 1986-07-29 2007-06-23
  • 21.
  • 22.
  • 23.
  • 24.