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Ocean Science An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/os-2019-13
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-2019-13
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 12 Jul 2019

Submitted as: research article | 12 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Ocean Science (OS).

Improved Spectral Angle Mapper applications for mangrove classification using SPOT5 imagery

Xiu Su, Xiang Wang, Jianhua Zhao, Ke Cao, Jianchao Fan, and Zhengxian Yang Xiu Su et al.
  • National Marine Environmental Monitoring Center, Dalian 116023, China

Abstract. The traditional Spectral Angle Mapper (SAM) is an image classification method that uses image endmember spectra. Image spatial structure information may be neglected, especially in mangrove classification research where there is greater spectral similarity between species. This study combined object-oriented classification to improve the accuracy of the method in mangrove ecosystems. A mangrove area in Guangxi's coastal zone was chosen as the study site, and spectral feature analysis and ground investigations were carried out, combining pixel purification, training sample set optimization, and watershed image segmentation algorithm to improve the SAM. The improved SAM was used to classify SPOT5 remote sensing image data for a mangrove ecosystem and then classification accuracy was assessed. The results showed that the improved SAM had better classification accuracy for SPOT5 imagery. Accuracy for each mangrove species was greater than 80 % and overall accuracy was greater than 90 %, which showed that SAM was applicable for mangrove remote sensing. This application potential for classification and information extraction lays the foundation for commercialized remote sensing monitoring of mangrove ecosystems.

Xiu Su et al.
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Status: open (until 23 Nov 2019)
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