Target detection with spatio-spectral data via concordance learning

In challenging environments, in order to uniquely define a sample as a target, multiple representations of the samples might be required. As a case study, we consider cars in the parking lots of an urban imagery as targets. What makes this problem challenging is the copresence of several parking garages and parking lots in the same imagery. Both the cars in the parking lots and in the parking garages present with similar spectral characteristics. Spectral representation alone is not sufficient to uniquely define a pixel as a car in the parking lot. In this example, before a pixel is confirmed as a target or rejected as not being a target, classifiers corresponding to spectral and spatial representations of the samples has to concord. The current study discusses some possible ways these classifiers can be trained so that the rate of true concordance is maximized. We consider independent training and feature concatenation first and then propose a joint optimization scheme. The proposed approach aims to optimize multiple classifiers at once so as to maximize concordance among the classifiers while minimizing the classification error.