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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >High-Order Generalized Orderless Pooling Networks for Synthetic-Aperture Radar Scene Classification
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High-Order Generalized Orderless Pooling Networks for Synthetic-Aperture Radar Scene Classification

机译：合成孔径雷达场景分类的高阶广义无序合并网络

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摘要

Fixed coding style in bag of visual words (BOVW) model and strong spatial information in convolutional neural network (CNN) feature representation make the feature vector less adaptable for scene classification. With the purpose of extracting the learnable orderless feature for SAR scene classification, the high-order generalized orderless pooling network trained by backpropagation is proposed for learning the high-order vector of locally aggregated descriptors (VLADs) and locality constrained affine subspace coding (LASC), compared with the first-order feature coding style, the proposed network could learn high-order coding features by outer product automatically. Subsequently, for making the feature representation more powerful, the matrix normalization (square root) whose gradients are computed via singular value decomposition (SVD) and elementwise normalization are introduced into the proposed network. Finally, experiments on the SAR scene classification data set from TerraSAR-X image show the proposed networks achieve better performance than the state-of-the-art approaches.
机译：视觉单词袋（BOVW）模型中的固定编码样式以及卷积神经网络（CNN）特征表示中的强大空间信息，使得特征向量不太适合场景分类。为了提取可学习的无序特征进行SAR场景分类，提出了一种通过反向传播训练的高阶广义无序池网络，以学习局部聚集描述符（VLAD）和局部约束仿射子空间编码（LASC）的高阶向量。与一阶特征编码方式相比，所提出的网络可以通过外部产品自动学习高阶编码特征。随后，为了使特征表示更强大，将其梯度通过奇异值分解（SVD）和逐元素归一化的矩阵归一化（平方根）引入了所提出的网络。最后，从TerraSAR-X图像对SAR场景分类数据集进行的实验表明，所提出的网络比最新方法具有更好的性能。

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