Liu C, Hong L, Chu S, et al. A SVM ensemble approach combining pixel-based and object-based features for the classification of high resolution remotely sensed imagery[C]//2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2014: 140-144.

Release time:2018-04-26

Abstract:According to the `salt and pepper' effect of pixel-based multi-feature classification and over-smoothing of ground details of object-based image analysis, in this paper, an approach, which fuses pixel-based features and multi-scale object-based features is proposed to improve the accuracy of image classification. (1) Firstly, mean shift algorithm is used to segment the image to obtain over-segmentation regions. Multi-scale segmentation results are obtained by merging the over-segmentation results. The relation between segmentation scales and classification accuracy on each scale is analyzed, and an optimal scale is found. (2)Secondly, objects' spectral features of the optimal scale, pixel-based spectral features and objects' spectral features of initialization segmentation scale are normalized. (3)Finally, the classification method based on pixel-based and object-based features is implemented by means of support vector machine ( SVM ). The experiment results demonstrate that our method can not only effectively reduce the `salt and pepper' effect of pixel-based algorithm, but also maintain the integrity of the ground objects and preserve details. The classification accuracy of categories that are easily confused (e.g. shadow vs. streets) is also improved.

Translation or Not:no

Links to published journals:https://ieeexplore.ieee.org/abstract/document/6927866