基于纹理和空间特征的中分辨率影像滨海水产养殖用地提取研究
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摘要
近年来,水产养殖用地分布广泛,但由于其在影像上所表现的复杂性和不均匀性,造成该用地类型提取中的困难,尤其针对中分辨率遥感影像。对此,本文提出了一种基于纹理和空间特征的养殖用地提取方法,该方法主要包括3个步骤:首先,利用纹理熵和归一化差异水体指数NDWI实现水产养殖用地的粗提取;然后,依据相邻地物间的关系实现同类型地物合并;最后,本文构建一种相对宽度作为地物的近似宽度,再次利用NDWI实现水产养殖用地的准确识别。本文以越南万丰湾为研究区域,以Landat-8融合影像(融合后的像元大小为15 m)的目视解译结果为标准,对本文方法与最小距离法分类结果进行比较。实验结果表明,该方法的精度可达91.13%,远高于传统的面向对象方法,并且所提方法的错误率和虚假率分别为0.09%和8.87%,表明了该方法可靠性,因而该方法可为基于中分辨率影像的地物类型提取提供一种有效手段。
Aquaculture land has become widely distributed in recent years. It's difficult to extract aquaculture land for its complexity and inhomogeneity, especially from the medium spatial resolution satellite images. In this paper, we present an automated method containing three main steps to extract aquaculture land from medium spatial resolution images. First, the texture entropy and Normalized Difference Water Index(NDWI) were used to extracted aquaculture land initially. Then, the interrelations of neighboring objects were utilized to merge objects.Finally, a relative new feature called relative width was proposed, and the NDWI was used again to separate the aquaculture land accurately. This method was applied to Van fong Bay, Vietnam using Landsat-8 images whith pixel size of 15 m after image fusion. Manual interpretation was conducted in the same region to support and validate our results of the minimum distance method. The result shows that the precision of the proposed method is 91.13%, which is far higher than the traditional object-oriented method. And the missing rate and false rate of the proposed method are 0.09% and 8.87%, respectively, indicating that the proposed method is reliable. This method provides an accurate and efficient means for fast land use mapping from medium resolution imagery.
Aquaculture land has become widely distributed in recent years. It's difficult to extract aquaculture land for its complexity and inhomogeneity, especially from the medium spatial resolution satellite images. In this paper, we present an automated method containing three main steps to extract aquaculture land from medium spatial resolution images. First, the texture entropy and Normalized Difference Water Index(NDWI) were used to extracted aquaculture land initially. Then, the interrelations of neighboring objects were utilized to merge objects.Finally, a relative new feature called relative width was proposed, and the NDWI was used again to separate the aquaculture land accurately. This method was applied to Van fong Bay, Vietnam using Landsat-8 images whith pixel size of 15 m after image fusion. Manual interpretation was conducted in the same region to support and validate our results of the minimum distance method. The result shows that the precision of the proposed method is 91.13%, which is far higher than the traditional object-oriented method. And the missing rate and false rate of the proposed method are 0.09% and 8.87%, respectively, indicating that the proposed method is reliable. This method provides an accurate and efficient means for fast land use mapping from medium resolution imagery.
引文
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[20]Hussain E,Shan J.Object-based urban land cover classification using rule inheritance over very high-resolution multisensor and multitemporal data[J].Mapping Sciences&Remote Sensing,2016,53(2):164-182.
[21]Zhang C.Multiscale quantification of urban composition from EO-1/Hyperion data using object-based spectral unmixing[J].International Journal of Applied Earth Observation&Geoinformation,2016,47:153-162.
[22]Shirke S,Pinto S M,Kushwaha V K,et al.Object-based image analysis for the impact of sewage pollution in Malad Creek,Mumbai,India.[J].Environmental Monitoring&Assessment,2016,188(2):95.
[23]孙晓宇,苏奋振,周成虎,等.基于RS与GIS的珠江口养殖用地时空变化分析[J].资源科学,2010,32(1):71-77.[Sun X,Fenzhen S U,Zhou C,et al.Analyses on spatialtemporal changes in aquaculture land in coastal areas of the pearl river estuarine[J].Resources Science,2010,32(1):71-77.]
[24]Singh S K,Srivastava P K,Gupta M,et al.Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine[J].Environmental Earth Sciences,2014,71(5):2245-2255.
[25]Wang C Y,Wang A L,Wang J Y,et al.Coastal zone land use information extraction based on objectoriented classification method[J].Journal of Natural Resources,2014,29(9):1589-1597.
[26]Son N T,Chen C F,Chang N B,et al.Mangrove mapping and change detection in Ca Mau Peninsula,Vietnam,using landsat data and object-based image analysis[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2015,8(2):503-510.
[27]Benz U C,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&Remote Sensing,2004,58(3):239-258.
