Learning scene categories from high resolution satellite image for aerial video analysis

Automatic scene categorization can benefit various aerial video processing applications. This paper addresses the problem of predicting the scene category from aerial video frames using a prior model learned from satellite imagery. We show that local and global features in the form of line statistics and 2-D power spectrum parameters respectively can characterize the aerial scene well. The line feature statistics and spatial frequency parameters are useful cues to distinguish between different urban scene categories. We learn the scene prediction model from high-resolution satellite imagery to test the model on the Columbus Surrogate Unmanned Aerial Vehicle (CSUAV) dataset collected by a high-altitude wide area UAV sensor platform. We compare the proposed features with the popular Scale Invariant Feature Transform (SIFT) features. Our experimental results show that the proposed approach outperforms the SIFT model when the training and testing are conducted on disparate data sources.