Hyperspectral Image Classification Based on 3-D Separable ResNet and Transfer Learning

Yenan Jiang, Ying Li, Haokui Zhang

科研成果: 期刊稿件文章同行评审

100 引用 (Scopus)

摘要

Deep learning (DL) has proven to be a promising technique for hyperspectral image (HSI) classification. However, due to complex network structure and massive parameters, it is challenging to achieve satisfying classification accuracy with only a small number of training samples. In this letter, we propose a novel HSI classification method by collaborating the 3-D separable ResNet (3-D-SRNet) with cross-sensor transfer learning. The 3-D-SRNet replaces 3-D convolutions with spatial and spectral separable 3-D convolutions, thus showing much less parameters than models that use standard 3-D convolutions. First, we pretrain a classification model with the proposed 3-D-SRNet on the source HSI data set with sufficient training samples compared with the target HSI data set. Then, the pretrained model is transferred to the target HSI data set for fine-tuning to finish the classification task. It is worth noting that the source data for pretraining can be captured by the different sensor with the target data. Compared with the conventional 3-D-ResNet, the proposed 3-D-SRNet has less parameters involving lower computation cost while achieving better classification performance. Experimental results on three benchmark data sets show that our method outperforms several state-of-the-art methods in HSI classification with small training samples.

源语言英语
文章编号8721232
页(从-至)1949-1953
页数5
期刊IEEE Geoscience and Remote Sensing Letters
16
12
DOI
出版状态已出版 - 12月 2019

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