Ensemble CNN Based on Pixel-Pair and Random Feature Selection for Hyperspectral Image Classification with Small-Size Training Set

2021 
Recently, convolutional neural network (CNN) is widely used in hyperspectral image classification (HSIC) because of its strong self-learning and efficient feature expression ability. However, the CNN model faces the “overfitting” problem when the number of training samples is small. To improve the classification accuracy of CNN under the condition of limited training set, an ensemble CNN method based on pixel-pair and random feature selection (RFS) for HSIC is proposed in this paper. With the purpose of expanding training samples, the pixel-pair feature (PPF) is used in the presented study. Besides, ensemble CNN based on RFS is applied to further improve the classification performance. Experimental results based on two standard hyperspectral images demonstrate that the proposed method achieves better classification performance than the PPF based on CNN (PPF-CNN) and RFS based on SVM (RFS-SVM) methods.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    9
    References
    1
    Citations
    NaN
    KQI
    []