Saliency-constrained semantic learning for airport target recognition of aerial images

Abstract

The airport target recognition method for remote sensing images is generally based on image matching, which is significantly affected by the variations of illumination, viewpoints, scale, and so on. As a well-known semantic model for target recognition, bag-of-features (BoF) performs k-means clustering on enormous local feature descriptors and thus generates the visual words to represent the images. We propose a fast automatic recognition framework for an airport target of a low-resolution remote sensing image under a complicated environment. It can be viewed as a two-phase procedure: detection and then classification. Concretely, it first utilizes a visual attention model for locating the salient region, and then detects possible candidate targets and extracts saliency-constrained scale invariant feature transform descriptors to build a high-level semantics model. Consequently, BoF is applied to mine the high-level semantics of targets. Different from k-means in a traditional BoF, we employ locality preserving indexing (LPI) to obtain the visual words. Because LPI can consider the intrinsic local structure of descriptors and further enhance the ability of words to describe the image content, it can accurately classify the detected candidate targets. Experiments on the dataset of 10 kinds of airport aerial images demonstrate the feasibility and effectiveness of the proposed method.