Hyperspectral Image Classification With Deep Feature Fusion Network
Hyperspectral Image Classification With Deep Feature Fusion Network
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DOI：
10.1109/tgrs.2018.2794326
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发表时间：
2018-06-01
影响因子：
8.2
通讯作者：
Lu, Ting
中科院分区：
文献类型：
--
作者：
Song, Weiwei;Li, Shutao;Lu, Ting
最近，深度学习被引入到高光谱图像（HSI）分类中，并取得了良好的性能。一般来说，深度模型采用大量的分层来提取特征。然而，过度增加网络深度将导致一些负面影响（例如，过拟合、梯度消失和精度降低）。此外，以往的HSI分类网络没有考虑不同层次之间强互补但相关的信息。为了解决上述两个问题，提出了一种用于HSI分类的深度特征融合网络（DFFN）。一方面，引入残差学习来优化几个卷积层作为身份映射，这可以简化深度网络的训练并受益于增加深度。因此，我们可以构建一个非常深的网络来提取HSI的更具鉴别力的特征。另一方面，所提出的DFFN模型融合了不同层次的输出，这可以进一步提高分类精度。在三个真实的HSI上的实验结果表明，该方法优于其他竞争性分类器。
Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and achieved good performance. In general, deep models adopt a large number of hierarchical layers to extract features. However, excessively increasing network depth will result in some negative effects (e.g., overfitting, gradient vanishing, and accuracy degrading) for conventional convolutional neural networks. In addition, the previous networks used in HSI classification do not consider the strong complementary yet correlated information among different hierarchical layers. To address the above two issues, a deep feature fusion network (DFFN) is proposed for HSI classification. On the one hand, the residual learning is introduced to optimize several convolutional layers as the identity mapping, which can ease the training of deep network and benefit from increasing depth. As a result, we can build a very deep network to extract more discriminative features of HSIs. On the other hand, the proposed DFFN model fuses the outputs of different hierarchical layers, which can further improve the classification accuracy. Experimental results on three real HSIs demonstrate that the proposed method outperforms other competitive classifiers.