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DETECTING SPATIOTEMPORAL DYNAMICS IN SATELLITE REMOTE SENSING TIME SERIES: METHODOLOGICAL APPROACH COMBINING OBIA AND DATA MINING TECHNIQUES
Dino Ienco
Researcher @Irstea (UMR-TETIS)
Fabio N. Güttler
Jordi Nin,
Pascal Poncelet,
Maguelonne Teisseire
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
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I. Climate Change & Remote Sensing
II. Introduction
III. Study Area
IV. Methods
V. Results
VI. Conclusions / Perspectives
OUTLINE
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
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Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
I. CLIMATE CHANGES & REMOTE SENSING
Climate changes :
• Important Issue at global/local scale
• Impacts on various sectors: agriculture, fishery,
forestry, water management, health, coastal
environment, natural habitats, etc…
Study what happens in the past to
understand how to act in the future
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Remote Sensing analysis constitutes an important tool to
study what happens:
- Capture area characterisitcs (spectral/radiometric)
- Huge quantity of images available soon
- Acquisition of time series over the same area
I. CLIMATE CHANGES & REMOTE SENSING
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
Series of satellite images can help to understand long term behaviors
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Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
I. CLIMATE CHANGES & REMOTE SENSING
Mining series of satellite images can help to:
Automatically analyse huge data
Study long term behaviour
Extract evolutions of natural/artificial habitats
Methodology to Mine Evolutions/Changes on
Remote Sensing time series data
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II. INTRODUCTION
• Spectral response of natural vegetation
• Vegetation types
• Textural response
• Vegetation structure (mapping physiognomic
classes, e.g. shrubland and grassland)
• Temporal response
• Vegetation phenology
and dynamics
MOTIVATIONS – REMOTE SENSING POSSIBILITIES
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
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II. INTRODUCTION
• Detect spatiotemporal evolutions on satellite time series automatically
• Provided useful information for natural habitats monitoring and mapping
• Propose a new and adapted method for future high repetitivity RS time series
(i.e. Sentinel-2) that can be employed to understand Climate Changes
OBJECTIVES
Methods describing multi-temporal behaviour are among open
challenges in OBIA (Blaschke et al. 2014 ; Chen et al. 2012)
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
[Blaschke et al 2014] Blaschke et al. Geographic object-based image analysis towards a new paradigm. ISPRS Journal of
Photogrammetry and Remote Sensing 87 (0), 180–191.
[Chen et al. 2012] Chen, G., Hay, G. J., Carvalho, L. M. T., Wulder, M. A., 2012. Object- based change detection.
International Journal of Remote Sensing 33 (14), 4434–4457.
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III. STUDY AREAS AND TIME SERIES
Low Aude Valley
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
Area: 4,842 ha
Dominated by
natural habitat
types of
Community
interest (19 in
total)
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III. STUDY AREAS AND TIME SERIES
Low Aude Valley
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
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IV. PROPOSED METHODOLOGY
MOTIVATIONS
- Approach coupling OBIA (Object-based Image
Analysis) and Data Mining
- Method ables to track object evolution along the time
- Main assumption - Maximal Spatial Extent: For each complex habitat there will be a
timestamp, in the time series, in which we can
observe its maximum extent
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
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IV. PROPOSED METHODOLOGY
GENERAL SCHEMA
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
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MAIN STEPS
Step 1 •Preprocessing
Step 2
Step 3
Step 4
Step 5
Step 6
- data standardization
- fine geometrical registration
- spatial subset
- spectral indices
- …
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
IV. PROPOSED METHODOLOGY
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Step 1 •Preprocessing
Step 2 •Segmentation
Step 3
Step 4
Step 5
Step 6
T0
T1
Tn
…
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
IV. PROPOSED METHODOLOGY
MAIN STEPS
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Step 1 •Preprocessing
Step 2 •Segmentation
Step 3 •BB selection
Step 4
Step 5
Step 6
- Retrieve the Maximal Spatial Extents - On the whole study area
- Through the 6 timestamp
- The output is a list of objects
candidateBB
T0
T1
Tn
…
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
IV. PROPOSED METHODOLOGY
MAIN STEPS
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Step 1 •Preprocessing
Step 2 •Segmentation
Step 3 •BB selection
Step 4
Step 5
Step 6
- Filtering of candidateBB - Obtain a global cover of the
study area
- Decrease the overlapping
among the BB (redundancy)
- Retrieve the Maximal Spatial Extents - On the whole study area
- Through the 6 timestamp
- The output is a list of objects
candidateBB
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
IV. PROPOSED METHODOLOGY
MAIN STEPS
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Step 1 •Preprocessing
Step 2 •Segmentation
Step 3 •BB selection
Step 4 •Graph construction
Step 5
Step 6
- Each BB is exploited to build an
evolution graph
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
IV. PROPOSED METHODOLOGY
MAIN STEPS
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Step 1 •Preprocessing
Step 2 •Segmentation
Step 3 •BB selection
Step 4 •Graph construction
Step 5
Step 6
T0
T1
T2
T3
T4
object ID
timestamp
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
IV. PROPOSED METHODOLOGY
MAIN STEPS
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Step 1 •Preprocessing
Step 2 •Segmentation
Step 3 •BB selection
Step 4 •Graph construction
Step 5
Step 6
T0
T1
T2
T3
T4
object ID
timestamp
overlap between objects
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
IV. PROPOSED METHODOLOGY
MAIN STEPS
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Step 1 •Preprocessing
Step 2 •Segmentation
Step 3 •BB selection
Step 4 •Graph construction
Step 5
•Measuring evolutions and similarity
- Evaluate the temporal evolution of a
natural habitat
- Evaluate the global intensity of
evolution of a graph
- graph ranking for each set of
chosen attributes
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
IV. PROPOSED METHODOLOGY
MAIN STEPS
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V. RESULTS
- Total Number of Objects)
3 373 (~ 562 per timestamp)
- Total Number of generated graphs
BPA = 340
Landsat 2009
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
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- Each node contains a set of
attributes
- Band1
- Band2
- …
- NDVI
- NDWI
- VSDI
- …
- The graphs are also described
by some features
- N. of nodes
- N. of edges
- N. of sequences
- … Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
V. RESULTS
EVOLUTION GRAPHS
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EVOLUTION GRAPHS
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
V. RESULTS
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INTENSITY OF EVOLUTION
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
V. RESULTS
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VI. CONCLUSIONS
• The proposed methodology :
• Connects objects along the time series monitoring the same phenomenon over time
• Combines OBIA & Data Mining to extract spatiotemporal evolutions
• Can be easily applied to longer time series, no assumption on the number of images is made
• Can be used to generate a ranking of the habitats considering their changes during the time (and the
ranking can be exploited by expert to focus their attention on particular portion of the study area)
Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
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Tri-National Scientific Workshop, 29 October 2015, Bogor, Indonesia
Thank you for your attention
ANY QUESTION
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NDVI and NDWI evolutions
22-Feb
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NDVI and NDWI evolutions
27-Feb
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NDVI and NDWI evolutions
03-Apr
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NDVI and NDWI evolutions
13-Apr
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NDVI and NDWI evolutions
18-Apr
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NDVI and NDWI evolutions
23-Apr
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NDVI and NDWI evolutions
13-May
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NDVI and NDWI evolutions
02-Jun