论文标题
ddipnet和ddipnet+：用于遥感图像分类的判别深图像先验网络
DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification
论文作者
论文摘要
遥感图像分类的研究显着影响人类常规任务，例如城市规划和农业。如今，技术的快速进步和许多高质量遥感图像的可用性创造了对可靠自动化方法的需求。当前的论文提出了两种新颖的基于学习的架构，用于图像分类目的，即，判别深度图像先验网络和判别深度图像先验网络+，它们结合了深层图像先验和三重态网络学习策略。通过三个众所周知的公共遥感图像数据集进行的实验实现了最新的结果，从而证明了使用深层图像先验进行遥感图像分类的有效性。
Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technology and the availability of many high-quality remote sensing images create a demand for reliable automation methods. The current paper proposes two novel deep learning-based architectures for image classification purposes, i.e., the Discriminant Deep Image Prior Network and the Discriminant Deep Image Prior Network+, which combine Deep Image Prior and Triplet Networks learning strategies. Experiments conducted over three well-known public remote sensing image datasets achieved state-of-the-art results, evidencing the effectiveness of using deep image priors for remote sensing image classification.
