论文标题
SAR图像更改基于多尺寸胶囊网络的检测
SAR Image Change Detection Based on Multiscale Capsule Network
论文作者
论文摘要
基于卷积神经网络（CNN）的传统合成孔径图像变化检测方法面临斑点噪声和变形灵敏度的挑战。为了减轻这些问题，我们提出了一个多尺寸胶囊网络（MS-CAPSNET），以提取更改和未改变像素之间的区分信息。一方面，使用多尺寸胶囊模块来利用特征的空间关系。因此，可以通过汇总来自不同位置的特征来实现模棱两可的属性。另一方面，自适应融合卷积（AFC）模块是为建议的MS-CAPSNET设计的。可以为主胶囊捕获较高的语义特征。 AFC模块提取的功能可显着提高斑点噪声的鲁棒性。在三个真实的SAR数据集上验证了所提出的MS-CAPSNET的有效性。使用四种最先进方法的比较实验证明了该方法的效率。我们的代码可在https://github.com/summitgao/sar_cd_ms_capsnet上找到。
Traditional synthetic aperture radar image change detection methods based on convolutional neural networks (CNNs) face the challenges of speckle noise and deformation sensitivity. To mitigate these issues, we proposed a Multiscale Capsule Network (Ms-CapsNet) to extract the discriminative information between the changed and unchanged pixels. On the one hand, the multiscale capsule module is employed to exploit the spatial relationship of features. Therefore, equivariant properties can be achieved by aggregating the features from different positions. On the other hand, an adaptive fusion convolution (AFC) module is designed for the proposed Ms-CapsNet. Higher semantic features can be captured for the primary capsules. Feature extracted by the AFC module significantly improves the robustness to speckle noise. The effectiveness of the proposed Ms-CapsNet is verified on three real SAR datasets. The comparison experiments with four state-of-the-art methods demonstrate the efficiency of the proposed method. Our codes are available at https://github.com/summitgao/SAR_CD_MS_CapsNet .
