基于关联规则面向对象的海岸带海水养殖模式遥感识别  

Remote sensing identification of coastal zone mariculture modes based on association-rules object-oriented method

作　　者：王芳[1] 夏丽华[1] 陈智斌[1] 崔文君 刘志根 潘翠红 Wang Fang;Xia Lihua;Chen Zhibin;Cui Wenjun;Liu Zhigen;Pan Cuihong(School of Geographical Sciences,Guangzhou University,Guangzhou 510006,China)

机构地区：[1]广州大学地理科学学院,广州510006

出　　处：《农业工程学报》2018年第12期210-217,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金面上项目(41371499);广东省渔业生态环境重点试验室开放基金(LFE-2013-5);广东省自然科学基金(2015A030313505);广东省科技计划资助项目(2015A020216021)

摘　　要：针对目前海水养殖模式遥感识别中的效率低,"同物异谱"、"异物同谱"和"椒盐"噪声等问题,该文研究了关联规则分类和面向对象相结合的养殖模式遥感识别方法,通过不同养殖模式的对象分割和关联规则的自动和智能获取,来构建海水养殖模式分类器。以高分一号PMS1卫星影像为数据源,把不同养殖模式对象的光谱、空间形态和纹理特征及其关联关系作为事务数据,使用Apriori算法挖掘类别作为后件的强规则,对粤东柘林湾养殖核心区内4种海水养殖模式(池塘养殖、网箱养殖、滩涂插养、浮筏吊养)水面信息进行提取。结果表明:基于关联规则面向对象的海水养殖模式分类精度能达到88.65%,比K-近邻法面向对象法精度提高了14.38个百分点,比关联规则挖掘分类法精度提高了12.16个百分点。关联规则分类和面向对象结合方法拓宽了传统逻辑推理分类方法中获取信息的途径,使分类更加自动化和智能化,且分类精度得到显著提高,可以成为海岸带海水养殖复杂模式识别的有效支持手段。Marine aquaculture has developed very rapidly in China and at present, China has become the largest producer of marine aquaculture in the world. While meeting the growing demand for seafood consumption, the mariculture industry also poses serious ecological and environmental problems to the coastal zone. Remote sensing recognition of mariculture modes in coastal zone is of great significance to real-time monitoring, rational planning and orderly development of mariculture, which can help to manage the coastal aquaculture mode, aquaculture structure and aquaculture capacity in coastal zone. At present, there are four main methods for remote sensing identification in aquaculture waters： 1） Extraction by visual interpretation; 2） Extraction by spectral features; 3） Analysis by spatial morphology and structure; 4） Extraction based on Object-oriented techniques. There will have mixing problems caused by ＂different objects with the same spectrum＂, ＂same objects with the different spectrum＂ and salt-and-pepper noise in image processing, if aquaculture information is extracted by spectral information or texture information alone. In order to reduce the interference of human factors of object-oriented classification rules and improve the efficiency and automation of classification rules generation, in this paper, we combined the association rules method and Object-oriented method to build a mariculture modes classifier through automatic and intelligent acquisition for different modes classification. Taking Zhelin Bay in the east of Guangdong province as an example, the GF-1 image as data source, using the spectral, geometric and texture features and their correlations of the objects of different mariculture modes as transaction data, mariculture modes strong rules were mined by Apriori algorithm. Four kinds of mariculture modes information（pond culture, cage culture, beach aquaculture, floating raft） in bay aquaculture core area were extracted. The results showed that pond culture area in Zhelin Bay

关 键 词：遥感 水产养殖 GF-1 海水养殖模式 关联分类规则 面向对象 信息提取 

分 类 号：P715.6[天文地球—海洋科学]