Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem

作者：Zhang Caiyun^*

来源：ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 104: 213-223.

DOI：10.1016/j.isprsjprs.2014.06.005

• Data fusion
• Machine learning
• Benthic habitat mapping
• Hyperspectral imagery
• Object-based image analysis
• Coral reef ecosystem

Accurate mapping and effective monitoring of benthic habitat in the Florida Keys are critical in developing management strategies for this valuable coral reef ecosystem. For this study, a framework was designed for automated benthic habitat mapping by combining multiple data sources (hyperspectral, aerial photography, and bathymetry data) and four contemporary imagery processing techniques (data fusion, Object-based Image Analysis (OBIA), machine learning, and ensemble analysis). In the framework, 1-m digital aerial photograph was first merged with 17-m hyperspectral imagery and 10-m bathymetiy data using a pixel/feature-level fusion strategy. The fused dataset was then preclassified by three machine learning algorithms (Random Forest, Support Vector Machines, and k-Nearest Neighbor). Final object-based habitat maps were produced through ensemble analysis of outcomes from three classifiers. The framework was tested for classifying a group-level (3-class) and code-level (9-class) habitats in a portion of the Florida Keys. Informative and accurate habitat maps were achieved with an overall accuracy of 88.5% and 83.5% for the group-level and code-level classifications, respectively.

• 出版日期2015-6