Segmenting multispectral landsat TM images into field units

IEEE Transactions on Geoscience and Remote Sensing

Volumn 40, Issue 5, 2002, Pages 1054-1064

  Evans, Carolyn ^a   Jones, Ronald ^b   Svalbe, Imants ^c   Berman, Mark ^a  

^a CSIRO   (Australia)

^b Proteome Systems Ltd   (Australia)

^c MONASH UNIVERSITY   (Australia)

Author keywords

Image segmentation; Multi variate statistics; Multispectral images

Indexed keywords

ALGORITHMS; EIGENVALUES AND EIGENFUNCTIONS; FARMS; SATELLITES; STATISTICS;

MULTISPECTRAL IMAGES;

IMAGE SEGMENTATION;

AGRICULTURE; ALGORITHMS; IMAGE ANALYSIS; SATELLITES; STATISTICS;

EID: 0036564129     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2002.1010893     Document Type: Article

References (26)

1. Image segmentation by region growing and spectral clustering with a natural convergence criterion
2. Mapping and monitoring salt-affected land in Western Australia
3. Classification of mulispectral Image data by extraction and classification of homogeneous objects
4. Terrain objects, their dynamics and their monitoring by the intergration of GIS and remote sensing
5. A neural system for deforestation monitoring on landsat images of the amazon region
6. Refining image segmentation by integration of edge and region data
7. Delineating agricultural field boundaries from TM imagery using dyadic wavelet transforms
8. Morphological partitioning of multispectral images
9. Image segmentation and discriminant analysis for the identification of land cover units in ecology
10. Seeded region growing
11. Edge detection alternatives for multispectral remote sensing images
12. A transformation for ordering multispectral data in terms of image quality with implications for noise removal
13. Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform
14. Field segmentation in multispectral satellite images
15. On the calculation of a robust S-estimator of a covariance matrix
16. An experimental rule based system for testing low level segmentation strategies
17. p version of the Hausdorff metric
18. The use of different metrics in vector median filtering: Application to fine arts and paintings