结合数据增广和迁移学习的高分辨率遥感影像场景分类

深度学习在计算机视觉领域取得了显著的成果，如图像分类、人脸识别、图像检索等。对于遥感领域而言，获取用于训练CNN的有标签数据集通常是一个重大挑战。本文研究了如何将CNN用于高分辨率遥感影像的场景分类，为了克服缺乏大量有标签遥感影像数据集的问题，结合CNN采用了两种技术：数据增广和迁移学习。在UC Merced Land Use数据集上，验证了VGG16、VGG19、ResNet50、InceptionV3、DenseNet121等5种网络的性能，分别达到了98.10%、96.19%、99.05%、97.62%、99.52%的分类准确率。

Scene classification of high-resolution remote sensing image combining data augmentation and transfer learning

Deep learning has achieved remarkable results in the field of computer vision, such as image classification, face recognition, image retrieval and so on. For remote sensing, obtaining a labeled dataset for training DCNN is often a major challenge. In this paper, the use of DCNN for scene classification in high-resolution remote sensing imagery is investigated. In order to overcome the lack of a large number of labeled remote sensing image datasets, two technologieswere combined with DCNN:data augmentation and transfer learning. On the UC Merced Land Use dataset, the performances of 5 networks including VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121 were verified, which achieved classification accuracy of 98.10%, 96.19%, 99.05%, 97.62%, and 99.52%, respectively.