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- W2051256246 abstract "In remotely sensed imagery with high spatial resolution, more detail spatial information of mangrove forest can be shown. It is important to find a method to effectively use the spatial information so as to improve the accuracy of mangrove forest classification. In the study, different classification schemes (including pixel-based classification and object-based classification), different classifiers, and different texture features have been conducted. The classification results of SPOT-5 image of Matang Mangrove Forest Reserve in Malaysia show that the performances of object-based classifications are better than that of pixel-based classifications. However, the classifier type is important for object-based classification. The accuracies of nearest neighborhood classifiers, which are widely used in object-based classifications, were obviously lower that that of maximum likelihood classifiers and support vector Machines. It is also shown that the involvement of second-order texture features can't effectively improve the classification accuracy of neither object-based classifications nor pixel-based classifications." @default.
- W2051256246 created "2016-06-24" @default.
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- W2051256246 date "2007-06-10" @default.
- W2051256246 modified "2023-09-23" @default.
- W2051256246 title "Object-based classification for mangrove with VHR remotely sensed image" @default.
- W2051256246 doi "https://doi.org/10.1117/12.760797" @default.
- W2051256246 hasPublicationYear "2007" @default.
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