基于关联规则面向对象的海岸带海水养殖模式遥感识别
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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 was 2 228.47 hm2, cage culture area was 111.95 hm2, beach aquaculture t area was 12.95 hm2, floating raft area was 48.34 hm2. Cages in Zhelin Bay were distributed in two regions, one was in the sea area between Suizhou Island and Xuxian Island and Xi'ao Island, the other was located between the northeast corner of Haishan Island and Xizhou Island. Ponds were mainly located in the northern part of the study area in Huanggang town. Beach aquaculture was in the innermost part of Zhelin Bay, near the south side of the pond. Floating rafts were distributed around the cages and tended to be at the side of the bay center. In order to compare the association rules mining with the object-oriented combination method and the traditional methods, K-neighboring object-oriented method and association rule mining method were used respectively for the classification and extraction of marine aquaculture modes. These two classifications were implemented in Envi5.5 and Weka3.7.12 software, respectively. The specific steps were as follows, 300 sample points in Google Earth high-resolution images and field survey samples were chosen, of them, 50% were used as training samples, and the other 50% for test samples. Then, the K-adjacent object-oriented method and association rule mining method were selected respectively for classification. Finally, the classification accuracy of the three methods was compared and evaluated. The classification accuracy of mariculture model extraction based on association-rules object-oriented method was 88.65%, which was 14.38 percent point higher than that of K-adjacent object-oriented classification, and 12.16 percent point higher than that of association rules mining classification. association-rules object-oriented method broadened the access to information in the traditional logical reasoning classification method, made the classification more intelligent, and enhanced the classification speed and algorithm reliability. This method can improve the classification accuracy remarkably, which can be an effective support method for the complex mariculture modes recognition.
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 was 2 228.47 hm2, cage culture area was 111.95 hm2, beach aquaculture t area was 12.95 hm2, floating raft area was 48.34 hm2. Cages in Zhelin Bay were distributed in two regions, one was in the sea area between Suizhou Island and Xuxian Island and Xi'ao Island, the other was located between the northeast corner of Haishan Island and Xizhou Island. Ponds were mainly located in the northern part of the study area in Huanggang town. Beach aquaculture was in the innermost part of Zhelin Bay, near the south side of the pond. Floating rafts were distributed around the cages and tended to be at the side of the bay center. In order to compare the association rules mining with the object-oriented combination method and the traditional methods, K-neighboring object-oriented method and association rule mining method were used respectively for the classification and extraction of marine aquaculture modes. These two classifications were implemented in Envi5.5 and Weka3.7.12 software, respectively. The specific steps were as follows, 300 sample points in Google Earth high-resolution images and field survey samples were chosen, of them, 50% were used as training samples, and the other 50% for test samples. Then, the K-adjacent object-oriented method and association rule mining method were selected respectively for classification. Finally, the classification accuracy of the three methods was compared and evaluated. The classification accuracy of mariculture model extraction based on association-rules object-oriented method was 88.65%, which was 14.38 percent point higher than that of K-adjacent object-oriented classification, and 12.16 percent point higher than that of association rules mining classification. association-rules object-oriented method broadened the access to information in the traditional logical reasoning classification method, made the classification more intelligent, and enhanced the classification speed and algorithm reliability. This method can improve the classification accuracy remarkably, which can be an effective support method for the complex mariculture modes recognition.
引文
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[7]吴岩峻,张京红,田光辉,等.利用遥感技术进行海南省水产养殖调查[J].热带作物学报,2006(2):108-111.Wu Yanjun,Zhang Jinghong,Tian Guanghui,et al.A survey to aquiculture with remote sensing technology in Hainan Province[J].Chinese Journal of Tropical Crops,2006(2):108-111.(in Chinese with English abstract)
[8]马艳娟,赵冬玲,王瑞梅,等.基于ASTER数据的近海水产养殖区提取方法[J].农业工程学报,2010,26(增刊2):120-124.Ma Yanjuan,Zhao Dongling,Wang Ruimei,et al.Offshore aquatic farming areas extraction method based on ASTER data[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2010,26(Supp.2):120-124.(in Chinese with English abstract)
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[11]林桂兰,孙飒梅,曾良杰,等.高分辨率遥感技术在厦门海湾生态环境调查中的应用[J].台湾海峡,2003(2):242-247.Lin Guilan,Sun Samei,Zeng Liangjie,et al.Application of high resolution satellite remote sensing in survey of ecological environment at Xiamen Bay[J].Journal of Oceanography in Taiwan Strait,2003(2):242-247.(in Chinese with English abstract)
[12]初佳兰,赵冬至,张丰收,等.基于卫星遥感的浮筏养殖监测技术初探—以长海县为例[J].海洋环境科学,2008,27(增刊2):35-40.Chu Jialan,Zhao Dongzhi,Zhang Fengshou,et al.Monitor method of rafts cultivation by remote sense:A case of Changhai[J].Marine Environmental Science,2008,27(S2):35-40.(in Chinese with English abstract)
[13]武易天.基于遥感影像的近海岸水产提取方法研究[D].北京:中国科学院大学,2017.Wu Yitian.Research on Coastal Aquaculture Detection Using Remote Sensing Images[D].Beijing:University of Chinese Academy of Sciences,2017.(in Chinese with English abstract)
[14]王晓轩.基于面向对象的海岸带水产养殖模式识别[D].广州:广州大学,2011.Wang Xiaoxuan.Recognition of Aquaculture Types in Coastal Zone Based on Object-oriented Method[D].Guangzhou:Guangzhou university,2011.(in Chinese with English abstract)
[15]关学彬,张翠萍,蒋菊生,等.水产养殖遥感监测及信息自动提取方法研究[J].国土资源遥感,2009(2):41-44.Guan Xuebin,Zhang Cuiping,Jiang Jusheng,et al.Research on coastal aquaculture detection using remote sensing images[J].Remote Sensing for Land&Resources,2009,(2):41-44.(in Chinese with English abstract)
[16]刘鹏,杜云艳.基于遥感案例推理的海岸带养殖信息提取[J].遥感技术与应用,2012,27(6):857-864.Liu Peng,Du Yunyan.A CBR approach for extraction coastal aquaculture area[J].Remote Sensing Technology and Application,2012,27(6):857-864.(in Chinese with English abstract)
[17]Agrawal R,Imielinski T,Swanmi A N.Mining association rules between sets of items in large databases[J].Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data,1993:207-216.
[18]陈云浩,冯通,史培军,等.基于面向对象和规则的遥感影像分类研究[J].武汉大学学报:信息科学,2006(4):316-320.Chen Yunhao,Feng Tong,Shi Peijun,et al.Classification of remote sensing image based on object oriented and class rules[J].Geomatics and Information Science of Wuhan University,2006(4):316-320.(in Chinese with English abstract)
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