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- W2006028195 abstract "High resolution remote sensing imagery can provide more useful information, such as spectral, shape and texture information. However, traditional pixel-based image classification approaches may suffer the increase of within-class spectral variation with improved spatial resolution. This paper presents a novel method which combines the optimal segmentation scale with Bag-of-Visual Words (BOV) representation for object-oriented classification. More precisely, an improved estimation of scale parameter (ESP) tool is adopted to determine the optimal parameters in multi-scale image segmentation. BOV is introduced to construct the midlevel representations instead of low-level features for object description. Then Support vector machine (SVM) is used for classification. And the experiments are conducted on high spatial resolution images to validate the proposed algorithm." @default.
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- W2006028195 date "2015-07-01" @default.
- W2006028195 modified "2023-09-27" @default.
- W2006028195 title "A bag-of-visual words approach based on optimal segmentation scale for high resolution remote sensing image classification" @default.
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- W2006028195 doi "https://doi.org/10.1109/igarss.2015.7325940" @default.
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