影像分割的城市不透水面信息提取
|
摘要
针对线性光谱混合分解在端元选取中的不足,该文提出了结合影像分割的线性光谱混合分解不透水面估算模型。选取植被、高反射率、低反射率、土壤4种端元,利用线性光谱混合分解和结合影像分割的线性光谱混合分解两种模型,以2010年的TM5遥感影像为数据源对哈尔滨市主城区的不透水面进行估算,并对两种模型进行了对比分析。研究结果表明:线性光谱混合分解和结合影像分割的线性光谱混合分解的平均绝对误差分别为19.84%和14.76%,说明结合影像分割的线性光谱混合分解模型比线性光谱混合分解方法的估算精度高。
To solve the problem of selecting endmember of traditional linear spectral mixture analysis(LSMA),the segment-based linear spectral mixture analysis(S-LSMA)was proposed.Vegetation,high albedo,low albedo,soil were selected as endmember.Both S-LSMA and LSMA were applied to a Landsat TM image acquired in Harbin.According to the contrast and analysis results,it was indicated that the performance of the developed S-LSMA outperformed traditional LSMA techniques with a mean average error(MAE)of 14.76%.The MAE of %ISA was 19.84% with LSMA,which showed a relatively large estimation error.
To solve the problem of selecting endmember of traditional linear spectral mixture analysis(LSMA),the segment-based linear spectral mixture analysis(S-LSMA)was proposed.Vegetation,high albedo,low albedo,soil were selected as endmember.Both S-LSMA and LSMA were applied to a Landsat TM image acquired in Harbin.According to the contrast and analysis results,it was indicated that the performance of the developed S-LSMA outperformed traditional LSMA techniques with a mean average error(MAE)of 14.76%.The MAE of %ISA was 19.84% with LSMA,which showed a relatively large estimation error.
引文
[1]RIDD M K.Exploring a VIS(vegetation-impervious surface-soil)model for urban ecosystem analysis through remote sensing:comparative anatomy for cities[J].International Journal of Remote Sensing,1995,16(12):2165-2185.
[2]SETIAWAN H,MATHIEU R,THOMPSON-FAWCETT M.Assessing the applicability of the V-I-S model to map urban land use in the developing world:case study of Yogyakarta,Indonesia[J].Computers,Environment and Urban Systems,2006,30(4):503-522.
[3]SMALL C.Estimation of urban vegetation abundance by spectral mixture analysis[J].International Journal of Remote Sensing,2001,22(7):1305-1334.
[4]WU C,MURRAY A T.Estimating impervious surface distribution by spectral mixture analysis[J].Remote Sensing of Environment,2003,84(4):493-505.
[5]LU D,WENG Q.Use of impervious surface in urban land-use classification[J].Remote Sensing of Environment,2006,102(1):146-160.
[6]XU H.Modification of normalised difference water index(NDWI)to enhance open water features in remotely sensed imagery[J].International Journal of Remote Sensing,2006,27(14):3025-3033.
[7]刘珍环,王仰麟,彭建.深圳市不透水表面的遥感监测与时空格局[J].地理研究,2012,31(8):1535-1545.(LIU Zhenhuan,WANG Yanglin,PENG Jian.Quantifying spatiotemporal patterns dynamics of impervious surface in Shenzhen[J].Geographical Research,2012,31(8):1535-1545.)
[8]LI M,ZANG S,WU C,et al.Segmentation-based and rule-based spectral mixture analysis for estimating urban imperviousness[J].Advances in Space Research,2015,55(5):1307-1315.
[9]LIU D,XIA F.Assessing object-based classification:advantages and limitations[J].Remote Sensing Letters,2010,1(4):187-194.
[10]DENG C,WU C.A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution[J].Remote Sensing of Environment,2013,133(12):62-70.
[2]SETIAWAN H,MATHIEU R,THOMPSON-FAWCETT M.Assessing the applicability of the V-I-S model to map urban land use in the developing world:case study of Yogyakarta,Indonesia[J].Computers,Environment and Urban Systems,2006,30(4):503-522.
[3]SMALL C.Estimation of urban vegetation abundance by spectral mixture analysis[J].International Journal of Remote Sensing,2001,22(7):1305-1334.
[4]WU C,MURRAY A T.Estimating impervious surface distribution by spectral mixture analysis[J].Remote Sensing of Environment,2003,84(4):493-505.
[5]LU D,WENG Q.Use of impervious surface in urban land-use classification[J].Remote Sensing of Environment,2006,102(1):146-160.
[6]XU H.Modification of normalised difference water index(NDWI)to enhance open water features in remotely sensed imagery[J].International Journal of Remote Sensing,2006,27(14):3025-3033.
[7]刘珍环,王仰麟,彭建.深圳市不透水表面的遥感监测与时空格局[J].地理研究,2012,31(8):1535-1545.(LIU Zhenhuan,WANG Yanglin,PENG Jian.Quantifying spatiotemporal patterns dynamics of impervious surface in Shenzhen[J].Geographical Research,2012,31(8):1535-1545.)
[8]LI M,ZANG S,WU C,et al.Segmentation-based and rule-based spectral mixture analysis for estimating urban imperviousness[J].Advances in Space Research,2015,55(5):1307-1315.
[9]LIU D,XIA F.Assessing object-based classification:advantages and limitations[J].Remote Sensing Letters,2010,1(4):187-194.
[10]DENG C,WU C.A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution[J].Remote Sensing of Environment,2013,133(12):62-70.