Please wait a minute...
img

官方微信

遥感技术与应用  2013, Vol. 28 Issue (4): 633-639    DOI: 10.11873/j.issn.1004-0323.2013.4.633
图像与数据处理     
基于模糊子集的土地利用遥感图像模糊规则分类
强振平,狄光智,陈旭
(西南林业大学计算机与信息学院,云南 昆明 650224)
Land Use Classification of Remote Sensing Images Fuzzy Rules based on Fuzzy Subsets
Qiang Zhenping,Di Guangzhi,Chen Xu
(School of Computer and Information,Southwest Forestry University,Kunming 650224 China)
 全文: PDF(3694 KB)  
摘要:

为了较好地处理遥感图像的不确定性或模糊性,提高分类精度,提出了一种基于模糊子集的土地利用遥感图像模糊规则分类方法。将模糊隶属度函数值对应到特定的模糊子集建立模糊规则条件,由样本建立分类规则库,通过计算分类数据规则条件部分与分类规则库中规则条件部分的模糊贴进度进行土地利用分类。结果表明:与传统的最大似然法分类方法相比,基于模糊规则的分类方法在高模糊性数据分类中显著提高了分类精度,在低模糊性数据分类中也能取得与最大似然法近似的结果。

关键词: 模糊子集模糊规则遥感土地利用    
Abstract:

In order to represent vague and imprecise value and improve the classification accuracy of remote sensing images,a fuzzy rule-based classification method was proposed.Firstly,by transforming the fuzzy membership function values into corresponding fuzzy subsets,fuzzy rule conditions were established.And then,the fuzzy rule database was derived from samples.Finally,based on the fuzzy nearness degrees of rule conditions were calculated from classified data and fuzzy rule database,the land use was classified.The experimental results show that the proposed method is able to significantly improve the classification accuracy than the maximum likelihood method while the data contains complex mixture of spatial information.Furthermore,this method can get the approximate results as the maximum likelihood method while the data contains relatively homogeneous spatial information.

Key words: Fuzzy subsets    Fuzzy rules    Remote sensing    Land use
收稿日期: 2012-08-16 出版日期: 2013-08-14
:  TP 751.1  
基金资助:

云南省应用基础研究面上基金资助项目(2011FZ140\,2010CD047)。

通讯作者: 陈旭(1973-),男,云南昆明人,博士,副教授,主要从事遥感图像处理与应用方面的研究。E-mail:chenxu_gis@yahoo.com.cn。    
作者简介: 强振平(1981-),男,甘肃庆阳人,硕士,讲师,主要从事数字图像处理方面的研究。E-mail:zhenpingqiang@gmail.com。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
强振平
狄光智
陈旭

引用本文:

强振平,狄光智,陈旭. 基于模糊子集的土地利用遥感图像模糊规则分类[J]. 遥感技术与应用, 2013, 28(4): 633-639.

Qiang Zhenping,Di Guangzhi,Chen Xu. Land Use Classification of Remote Sensing Images Fuzzy Rules based on Fuzzy Subsets. Remote Sensing Technology and Application, 2013, 28(4): 633-639.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2013.4.633        http://www.rsta.ac.cn/CN/Y2013/V28/I4/633

[1]Mota G L,Feitosa R Q,Coutinho H L,et al.Multitemporal Fuzzy Classification Model based on Class Transition Possibilities[J].ISPRS Journal of Photogrammetry and Remote Sensing,2007,62(3):186-200.

[2]Stavrakoudis D G,Theocharis J B,Zalidis G C.A Boosted Genetic Fuzzy Classifier for Land Cover Classification of Remote Sensing Imagery[J].ISPRS Journal of Photogrammetry and Remote Sensing,2011,66(4):529-544.

[3]Bezdek J C.Fuzzy Mathematics in Pattern Classification[D].Ithaca,New York:Cornell University,1973.

[4]Otukei J R,Blaschke T.Land Cover Change Assessment Using Decision Trees,Support Vector Machines and Maximum Likelihood Classification Algorithms[J].International Journal of Applied Earth Observation and Geoiniformation,2010,125:527-531.

[5]Nikolaos E M,Charalampos A T,Thomas K A,et al.Decision Fusion of GA Self-organizing Neuro-fuzzy Multilayered Classifiers for Land Cover Classification Using Textural and Spectral Features[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(7):2137-2152.

[6]Fisher P F.Remote Sensing of Land Cover Classes as Type 2 Fuzzy Sets[J].Remote Sensing of Environment,2010,114:309-321.

[7]Lucas L A,Centeno T M,Delgado M R.Land Cover Classification based on General Type-2 Fuzzy Classifiers[J].International Journal of Fuzzy Systems,2008,10:207-216.

[8]Sanghamitra B,Ujjwal M,Anirban M.Multiobjective Genetic Clustering for Pixel Classification in Remote Sensing Imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(5):1506-1511.

[9]Hu Rongming,Wei Man,Yang Chengbin,et al.Taking SP-OT5 Remote Sensing Data for Example to Compare Pixel-based and Object-oriented Classification[J].Remote Sensing Technology and Application,2012,27(3):366-371.[胡荣明,魏曼,杨成斌,等.以SPOT5遥感数据为例比较基于像素与面向对象的分类方法[J].遥感技术与应用,2012,27(3):366-371.]

[10]Zadeh L A.Fuzzy Sets[J].Information and Control,1965,8(3):338-353.