[28]Desheng Liu,Fan Xia.Assessing object-based classification:Advantages and limitations[J].Remote Sensing Letters,2009,1(4):187-194.
[29]Haralick R M,Shanmugam K,Dinstein I.Textural Features for Image Classification[J].Systems Man&Cybernetics IEEE Transactions on,1973,3(6):610-621.
[30]Van Genderen J,Marghany M.Entropy algorithm for automatic detection of oil spill from radarsat-2 SAR data[J]Marine Pollution Bulletin,2014,18(1):1475-1480.
[31]Falahatkar S,Hosseini S M,Ayoubi S,et al.Predicting soil organic carbon density using auxiliary environmental variables in northern Iran[J].Archives of Agronomy&Soil Science,2016,62(3):375-393.
[32]Eswar R,Sekhar M,Bhattacharya B K.Disaggregation of LST over India:comparative analysis of different vegetation indices[J].International Journal of Remote Sensing,2016,37(5):1035-1054.
[33]Choung Y J,Jo M H.Shoreline change assessment for various types of coasts using multi-temporal Landsat imagery of the east coast of South Korea[J].Remote Sensing Letters,2016,7(1):91-100.
[34]Gao B C.NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space[J].Remote Sensing of Environment,1996,58(3):257-266.
[35]Su B,Noguchi N.Discrimination of land use patterns in remote sensing image data using minimum distance algorithm and watershed algorithm[J].Engineering in Agriculture Environment&Food,2013,6(2):48-53.
[36]Salas E,Valdez R,Boykin K.Geographic layers as landscape drivers for the marco polo argali habitat in the southeastern pamir mountains of Tajikistan[J].ISPRS International Journal of Geo-Information,2015,4(4):2094-2108.
[2]Ottinger M,Clauss K,Kuenzer C.Aquaculture:Relevance,distribution,impacts and spatial assessments:A review[J].Ocean&Coastal Management,2016,119(6):244-266.
[3]Zhang T,Li Q,Yang X,et al.Automatic mapping aquaculture in coastal zone from TM imagery with OBIA approach[C]//International Conference on Geoinformatics.IEEE,2010:1-4.
[4]MathiesenáM.The state of world fisheries and aquaculture[J].Rome Italy Fao,2010,4(1):40-41.
[5]Hamilton S E,Lovette J.Ecuador's mangrove forest carbon stocks:A spatiotemporal analysis of living carbon holdings and their depletion since the advent of commercial aquaculture.Plo S One,2015,10(3):e0118880.
[6]Tran H,Tran T,Kervyn M.Dynamics of land cover/land use changes in the mekong delta,1973-2011:A remote sensing analysis of the tran van thoi district,Ca Mau province,Vietnam[J].Remote Sensing,2015,7(3):2899-2925.
[7]Vo T Q,Kuenzer C,Oppelt N.How remote sensing supports mangrove ecosystem service valuation:A case study in Ca Mau province,Vietnam[J].Ecosystem Services,2015,14:67-75.
[8]Vo Q T,Oppelt N,Leinenkugel P,et al.Remote sensing in mapping mangrove ecosystems:An Object-based approach[J].Remote Sensing,2013,5(1):183-201.
[9]Liu Y,Saitoh S I,Nakada S,et al.Impact of oceanographic environmental shifts and atmospheric events on the sustainable development of coastal aquaculture:A case study of kelp and scallops in Southern Hokkaido,Japan[J].Sustainability,2015,7(2):1263-1279.
[10]Virdis S G P.An object-based image analysis approach for aquaculture ponds precise mapping and monitoring:A case study of Tam Giang-Cau Hai Lagoon,Vietnam[J].Environmental Monitoring and Assessment,2014,186(1):117-133.
[11]Chang B,Li R,Zhu C,et al.Quantitative impacts of climate change and human activities on water-surface area variations from the 1990s to 2013 in Honghu Lake,China[J].Water,2015,7(6):2881-2899.
[12]Lu Y,Li Q,Du X,et al.A method of coastal aquaculture area automatic extraction with high spatial resolution images[J].Remote Sensing Technology and Application,2015,30(3):486-494.
[13]刘俊霞,马毅,李晓敏,等.基于国产高分影像的海岸带盐田和水产养殖区图谱特征分析[J].海洋科学,2015,39(2):63-66.[Liu J X,Ma Y,Li X M,et al.TUPU characteristic analysis of the salt fields and aquaculture areas in coastal zone based on domestic high spatial resolution satellite images.Marine Sciences,2015,39(2):63.
[14]Yun Y D,Wu D,Liang F Y,et al.Integration of casebased reasoning and object-based image classification to classify SPOT images:A case study of aquaculture land use mapping in coastal areas of Guangdong province,China[J].Mapping Sciences&Remote Sensing,2013,50(5):574-589.
