Title :
Unsupervised classification of radar imagery of wetlands using the soft competition scheme
Author :
Durden, S.L. ; Haddad, Z.S.
Author_Institution :
Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA
Abstract :
The authors apply a technique developed in the field of vector quantization to the problem of unsupervised classification of radar imagery of wetlands. The method can perform better than traditional unsupervised methods, such as k-means, because it performs soft classification at each step. The method is applied to Alaska data and shown to give results that are similar to previous results using supervised classification. Results are also shown for data from Belize
Keywords :
geophysical signal processing; geophysical techniques; hydrological techniques; image classification; radar imaging; remote sensing by radar; terrain mapping; vegetation mapping; Alaska; Belize; USA; United States; geophysical measurement technique; hydrology; image classification; land surface; radar imagery; radar imaging; radar remote sensing; soft classification; soft competition scheme; terrain mapping; unsupervised classification; vector quantization; vegetation mapping; wetland; wetlands; Airborne radar; Clustering algorithms; Exchange rates; Image segmentation; Laboratories; Propulsion; Radar imaging; Synthetic aperture radar; Vector quantization; Vegetation mapping;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-6359-0
DOI :
10.1109/IGARSS.2000.858340