An efficient neural classification chain of SAR and optical urban images

Author(s): Gamba, P. | Houshmand, B. |

Year: 2001

Citation: INTERNATIONAL JOURNAL OF REMOTE SENSING Volume: 22 Issue: 8 Pages: 1535-1553

Abstract: In this paper a suitable neural classification algorithm, based on the use of Adaptive Resonance Theory (ART) networks, is applied to the fusion and classification of optical and SAR urban images. ART networks provide a flexible tool for classification, but are ruled by a large number of parameters. Therefore, the simplified ART2-A algorithm is used in this paper, and the neural approach is integrated into a classification chain where fuzzy clustering for merging of classes is also considered. The interaction between the two methods leads to encouraging results in less CPU time than classification with fuzzy clustering alone or other classical approaches (ISODATA). Examples of classification are provided using C-band total power AIRSAR data and optical images of Santa Monica, Los Angeles.

Topics: Image Analysis, Applications: Remote Sensing, Models: ART 2-A,

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