Superpixel-based principal component analysis for high resolution remote sensing image classification
- 81 Downloads
In object-based image analysis (OBIA), it is often difficult to select the most useful features from a large number of segment-based information. The problem of choosing superpixel-based features is also very challenging. In order to solve this issue, this paper proposes a principal component analysis (PCA)-based method for superpixel-based classification of high resolution remote sensing imagery. This technique transforms the spectral features of superpixels, and the resulted feature variables are used to train a support vector machine classifier. Experiments based on 4 high resolution multispectral images indicated that although the performance is sensitive to the two parameters, the proposed method can increase classification accuracy effectively.
KeywordsImage classification Superpixel Principal component analysis Feature transform
This work is supported by national natural science foundation of China, under grant of 61701265. The anonymous reviewers are sincerely thanked because of their constructive comments which helped improve the quality of this paper.
- 16.Panboonyuen T, Jitkajornwanich K, Lawawirojwong S, Srestasathiern P, Vateekul P (2017) Road segmentation of remotely-sensed images using deep convolutional neural networks with landscape metrics and conditional random fields. Remote Sens 9(7):680. https://doi.org/10.3390/rs9070680 CrossRefGoogle Scholar
- 19.Su T, Zhang S, Tian Y (2019) Extracting croplands in western Inner Mongolia by using random forest and temporal feature selection. J Spat Sci. https://doi.org/10.1080/14498596.2018.1552542