  Aerial image categorization plays an indispensable role in remote sensing and
artificial intelligence. In this paper, we propose a new aerial image
categorization framework, focusing on organizing the local patches of each
aerial image into multiple discriminative subgraphs. The subgraphs reflect both
the geometric property and the color distribution of an aerial image. First,
each aerial image is decomposed into a collection of regions in terms of their
color intensities. Thereby region connected graph (RCG), which models the
connection between the spatial neighboring regions, is constructed to encode
the spatial context of an aerial image. Second, a subgraph mining technique is
adopted to discover the frequent structures in the RCGs constructed from the
training aerial images. Thereafter, a set of refined structures are selected
among the frequent ones toward being highly discriminative and low redundant.
Lastly, given a new aerial image, its sub-RCGs corresponding to the refined
structures are extracted. They are further quantized into a discriminative
vector for SVM classification. Thorough experimental results validate the
effectiveness of the proposed method. In addition, the visualized mined
subgraphs show that the discriminative topologies of each aerial image are
discovered.
