Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification

Author:

Abe B T,Olugbara O O,Marwala T

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Reference73 articles.

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2. Abe B T, Olugbara O O and Marwala T 2012 Hyperspectral image classification using random forest and neural network; Lecture Notes in Engineering and Computer Science: Proceedings of the World Congress on Engineering and Computer Science, WCECS 2012, 24–26 October, San Francisco, USA, pp. 522–527.

3. Adams J B, Sabol D E, Kapos V, Filho R A, Roberts D A, Smith M O and Gillespie A R 1995 Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon; Remote Sens. Environ. 52(2) 137–154.

4. Bateson C A and Curtiss B 1996 A method for manual endmember selection and spectral unmixing; Remote Sens. Environ. 55 229–243.

5. Bateson C A, Asner G P and Wessman C A 2000 Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis; IEEE Trans. Geosci. Remote Sens. 38 1083–1094.

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