浏览全部资源
扫码关注微信
纸质出版日期: 2015 ,
扫 描 看 全 文
[1]HU Yong,LIU Liangyun,CACCETTA Peter,JIAO Quanjun.Landsat time-series land cover mapping with spectral signature extension method[J].遥感学报,2015,19(04):639-647.
HU Yong, LIU Liangyun, CACCETTA Peter, et al. Landsat time-series land cover mapping with spectral signature extension method[J]. Journal of Remote Sensing, 2015,19(4):639-647.
Time-series remote sensing images were previously employed to detect land use and land-cover changes and to analyze related trends. However
land-cover change mapping using time-series remote sensing data
especially medium-resolution imagery
was often constrained by a lack of high-quality training and validation data
especially for historical satellite images. In this study
we tested and evaluated a generalized classifier for time series Landsat Thematic Mapper( TM) imagery based on spectral signature extension. First
a new atmospheric correction procedure and a robust relative normalization method were performed on time-series images to eliminate the radiometric differences between them and to retrieve the surface reflectance. Second
we selected one surface reflectance image from the time series as a source image based on the availability of reliable ground truth data. The spectral signature was then extracted from the training data and the source image. Third
the spectral signature was extended to all the corrected time-series images to build a generalized classifier. This method was tested on a time series consisting of five Landsat TM images of the Tibetan Plateau
and the results showed that the corrected time-series images could be classified effectively from the reference image using the generalized classifier. The overall accuracy achieved was between 88. 35% and 94. 25%
which is comparable with the results obtained using traditional scene-by-scene supervised classification. Results also showed that the performance of the extension method was affected by the difference in acquisition times of the source image and target image.
Time-series remote sensing images were previously employed to detect land use and land-cover changes and to analyze related trends. However
land-cover change mapping using time-series remote sensing data
especially medium-resolution imagery
was often constrained by a lack of high-quality training and validation data
especially for historical satellite images. In this study
we tested and evaluated a generalized classifier for time series Landsat Thematic Mapper( TM) imagery based on spectral signature extension. First
a new atmospheric correction procedure and a robust relative normalization method were performed on time-series images to eliminate the radiometric differences between them and to retrieve the surface reflectance. Second
we selected one surface reflectance image from the time series as a source image based on the availability of reliable ground truth data. The spectral signature was then extracted from the training data and the source image. Third
the spectral signature was extended to all the corrected time-series images to build a generalized classifier. This method was tested on a time series consisting of five Landsat TM images of the Tibetan Plateau
and the results showed that the corrected time-series images could be classified effectively from the reference image using the generalized classifier. The overall accuracy achieved was between 88. 35% and 94. 25%
which is comparable with the results obtained using traditional scene-by-scene supervised classification. Results also showed that the performance of the extension method was affected by the difference in acquisition times of the source image and target image.
相关文章
相关作者
相关机构