In this work, a discriminatively learned CNN embedding is proposed for remote sensing image scene classification. Our proposed siamese network simultaneously computes the classification loss function and the metric learning loss function of the two input images. Specifically, for the classification loss, we use the standard cross-entropy loss function to predict the classes of the images. For the metric learning loss, our siamese network learns to map the intra-class and inter-class input pairs to a feature space where intra-class inputs are close and inter-class inputs are separated by a margin. Concretely, for remote sensing image scene classification, we would like to map images from the same scene to feature vectors that are close, and map images from different scenes to feature vectors that are widely separated. Experiments are conducted on three different remote sensing image datasets to evaluate the effectiveness of our proposed approach. The results demonstrate that the proposed method achieves an excellent classification performance.
翻译:在这项工作中,为遥感图像场景分类,建议采用有区别学识的CNN嵌入方式。我们提议的Siamese网络同时计算两种输入图像的分类损失功能和计量学习损失功能。具体地说,在分类损失方面,我们使用标准的跨热带损失功能来预测图像的类别。在计量学习损失方面,我们的Siamese网络学会将类内和类间输入配对图绘制成一个特色空间,在这个空间里,类内投入接近,而分类之间的输入被一个边距分隔。具体地说,在遥感图像场景分类方面,我们想从同一场绘制图像,以显示接近的矢量,并从不同场绘制图像,以显示广泛分离的矢量。对三种不同的遥感图像数据集进行了实验,以评价我们拟议方法的有效性。结果表明,拟议的方法取得了出色的分类性能。