Mapping coral reef benthic cover with fused IKONOS imagery

Yuanyuan Wang; Yunhao Chen; Jing Li

doi:10.1117/12.760704

26 July 2007 Mapping coral reef benthic cover with fused IKONOS imagery

Yuanyuan Wang, Yunhao Chen, Jing Li

Proceedings Volume 6752, Geoinformatics 2007: Remotely Sensed Data and Information; 67521F (2007) https://doi.org/10.1117/12.760704
Event: Geoinformatics 2007, 2007, Nanjing, China

Abstract

In this article, we present some experiments on coral reef benthic cover mapping with fused IKONOS image. The objective of our study is to establish an efficient approach for the classification task on hand. Four scenarios are designed and in each scenario two classification methods (Maximum Likelihood and Decision Tree) are implemented. Ground truth data is obtained through visual interpretation and manual digitization, against which accuracy of classification map is calculated. Results indicate that mining spectral information deeply (scenario III and IV) can increase classification accuracy dramatically. Compared with conventional utilization of spectral data (scenarioI), classification accuracy of ML and DT respectively increases by 3.94% and 5.15% under scenario IV. However, when spectral and spatial information is combined together (scenario II), accuracy of ML and DT is respectively reduced by 8.02% and 2.31%. It can be concluded from our study that when classify benthic cover with high-resolution remote sensing data in pixel-based pattern, utilization of spatial information should not be excessively emphasized. Fully exploiting spectral information may bring more benefits. Moreover, DT is more robust and can produce more accurate classification results than ML. Our results help scientists and managers in applying IKONOS-class data for coral reef mapping applications.

Citation Download Citation

Yuanyuan Wang, Yunhao Chen, and Jing Li "Mapping coral reef benthic cover with fused IKONOS imagery", Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67521F (26 July 2007); https://doi.org/10.1117/12.760704

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KEYWORDS

Earth observing sensors

High resolution satellite images

Image segmentation

Reflectivity

Associative arrays

Feature extraction

Image classification

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