1. LAND COVER CLASSIFICATION WITH SUPPORT VECTOR MACHINE APPLIED TO MODIS IMAGERY
Paulo Gonçalves1
Hugo Carrão2
André Pinheiro2
Mário Caetano2
1 INRIA Rhône-Alpes, France
2 Instituto Geográfico Português, Portugal
15/11/2018
25th EARSeL Symposium, Porto 2005

2. Motivations and objectives
Automatic land cover classification, from moderate resolution reflectance imagery
MODIS data specificities:
- Multi-spectral reflectances: 7 bands (459nm nm)
Multi-temporal aquisition: weekly composite of daily images over one year
- Spatial resolution: meters
- Radiometric corrections and geolocated calibration
- Vegetation indices: NDVI and EVI
Methodology:
Support Vector Machine (SVM)
15/11/2018
25th EARSeL Symposium, Porto 2005

3. Classes nomenclature and sampling
9 land cover classes based on CORINE Land Cover 2000 nomenclature:
- Water areas
- Urban areas
- Bare soils
- Natural grasslands
- Shrub lands
- Needleleaf forests
- Broadleaf forests
- Non irrigated lands
- Irrigated lands
15/11/2018
25th EARSeL Symposium, Porto 2005

4. Classes nomenclature and sampling
9 land cover classes based on CORINE Land Cover 2000 nomenclature:
- Water areas (15)
- Urban areas (30)
- Bare soils (15)
- Natural grasslands (30)
- Shrub lands (30)
- Needleleaf forests (30)
- Broadleaf forests (30)
- Non irrigated lands (31)
- Irrigated lands (30)
Sample collection based on CORINE Land Cover 2000 cartography and Landsat ETM+ images
15/11/2018
25th EARSeL Symposium, Porto 2005

5. 25th EARSeL Symposium, Porto 2005
MODIS data processing toolbox (Matlab®)
15/11/2018
25th EARSeL Symposium, Porto 2005

6. Time sliding windowed median filter
Window width
25th EARSeL Symposium, Porto 2005

7. Cloud corrected profiles
Band 2
NDVI
EVI
Band 3
Band 7
Band 4
Band 5
Band 6
15/11/2018
25th EARSeL Symposium, Porto 2005

8. Support Vector Machine
Supervised machine learning system (Vapnik et al.,92)
1) Non-linear mapping of data into higher dimensional space
2) Linear separation in feature space
Non-linear classification in input space
Exhaustive benchmarks with standard classifiers exist (Nearest Neighbors, Neural Networks, Maximum Likelihood, Classification Trees...)
What kind of beast is this???
15/11/2018
25th EARSeL Symposium, Porto 2005

9. 25th EARSeL Symposium, Porto 2005
Support Vector Machine
15/11/2018
25th EARSeL Symposium, Porto 2005

10. 25th EARSeL Symposium, Porto 2005
Global classification performances
Experimental protocol
Principal components analysis with full rank representation (9 dim)
Cross validation to estimate generalized classification error (5 folds)
Single date measurements
K-NN
SVM
Generalized classification errors as functions of time
15/11/2018
25th EARSeL Symposium, Porto 2005

11. 25th EARSeL Symposium, Porto 2005
Class identification accuracy
User
Producer
15/11/2018
25th EARSeL Symposium, Porto 2005

12. 25th EARSeL Symposium, Porto 2005
Partial conclusions and future work
Land Cover Classification
Optimal date for MODIS-based Land Cover classification
Need to revise Classes Definition and Nomenclature
Need to collect new samples sets for Training and for Testing
Classification Methodology
Promising application of SVM learning systems
Fit the times series with (parsimonious) parametric models
(for data fusion and dimensionality reduction)
Use model parameters as inputs of SVM classifiers
15/11/2018
25th EARSeL Symposium, Porto 2005

13. Producer's Accuracy (%)
Class identification accuracy
Classification
Reference Data W NG
BLF BS SL
NLF IL
NIL U
User's Accuracy (%) 13 1
92.9 18 2 5 3
52.9 25
86.2 10
76.9 24
61.5
92.6 22
73.3 26
Producer's Accuracy (%)
86.7
60.0
83.3
66.7
80.0
71.0
15/11/2018
25th EARSeL Symposium, Porto 2005