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REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (3) : 171-175     DOI: 10.6046/gtzyyg.2017.03.25
Urban features classification based on objects segmentation and hyperspectral characteristics
SUN Xiaofang
Department of Geography, Minjiang College, Fuzhou 350121, China
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Abstract  Urban features classification is based on hyperspectral characteristics and high-resolution image segmentation objects. After the removal of bad lines and Smile effect, FLAASH atmospheric correction and 155 Hyperion bands were used in this study. Spectrum feature was used to determine objects recognition suitable spectral resolution, and after Hyperion dimensional reduction, 21 wide-bands were generated. Utility wavelet fusion was performed, and IKONOS high-resolution objects were generated by multi-resolution segmentation. On the basis of hierarchical analysis classification method for segmentation objects, fuzzy membership function of the vegetation red edge effect and the water absorption characteristics in the near infrared were used to complete first level classification. The larger distance of 10 Hyperion bands was used as feature bands, and the second level classification was completed by standard nearest neighbor classification. 9 types of urban features were separated. The classification results are better than the maximum likelihood classification and spectral angle mapper.
Keywords multi-scale      sparse decomposition      dictionary      remote sensing image      fusion     
Issue Date: 15 August 2017
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XU Jindong
NI Mengying
TONG Xiangrong
ZHANG Yanjie
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XU Jindong,NI Mengying,TONG Xiangrong, et al. Urban features classification based on objects segmentation and hyperspectral characteristics[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 171-175.
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[1] 潘灼坤,王 芳,夏丽华,等.高光谱遥感城市植被胁迫监测研究[J].遥感技术与应用,2012,27(1):68-76.
Pan Z K,Wang F,Xia L H,et al.Research on urban vegetation stress monitoring by hyperspectral remote sensing[J].Remote Sensing Technology and Application,2012,27(1):68-76.
[2] Zhang C Y,Xie Z X.Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery[J].Remote Sensing of Environment,2012,124:310-320.
[3] Roberts D A,Quattrochi D A,Hulley G C,et al.Synergies between VSWIR and TIR data for the urban environment:An evaluation of the potential for the Hyperspectral Infrared Imager(HyspIRI) Decadal Survey mission[J].Remote Sensing of Environment,2012,117:83-101.
[4] 龚建周,陈健飞,刘彦随.基于EO-1 Hyperion影像地物识别与分类不同方法的效果比较[J].应用基础与工程科学学报,2013,21(3):453-462.
Gong J Z,Chen J F,Liu Y S.Comparison of land use identification and classification using different models for EO-1 Hyperion images[J].Journal of Basic Science and Engineering,2013,21(3):453-462.
[5] 余先川,安卫杰,贺 辉.基于面向对象的无监督分类的遥感影像自动分类方法[J].地球物理学进展,2012,27(2):744-749.
Yu X C,An W J,He H.A method of auto classification based on object oriented unsupervised classification[J].Progress in Geophysics,2012,27(2):744-749.
[6] Petropoulos G P,Kalaitzidis C,Vadrevu K P.Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery[J].Computers and Geosciences,2012,41:99-107.
[7] Pu R L,Landry S.A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species[J].Remote Sensing of Environment,2012,124:516-533.
[8] Huang X,Lu Q K,Zhang L P.A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,90:36-48.
[9] 谭炳香,李增元,陈尔学,等.EO-1 Hyperion高光谱数据的预处理[J].遥感信息,2005(6):36-41.
Tan B X,Li Z Y,Chen E X,et al.Preprocessing of EO-1 Hyperion hyperspectral data[J].Remote Sensing Information,2005(6):36-41.
[10] Roshan-Chhetri P,Abd-Elrahman A.De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering[J].ISPRS Journal of Photogrammetry and Remote Sensing,2011,66(5):620-636.
[11] 乔振民,邢立新,李淼淼,等.Hyperion数据玉米叶绿素含量制图[J].遥感技术与应用,2012,27(2):275-281.
Qiao Z M,Xing L X,Li M M,et al.Mapping of maize Chlorophyll content with Hyperion data[J].Remote Sensing Technology and Application,2012,27(2):275-281.
[12] 龚建周,陈健飞,刘彦随.EO-1 Hyperion高光谱影像的FLAASH大气校正与评价[J].广州大学学报(自然科学版),2011,10(5):69-75.
Gong J Z,Chen J F,Liu Y S.Atmospheric correction and evaluation for EO-1 Hyperion images based on FLAASH model[J].Journal of Guangzhou University(Natural Science Edition),2011,10(5):69-75.
[13] Pu R L,Gong P.Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping[J].Remote Sensing of Environment,2004,91(2):212-224.
[14] Hsu P H.Feature extraction of hyperspectral images using wavelet and matching pursuit[J].ISPRS Journal of Photogrammetry and Remote Sensing,2007,62(1):78-92.
[15] Gmb H.Ecognition User Guide[M].Germany:Definients Image Company,2004:110-125.
[16] 王 露.面向对象的高分辨率遥感影像多尺度分割参数及分类研究[D].长沙:中南大学,2014.
Wang L.Analysing Classification and Segmentation Parameters Selection in High Resolution Remote Sensing Image Using Based on Object[D].Changsha:Central South University,2014.
[17] 王 雪,李培军,姜莎莎,等.利用机载LiDAR数据和高分辨率图像提取复杂城区建筑物[J].国土资源遥感,2016,28(2):106-111.doi:10.6046/gtzyyg.2016.02.17"> doi:10.6046/gtzyyg.2016.02.17.
Wang X,Li P J,Jiang S S,et al.Building extraction using airborne LiDAR data and very high resolution imagery over a complex urban area[J].Remote Sensing for Land and Resources,2016,28(2):106-111.doi:10.6046/gtzyyg.2016.02.17"> doi:10.6046/gtzyyg.2016.02.17.
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