Aerial images’ clustering is of great significance in many domains of the last-generation computer systems, such as intelligent navigation, geological analysis, and disaster prediction. Conventional methods exploit only the global visual information and unimodal visual feature to characterize each aerial image, which is simple and self-descriptive in some cases. However, it cannot adequately simulate tiny and fine-grained visual patterns within each aerial image, such as automobiles of the “intersection” category and houses of the “residence” category. Moreover, the unimodal feature cannot effectively capture each aerial image. Other visual channels, such as the light spectrum, should also be encoded into the aerial image modeling system. We propose a novel aerial image clustering system, with a seamless fusion of spectrum and local features in this work. The multispectral visual clue is characterized by color plus texture channels from aerial images of each spectral channel. Simultaneously, locally distributed and very small objects combined with their spatial configurations are described by a set of graphlets. That is, each edge connects a pair of small objects, which are spatially neighboring. Subsequently, a multi-view learning algorithm is formulated to fuse the above channels to characterize each aerial image optimally. The importance of multiple spectral channels is flexibly tuned. A graph-based clustering algorithm is adopted to categorize the massive-scale aerial images into multiple types based on each aerial image’s fused feature description. Extensive comparative results on a million-level real-world aerial photograph set prove our method’s advantage over available state-of-the-art ones. Besides, visualization results imply that our clustering algorithm can optimally discriminate aerial images from different categories.

航空影像的聚类在上一代计算机系统的许多领域都具有重要意义，例如智能导航，地质分析和灾害预测。传统方法仅利用全局视觉信息和单峰视觉特征来表征每个航空图像，这在某些情况下是简单且可自我描述的。但是，它不能充分模拟每个航拍图像中的细微视觉图案，例如“交叉口”类别的汽车和“居住”类别的房屋。此外，单峰特征无法有效捕获每个航拍图像。其他视觉通道（例如光谱）也应编码到航空影像建模系统中。我们提出了一种新颖的航空影像聚类系统，在这项工作中将频谱与本地特征无缝融合。多光谱视觉线索的特征是来自每个光谱通道的航拍图像的颜色加上纹理通道。同时，一组图集描述了局部分布的非常小的对象及其空间配置。即，每个边缘连接一对在空间上相邻的小物体。随后，制定了多视图学习算法以融合上述通道，以最佳地表征每个航拍图像。可以灵活调整多个光谱通道的重要性。采用基于图的聚类算法，根据每个航空图像的融合特征描述将大规模航空图像分为多种类型。在数百万个级别的现实航空照片集上的大量比较结果证明了我们的方法相对于可用的最新技术的优势。此外，可视化结果表明我们的聚类算法可以最佳地区分不同类别的航空图像。