SUPERPIXEL CLASSIFICATION OF HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGE BASED ON MULTI-SCALE CNN AND SCALE PARAMETER ESTIMATION

Chen, Y.; Ming, D.

In recent years, considerable attention has been paid to integrate convolutional neural network (CNN) with land cover classification of high spatial resolution remote sensing image. Per-pixel classification method based on CNN (Per-pixel CNN) achieved higher accuracy with the help of high-level features, however, this method still has limitations. Even though per-superpixel classification method based on CNN (Per-superpixel CNN) overcome the limitations of per-pixel CNN, classification accuracy of complex urban is easily influenced by scale effect. To solve this issue, superpixel classification method combining multi-scale CNN (Per-superpixel MCNN) method is proposed. Besides, this paper proposes a novel spatial statistics based method to estimate applicable scale parameter of per-superpixel CNN. Experiments using proposed method were performed on Digital Orthophoto Quarer Quad (DOQQ) images in urban and suburban area. Classification results show that per-superpixel MCNN can effectively avoid misclassification in complex urban area compared with per-superpixel classification method combining single-scale CNN (Per-superpixel SCNN). Series of classification results also show that using the pre-estimated scale parameter can guarantee high classification accuracy, thus arbitrary nature of scale estimation can be avoided to some extent.

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Zitierform:

Chen, Y. / Ming, D.: SUPERPIXEL CLASSIFICATION OF HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGE BASED ON MULTI-SCALE CNN AND SCALE PARAMETER ESTIMATION. 2019. Copernicus Publications.

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Rechteinhaber: Y. Chen

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