Abstract
Recently, convolutional neural networks have attracted much attention due to its good performance in hyperspectral image classification. However, excessively increasing the depth of the network will lead to overfitting and vanishing gradient. Besides, previous networks rarely consider the related information among different convolution layers. In this paper we propose a hierarchical deep features adaptive fusion network (FAFNet) to address the above two problems. On the one hand, we use dense connectivity to overcome vanishing gradient and overfitting. On the other hand, we adaptively fuse different convolution layers by the learned weights which utilizing softmax to calculate. Experimental results on two well-known datasets demonstrate the excellent performance of the proposed method compared with other state-of-the-art methods.
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Acknowledgments
The authors would like to thank the anonymous referees for their constructive comments which have helped improve the paper. This work was supported by National Natural Science Foundation of China (61502003, 71501002, 61472002, 61671018, 61860206004), by the Key Research Project of Humanities and Social Sciences in Colleges and Universities of Anhui Province under Grant SK2019A0013.
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Sun, Z., Xu, Q., Li, F., Mei, Y., Luo, B. (2020). Hyperspectral Image Classification via Hierarchical Features Adaptive Fusion Network. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_29
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DOI: https://doi.org/10.1007/978-3-030-39431-8_29
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