The evaluation of fuzzy membership of land cover classes in the suburban zone

Remote Sensing of Environment

Volumn 34, Issue 2, 1990, Pages 121-132

  Fisher, P F ^a   Pathirana, S ^a  

^a KENT STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY CLASSIFICATION; LAND COVER; SPATIAL RESOLUTION; SUBURBAN ZONE;

EID: 0025626316     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/0034-4257(90)90103-S     Document Type: Article

References (26)

1. FCM: The fuzzy c-means clustering algorithm
2. Convergence theory for Fuzzy c-Means Counterexamples and repairs
3. Efficient implementation of the Fuzzy c-Means Clustering Algorithm
4. Effect of mixed (boundary) pixels on crop proportion estimation
5. A modified fuzzy k-means method for predictive classification
6. IDRISI: A collective geographic analysis system project
7. Urban boundary detection from Landsat imagery: a GIS and Knowledge-based approach to MSS data
8. Evaluation of fuzzy membership of land cover classes in suburban areas of north-east Ohio
9. The use of structural information for improving land-cover classification accuracies at the rural-urban fringe
10. Modeling Error in Raster-based Spatial Data
11. Evaluation of several schemes for classification of remotely sensed data
12. The effect of spatial resolution on the classification of Thematic Mapper data
13. Spectral and textural features to classify elusive land cover at the urban fringe
14. Spatial classification using fuzzy membership models
15. Resolving the percentage of component terrains within single resolution elements
16. Fuzzy set theory applied to the mixed pixel problem of multispectral land cover databases
17. Fuzzy Clustering algorithms and their cluster validity
18. Convergence and consistency of Fuzzy c-Means/Isodata algorithms
19. An evaluation of thee accuracies of five algorithms for machine classification of remotely sensed data
20. Low-level segmentation of aerial images with fuzzy clustering
21. The factor of scale in remote sensing