Object-based classification for mangrove with VHR remotely sensed image

Zhigang Liu; Jing Li; Boonleong Lim; Chungyueh Seng; Suppiah Inbaraj

doi:10.1117/12.760797

26 July 2007 Object-based classification for mangrove with VHR remotely sensed image

Zhigang Liu, Jing Li, Boonleong Lim, Chungyueh Seng, Suppiah Inbaraj

Proceedings Volume 6752, Geoinformatics 2007: Remotely Sensed Data and Information; 67523C (2007) https://doi.org/10.1117/12.760797
Event: Geoinformatics 2007, 2007, Nanjing, China

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.

Citation Download Citation

Zhigang Liu, Jing Li, Boonleong Lim, Chungyueh Seng, and Suppiah Inbaraj "Object-based classification for mangrove with VHR remotely sensed image", Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67523C (26 July 2007); https://doi.org/10.1117/12.760797

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