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Exploring the Scale Effect in Thematic Classification of Remotely Sensed Data:the Statistical Separability-based Method |
BO Yan-chen1, WANG Jin-feng2 |
(1.Research Center for Remote Sensing and GIS,School of Geography,Beijing Normal University,Beijing100875,China; 2.LREIS,Institute of Geographical Science and Natural Resources Research,Chinese Academy of Sciences,Beijing100101,China) |
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Abstract The statistical separability is used to explore the scale effect of remote sensing data classification and to determine optimal resolution in this paper. The Landsat TM image with 30m spatial resolution is up-scaled to different spatial resolutions. The stratified random sampling method was used to select the training samples at 30m resolution, and the location of training samples were saved as masks to take training samples for up-scaled images so that training samples for images at every resolution are at same location. The transformed divergence and J-M distance of training samples at every resolution were calculated for every class pair, and were plotted versus the spatial resolution. The landscape metrics of the land cover in the study area were calculated Analysis to these plots showed that, for different pair of classes, the change pattern of statistical separability with spatial resolution is quite different. The spatial pattern between pair of classes has significant effect on the statistical separability pattern of change with spatial resolution and can be used to explain the underlying reasons for the change patterns. For our experimental data, the average statistical separability reached to the maximum at the 60m spatial resolution, which means that finer spatial resolution not necessary lead to high separability. :
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Received: 21 June 2004
Published: 26 December 2011
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