Transactions on Case-Based Reasoning

Volume 4 - Number 1 - October 2011 - Pages 18-33

A Comparison between CBR and MLC in Order to Identify an Aquaculture Area from a Coastal Image

L. Peng and D. Yunyan

State Key Laboratory of Resources and Environmental Information Systems,
Institute of Geographic Science and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China


Sea reclamation works and fish farming are increasingly common in coastal zones, and how to accurately and rapidly extract the coastal aquaculture area is important for the development of coastal zones. This paper discusses a CBR (case-based reasoning) method. Firstly, using a 10 meter resolution of a multi-spectral remote sensing image of Eastern Guangdong over ten thousands of spatial, spectral, shape and texture features were extracted based on the 1:50000 standard framing of land use thematic data and by using image analysis. Then nine optimized features were selected using the principal component analysis (PCA) and the construction of a case base was accomplished based on these. After that, a multi-scale image segmentation was performed on the 2.5 meter resolution of a fused image of the test area, which is located on the Western Guangdong coast, and CBR classification was applied on all the segmented image objects. In the end, the classification accuracy was evaluated. The CBR classifier classifies an aquaculture area within coastal belts with an accuracy of 84.6 %, in contrast to that the accuracy of the Maximum Likelihood Classifier (MLC) is 82.5 %. The CBR method outperforms the MLC by 2.2 % in prediction accuracy. The advantages of the CBR approach are obvious, particularly in the areas that are far away from the coastlines. In conclusion, the CBR approach could be successfully applied to the extraction of coastal aquaculture areas.

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