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As a basic and key problem in the remote sensing community, remote sensing image scene understanding (RSISU) has attracted increasing research interest. In recent years, deep learning has revolutionized RSISU. However, the great success of deep learning strongly depends on the availability of a largescale data set with explicit labels. Although remote sensing image scene data sets have been publicly released for a limited number of remote sensing image types, data sets of many types of remote sensing images are still not available, which limits the applicability of deep learning. Generally, exhaustively labeling remote sensing image scene data sets via manual labor is time consuming, and it becomes impossible when the data set volume is very large. Hence, it is necessary to develop an intelligent annotation approach to efficiently and accurately label these data sets. Based on a prior assumption of consistency, namely, the assumption that samples within the same cluster are likely to have the same label, this paper proposes a novel annotation method for remote sensing image scene data sets called automatic aggregation via hierarchical similarity diffusion (AA-HSD). More specifically, each remote sensing image scene is represented by multiple features. To make full use of these complementary features, this paper proposes a new hierarchical similarity diffusion method for robustly measuring the similarity matrix of the scenes in the data set. Based on this similarity matrix, the scenes are automatically aggregated into clusters. Instead of annotating the data set scene by scene, as in the traditional manual annotation solution, we annotate the data set cluster by cluster, which dramatically increases the annotation speed while achieving a very high accuracy. Extensive experiments on two public remote sensing image scene data sets demonstrate the validity of our proposed AA-HSD method, which outperforms all competing baselines.

Greedy Annotation of Remote Sensing Image Scenes Based on Automatic Aggregation via Hierarchical Similarity Diffusion