Textural and contextual land-cover classification using single and multiple classifier systems

Photogrammetric Engineering and Remote Sensing

Volumn 68, Issue 6, 2002, Pages 597-605

  Debeir, Olivier ^a   Van Den Steen, Isabelle ^a   Latinne, Patrice ^a   Van Ham, Philippe ^a   Wolff, Eléonore ^a  

^a UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; IMAGE QUALITY; LAND USE;

MAPPER;

REMOTE SENSING;

IMAGE CLASSIFICATION; LAND COVER; LAND USE; LANDSAT THEMATIC MAPPER;

BELGIUM;

EID: 0036276624     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (49)

1. Land-use classification schemes
2. Computational image interpretation models: An overview and perspective
3. Neural networks in remote sensing
4. Nearest neighbour classification from multiple feature subsets
5. Bagging predictor
6. Spatial correlation effects upon accuracy of supervised classification of land cover
7. A coefficient of agreement for nominal scales
8. Approximate statistical tests for comparing supervised learning algorithms
9. Modeling uncertainties in photo interpreted boundaries
10. Multi-scale fractal analysis of image texture and pattern
11. Decision tree classification of land cover from remotely sensed data
12. Combination of neural and statistical algorithms for supervised classification of remotesensing images
13. Frequency-based contextual classification and gray-level vector reduction for land-use identification
14. The use of contextual information in the classification of remotely sensed data
15. Classification trees: An alternative to traditional land cover classifiers
16. Textural features for image classification
17. Texture classification by center-symmetric auto-correlation, using Kullback discrimination of distribution
18. The random subspace method for constructing decision forests
19. Decision combination in multiple classifier systems
20. Correct formulation of the kappa coefficient
21. Combinations of weak classifiers
22. Combining classifiers: A theoretical framework
23. Different ways of weakening decision trees and their impact on classification accuracy
24. Fusion of HYDICE hyperspectral data with panchromatic imagery for cartographic feature extraction
25. The use of census data in urban image classification
26. Process in knowledge engineering for image interpretation and classification
27. Knowledge for supervised evidential classification
28. Bagging, boosting and C4.5
29. Multispectral satellite image and ancillary data integration for geological classification
30. A means for utilising ancillary information in multispectral classification
31. Image segmentation and image models
32. Surface characterisation: Fractal dimension. Hurst coefficient and frequency transform
33. Combining spectral and texture data in the segmentation of remotely sensed images
34. On comparing classifiers: Pitfalls to avoid and a recommended approach
35. The use of prior probabilities in maximum likelihood classification of remotely sensed data
36. Spatial composition spectral classes: A structural approach for image analysis of heterogeneous land-use and land cover types
37. Spatial analysis of land cover patterns and corresponding remotely-sensed image brightness
38. Methods of combining multiple classifiers and their applications to handwriting recognition
39. Measuring uncertainty in class assignment for natural resource maps under fuzzy logic