Land cover classification of SPOT image by local majority voting

Jen Hon Luo, Din Chang Tseng

Research output: Contribution to conferencePaperpeer-review

Abstract

We proposed a hierarchy scheme for the SPOT image land cover classification. In the first level, we combine the statistical classifier, maximum likelihood classification (MLC); the neural network classifier, Learning Vector Quantization (LVQ); and use a 3 × 3 window to extract second-order statistical features to classify the image. If the pixel can't reach the same label in this stage, it is processed in the second level. In the second stage, the first-order statistical features of each point in a window region are extracted. Then, the majority voting is used to label the pixel, the central point of the window, which is unclassified in the first level.

Original languageEnglish
Pages2931-2933
Number of pages3
StatePublished - 2001
Event2001 International Geoscience and Remote Sensing Symposium (Igarrs 2001) - Sydney, NSW, Australia
Duration: 9 Jul 200113 Jul 2001

Conference

Conference2001 International Geoscience and Remote Sensing Symposium (Igarrs 2001)
Country/TerritoryAustralia
CitySydney, NSW
Period9/07/0113/07/01

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