Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches

International Journal of Remote Sensing

Volumn 22, Issue 4, 2001, Pages 615-628

  Zhang, J ^a   Foody, G M ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA REDUCTION; FEATURE EXTRACTION; FUZZY SETS; IMAGE ANALYSIS; IMAGE QUALITY; LAND USE; NEURAL NETWORKS;

FUZZY CLUSTERING ALGORITHMS;

REMOTE SENSING;

ARTIFICIAL NEURAL NETWORK; FUZZY MATHEMATICS; IMAGE CLASSIFICATION; LAND COVER; REMOTE SENSING; SUBURBAN AREA;

EDINBURGH; SCOTLAND; UNITED KINGDOM;

EID: 0035048764     PISSN: 01431161     EISSN: None     Source Type: Journal    
DOI: 10.1080/01431160050505883     Document Type: Article

References (39)

1. Neural networks in remote sensing
2. Inferring urban land use from satellite sensor images using kernel-based spatial reclassification
3. Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels
4. FCM: The fuzzy c-means clustering algorithm
5. Some aspects of allocation and discrimination
6. A review of assessing the accuracy of classification of remotely sensed data
7. Assumptions implicit in remote sensing data acquisition and analysis
8. Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data
9. Approaches for the production and evaluation of fuzzy land cover classifications from remotely sensed data
10. Fully fuzzy supervised classification of land cover from remotely sensed imagery with an artificial neural network
11. The continuum of classification fuzziness in thematic mapping
12. Incorporation of mixed pixels in the training, allocation and testing stages of supervised classifications
13. Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions
14. Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification
15. Some urban measurements from Landsat data
16. Detecting sub-pixel woody vegetation in digital imagery using two artificial intelligence approaches
17. Integrating GIS and remote sensing for vegetation analysis and modelling: Methodological issues
18. Theory and methods for accuracy assessment of thematic maps using fuzzy sets
19. Forestry information content of Thematic Mapper data
20. Accuracy assessment of satellite derived land cover data: A review
21. Land-cover discrimination in SPOT HRV imagery using an artificial neural network-20-class experiment
22. Landscape and plant community boundaries in biogeography
23. Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications
24. Fuzzy classification of spatially degraded Thematic Mapper data for the estimation of sub-pixel components
25. Combining membership grades in image classification
26. Agricultural land-cover discrimination using thematic mapper spectral bands
27. The effect of misregistration on change detection
28. Improving remote sensing image analysis through fuzzy information representation
29. Using cover-type likelihoods and typicalities in a geographic information system data structure to map gradually changing environments
30. A fuzzy classification of sub-urban land cover from remotely sensed imagery
31. Classification of land cover with probabilistic, fuzzy and contextual evidence
32. An evaluation of fuzzy approaches to mapping land cover from aerial photographs