Runway Detection in SAR Images Based on Fusion Sparse Representation and Semantic Spatial Matching

In this paper, a novel algorithm is presented for runway detection in synthetic aperture radar images. It involves two steps: runway assessment and confirmation. In the first step, the primary runway (PR) and the auxiliary runway (AR) of the airport are assessed by using a sparse representation fusion frame. A set of residuals, for each feature from PR or AR, is first generated by performing sparse reconstructions over two training dictionaries constructed by a set of discriminative features. Based on all residuals for all types of features, two residual sequences for PR and AR are, respectively, built. To improve the assessment performance, these two residual sequences are normalized and further linearly fused. An assessment criterion is applied to the fusion result to infer an optimal target estimate. In the second step, the histogram of oriented gradient feature descriptors of PR, AR, and the entire runway region constructed by PR, AR, and taxiways are first generated. Afterward, two semantic spatial rules are developed to verify each candidate region of interest. If a perfect match is achieved, the candidate target can be confirmed. Since the PR and AR are selected based on the residual fusion related with the concatenation of multiple features, the presented algorithm has a good representation ability to the runway. By introducing semantic spatial relationships into the confirmation scheme, this algorithm can well discriminate other runway-like targets. The test results using real scene data demonstrate that the presented method has superiority to some state-of-the-art alternatives.