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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Change Detection From Synthetic Aperture Radar Images Based on Channel Weighting-Based Deep Cascade Network
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Change Detection From Synthetic Aperture Radar Images Based on Channel Weighting-Based Deep Cascade Network

机译：基于信道加权的深级级级联网络从合成孔径雷达图像的变化检测

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摘要

Deep learning methods have recently demonstrated their significant capability for synthetic aperture radar (SAR) image change detection. However, with the increase of network depth, convolutional neural networks often encounter some negative effects, such as overfitting and exploding gradients. In addition, the existing deep networks employed in SAR change detection tend to produce a lot of redundant features that affect the performance of the network. To solve the aforementioned problems, this article proposed a deep cascade network (DCNet) for SAR image change detection. On the one hand, a very DCNet is established to exploit discriminative features, and residual learning is introduced to solve the exploding gradients problem. In addition, a fusion mechanism is employed to combine the outputs of different hierarchical layers to further alleviate the exploding gradient problem. Moreover, a simple yet effective channel weighting-based module is designed for SAR change detection. Average pooling and max pooling are used to aggregate channel-wise information. Meaningful channel-wise features are emphasized and unnecessary ones are suppressed. Therefore, the similarity in feature maps can be reduced, and then, the classification performance of the DCNet is improved. Experimental results on four real SAR datasets demonstrated that the proposed DCNet can obtain better change detection performance than several competitive methods. Our codes are available at https://github.com/summitgao/SAR_CD_DCNet.
机译：最近，深入学习方法最近证明了它们对合成孔径雷达（SAR）图像变化检测的显着能力。然而，随着网络深度的增加，卷积神经网络经常遇到一些负面影响，例如过度装箱和爆炸梯度。此外，SAR变更检测中使用的现有深网络倾向于产生影响网络性能的大量冗余功能。为了解决上述问题，本文提出了一个用于SAR图像变化检测的深层级联网络（DCNet）。一方面，建立一个非常DCNet来利用歧视特征，并引入剩余学习以解决爆炸梯度问题。另外，采用融合机制来组合不同层次层的输出，以进一步缓解爆炸梯度问题。此外，设计了一个简单但有效的信道加权模块，用于SAR变化检测。平均池和最大池用于聚合通道明智的信息。强调有意义的频道明智的功能，并且抑制了不必要的渠道。因此，可以减少特征映射的相似性，然后，改进了DCNet的分类性能。 4个真实SAR数据集上的实验结果表明，所提出的DCNet可以比几种竞争方法获得更好的变化检测性能。我们的代码可在https://github.com/summitgao/sar_cd_dcnet上获得。

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