论文标题
遥感场景分类的成对比较网络
Pairwise Comparison Network for Remote Sensing Scene Classification
论文作者
论文摘要
遥感场景分类旨在将特定的语义标签分配给遥感图像。最近，卷积神经网络极大地改善了遥感场景分类的性能。但是，一些混乱的图像很容易被识别为不正确的类别，通常会降低性能。图像对之间的差异可用于区分图像类别。本文提出了一个成对比较网络，其中包含两个主要步骤：成对选择和成对表示。提出的网络首先选择相似的图像对，然后用成对表示表示图像对。引入了自我代理以突出每个图像本身的信息内容，而相互代理则提出了捕获图像对之间的细微差异。对两个具有挑战性的数据集（AID，NWPU-RESISC45）的全面实验结果证明了拟议网络的有效性。这些代码在https://github.com/spectralpublic/pcnet.git中提供。
Remote sensing scene classification aims to assign a specific semantic label to a remote sensing image. Recently, convolutional neural networks have greatly improved the performance of remote sensing scene classification. However, some confused images may be easily recognized as the incorrect category, which generally degrade the performance. The differences between image pairs can be used to distinguish image categories. This paper proposed a pairwise comparison network, which contains two main steps: pairwise selection and pairwise representation. The proposed network first selects similar image pairs, and then represents the image pairs with pairwise representations. The self-representation is introduced to highlight the informative parts of each image itself, while the mutual-representation is proposed to capture the subtle differences between image pairs. Comprehensive experimental results on two challenging datasets (AID, NWPU-RESISC45) demonstrate the effectiveness of the proposed network. The codes are provided in https://github.com/spectralpublic/PCNet.git.