[15]Meng Q W.Monitoring drought in mountainous area based on temperature/vegetation dryness index(TVDI)[J].Arid Land Geography,2007.Jayanthi M.Monitoring brackishwater aquaculture development using multi-spectral satellite data and GIS:A case study near Pichavaram mangroves south-east coast of India[J].Indian Journal of Fisheries,2011,58(1):85-90.
[16]Jayanthi M.Monitoring brackishwater aquaculture development using multi-spectral satellite data and GIS:A case study near Pichavaram mangroves south-east coast of India.Indian Journal of Fisheries,2011,58(1):85-90.
[17]周小成,汪小钦,向天梁,等.基于ASTER影像的近海水产养殖信息自动提取方法[J].湿地科学,2006,4(1):64-68.[Zhou X C,Wang X Q,Xiang T L,et al.Method of automatic extracting seaside aquaculture land based on ASTER remote sensing image[J].Wetland Science,2006,4(1):64-68.]
[18]Junjie L I,Longhua H E,Dai J A,et al.Extract enclosure culture in lakes based on remote sensing image texture information[J].Journal of Lake Sciences,2006,18(4):337-342.
[19]Salas E,Boykin K,Valdez R.Multispectral and texture feature application in image-object analysis of summer Vegetation in Eastern Tajikistan Pamirs[J].Remote Sensing,2016,8(1):78.
[20]Hussain E,Shan J.Object-based urban land cover classification using rule inheritance over very high-resolution multisensor and multitemporal data[J].Mapping Sciences&Remote Sensing,2016,53(2):164-182.
[21]Zhang C.Multiscale quantification of urban composition from EO-1/Hyperion data using object-based spectral unmixing[J].International Journal of Applied Earth Observation&Geoinformation,2016,47:153-162.
[22]Shirke S,Pinto S M,Kushwaha V K,et al.Object-based image analysis for the impact of sewage pollution in Malad Creek,Mumbai,India.[J].Environmental Monitoring&Assessment,2016,188(2):95.
[23]孙晓宇,苏奋振,周成虎,等.基于RS与GIS的珠江口养殖用地时空变化分析[J].资源科学,2010,32(1):71-77.[Sun X,Fenzhen S U,Zhou C,et al.Analyses on spatialtemporal changes in aquaculture land in coastal areas of the pearl river estuarine[J].Resources Science,2010,32(1):71-77.]
[24]Singh S K,Srivastava P K,Gupta M,et al.Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine[J].Environmental Earth Sciences,2014,71(5):2245-2255.
[25]Wang C Y,Wang A L,Wang J Y,et al.Coastal zone land use information extraction based on objectoriented classification method[J].Journal of Natural Resources,2014,29(9):1589-1597.
[26]Son N T,Chen C F,Chang N B,et al.Mangrove mapping and change detection in Ca Mau Peninsula,Vietnam,using landsat data and object-based image analysis[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2015,8(2):503-510.
[27]Benz U C,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&Remote Sensing,2004,58(3):239-258.
[28]Desheng Liu,Fan Xia.Assessing object-based classification:Advantages and limitations[J].Remote Sensing Letters,2009,1(4):187-194.
[29]Haralick R M,Shanmugam K,Dinstein I.Textural Features for Image Classification[J].Systems Man&Cybernetics IEEE Transactions on,1973,3(6):610-621.
[30]Van Genderen J,Marghany M.Entropy algorithm for automatic detection of oil spill from radarsat-2 SAR data[J]Marine Pollution Bulletin,2014,18(1):1475-1480.
[31]Falahatkar S,Hosseini S M,Ayoubi S,et al.Predicting soil organic carbon density using auxiliary environmental variables in northern Iran[J].Archives of Agronomy&Soil Science,2016,62(3):375-393.
[32]Eswar R,Sekhar M,Bhattacharya B K.Disaggregation of LST over India:comparative analysis of different vegetation indices[J].International Journal of Remote Sensing,2016,37(5):1035-1054.
[33]Choung Y J,Jo M H.Shoreline change assessment for various types of coasts using multi-temporal Landsat imagery of the east coast of South Korea[J].Remote Sensing Letters,2016,7(1):91-100.
[34]Gao B C.NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space[J].Remote Sensing of Environment,1996,58(3):257-266.
[35]Su B,Noguchi N.Discrimination of land use patterns in remote sensing image data using minimum distance algorithm and watershed algorithm[J].Engineering in Agriculture Environment&Food,2013,6(2):48-53.
[36]Salas E,Valdez R,Boykin K.Geographic layers as landscape drivers for the marco polo argali habitat in the southeastern pamir mountains of Tajikistan[J].ISPRS International Journal of Geo-Information,2015,4(4):2094-2108.