High resolution remote sensing (HRRS) image scene classification plays a crucial role in a wide range of applications and has been receiving significant attention. Recently, remarkable efforts have been made to develop a variety of approaches for HRRS scene classification, wherein deep-learning-based methods have achieved considerable performance in comparison with state-of-the-art methods. However, the deep-learning-based methods have faced a severe limitation that a great number of manually-annotated HRRS samples are needed to obtain a reliable model. However, there are still not sufficient annotation datasets in the field of remote sensing. In addition, it is a challenge to get a large scale HRRS image dataset due to the abundant diversities and variations in HRRS images. In order to address the problem, we propose a semi-supervised generative framework (SSGF), which combines the deep learning features, a self-label technique, and a discriminative evaluation method to complete the task of scene classification and annotating datasets. On this basis, we further develop an extended algorithm (SSGA-E) and evaluate it by exclusive experiments. The experimental results show that the SSGA-E outperforms most of the fully-supervised methods and semi-supervised methods. It has achieved the third best accuracy on the UCM dataset, the second best accuracy on the WHU-RS, the NWPU-RESISC45, and the AID datasets. The impressive results demonstrate that the proposed SSGF and the extended method is effective to solve the problem of lacking an annotated HRRS dataset, which can learn valuable information from unlabeled samples to improve classification ability and obtain a reliable annotation dataset for supervised learning.

高分辨率遥感（HRRS）图像场景分类在广泛的应用中起着至关重要的作用，并且受到了广泛的关注。近来，已经做出了巨大的努力来开发用于HRRS场景分类的各种方法，其中与传统方法相比，基于深度学习的方法已经取得了可观的性能。然而，基于深度学习的方法面临着严重的局限性，即需要大量的人工注释HRRS样本才能获得可靠的模型。但是，仍然不够遥感领域中的注释数据集。另外，由于HRRS图像的丰富多样性和变化性，要获得大规模HRRS图像数据集也是一个挑战。为了解决该问题，我们提出了一种半监督生成框架（SSGF），该框架结合了深度学习功能，自标签技术和判别式评估方法来完成场景分类和注释数据集的任务。在此基础上，我们进一步开发了扩展算法（SSGA-E）并通过独家实验对其进行了评估。实验结果表明，SSGA-E的性能优于大多数全监督和半监督方法。它在UCM数据集上达到了第三佳的精度，在WHU-RS，NWPU-RESISC45和AID数据集上也获得了第二佳的精度。