Multi-view clustering toward aerial images by combining spectral analysis and local refinement

Abstract

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.