[11]András B,Luis S.Fuzzy Rule-based Classification of Remotely Sensed Imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(2):362-374.[12]Benz U,Hofmann P,Willhauck G,et al.Multi-resolution,Object-oriented Fuzzy Analysis of Remote Sensing Data for GIS-ready Information[J].ISPRS Journal of Photogrammetry and Remote Sensing,2004,58:239-258.

[13]Liu J Y,Zhang Z,Xu X,et al.Spatial Patterns and Driving Forces of Land Use Change in China During the Early 21st Century[J].Journal of Geographical Sciences,2010,20:483-494.

[14]Zhang J,Zhang Y.Remote Sensing Research Issues of the National Land Use Change Program of China[J].ISPRS Journal of Photogrammetry and Remote Sensing,2007,62:461-472.

[15]Verburg P H,Veldkamp A,Fresco L O.Simulation of Changes in the Spatial Pattern of Land Use in China[J].Applied Geography,1999,19:211-233.

[16]Hubacek K,Sun L X.A Scenario Analysis of China's Land Use and Land Cover Change:Incorporating Biophysical Information into Input-output Modeling[J].Structural Change and Economic Dynamics,2001,12:367-397.

[17]Yue T X,Fan Z M,Liu J Y.Scenarios of Land Cover in China[J].Global and Planetary Change,2007,55:317-342.

[18]Chen X,Tateishi R,Wang C.Development of a 1-km Landcover Dataset of China Using AVHRR Data[J].ISPRS Journal of Photogrammetry and Remote Sensing,1999,54:305-316.

[19]Liu J Y,Zhuang D F,Luo D,et al.Land-cover Classification of China:Integrated Analysis of AVHRR Imagery and Geophysical Data[J].International Journal of Remote Sensing.2003,24(12):2485-2500.

[20]Pan Y Z,Li X B,Gong P,et al.An Integrative Classification of Vegetation in China based on NOAA AVHRR and Vegetation-climate Indices of the Holdridge Life Zone[J].International Journal of Remote Sensing,2003,24(5):1009-1027.

[21]Ma M G,Veroustraete F.Reconstructing Pathfinder AVHRR Land NDVI Time Series Data for the Northwest of China[J].Advances in Space Research,2006,37:835-840.

[22]Holben B N.Characteristics of Maximum Value Composite Images from Temporal AVHRR Data[J].International Journal of Remote Sensing,1986,7(11):1417-1434.

[1] 王卷乐, 程凯, 边玲玲, 韩雪华, 王明明. 面向SDGs和美丽中国评价的地球大数据集成框架与关键技术[J]. 遥感技术与应用, 2018, 33(5): 775-783.
[2] 王恺宁,王修信,黄凤荣,罗涟玲. 喀斯特城市地表温度遥感反演算法比较[J]. 遥感技术与应用, 2018, 33(5): 803-810.
[3] 张晓峰,吕晓琪,张信雪,张继凯,王月明,谷宇,樊宇. 多时刻海色遥感数据融合及其可视化[J]. 遥感技术与应用, 2018, 33(5): 873-880.
[4] 谢旭,陈芸芝. 基于PSO-RBF神经网络模型反演闽江下游水体悬浮物浓度[J]. 遥感技术与应用, 2018, 33(5): 900-907.
[5] 迟文峰,匡文慧,贾静,刘正佳. 京津风沙源治理工程区LUCC及土壤风蚀强度动态遥感监测研究[J]. 遥感技术与应用, 2018, 33(5): 965-974.
[6] 胡云锋,商令杰,张千力,王召海. 基于GEE平台的1990年以来北京市土地变化格局及驱动机制分析[J]. 遥感技术与应用, 2018, 33(4): 573-583.
[7] 李晨伟,张瑞丝,张竹桐,曾敏 . 基于多源遥感数据的构造解译与分析—以西藏察隅吉太曲流域为例[J]. 遥感技术与应用, 2018, 33(4): 657-665.
[8] 李生生,王广军,梁四海,彭红明,董高峰,罗银飞. 基于Landsat-8 OLI数据的青海湖水体边界自动提取[J]. 遥感技术与应用, 2018, 33(4): 666-675.
[9] 廖凯涛,齐述华,王成,王点. 结合GLAS和TM卫星数据的江西省森林高度和生物量制图[J]. 遥感技术与应用, 2018, 33(4): 713-720.
[10] 张震,刘时银,魏俊锋,蒋宗立. 1974~2012年珠穆朗玛峰地区冰川物质平衡遥感监测研究[J]. 遥感技术与应用, 2018, 33(4): 731-740.
[11] 王琳,徐涵秋,李胜. 重钢重工业区迁移对区域生态的影响研究[J]. 遥感技术与应用, 2018, 33(3): 387-397.
[12] 任浙豪,周坚华. 增大特征空间复杂度的方法——以城镇下垫面遥感分类为[J]. 遥感技术与应用, 2018, 33(3): 408-417.
[13] 王宝刚,晋锐,赵泽斌,亢健. 被动微波遥感在地表冻融监测中的应用研究进展[J]. 遥感技术与应用, 2018, 33(2): 193-201.
[14] 秦振涛,杨茹,张靖,杨武年. 基于聚类结构自适应稀疏表示的高光谱遥感图像修复研究[J]. 遥感技术与应用, 2018, 33(2): 212-215.
[15] 郭宇柏,卓莉,陶海燕,曹晶晶,王芳. 基于空谱初始化的非负矩阵光谱混合像元盲分解[J]. 遥感技术与应用, 2018, 33(2): 216-226.