基于遥感案例推理的海岸带养殖信息提取

目前基于目视解释或光谱分类的养殖信息提取效率低,难以克服由于地物混杂带来的“椒盐”噪声现象且难以融合地学知识。针对养殖信息提取中存在的问题,首先在分析现有养殖信息提取方法和案例推理CBR(Case\|Based Reasoning)用于遥感图像处理的基础上,提出基于遥感案例推理的海岸带养殖信息提取的研究思路；其次,结合养殖区域的空间特征和属性特征,构建案例的表达模型以及CBR相似性推理模型；最后,对不属于案例构建区的粤西沙田镇进行养殖信息提取的CBR实验,精度达到84.56%。对比CBR方法和传统监督分类方法可知,CBR方法是实现海岸带养殖信息快速准确提取的一种有效手段。

A CBR Approach for Extracting Coastal Aquaculture Area

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∶50 000 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 84.56%,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.