Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery

Recognizing targets in infra-red images is an important problem for defense and security applications. A deployed network must not only recognize the known classes, but it must also reject any new or unknown objects without confusing them to be one of the known classes. Our goal is to enhance the ability of existing (or pretrained) classifiers to detect and reject unknown classes. Specifically, we do not alter the training strategy of the main classifier so that its performance on known classes remains unchanged. Instead, we introduce a second network (trained using regression) that uses the decision of the primary classifier to produce a class conditional score that indicates whether an input object is indeed a known object. This is performed in a Bayesian framework where the classification confidence of the primary network is combined with the class-conditional score of the secondary network to accurately separate the unknown objects from the known target classes. Most importantly, our method does not require any examples of OOD imagery to be used for training the second network. For illustrative purposes, we demonstrate the effectiveness of the proposed method using the CIFAR-10 dataset. Ultimately, our goal is to classify known targets in infra-red images while improving the ability to reject unknown classes. Towards this end, we train and test our method on a public domain medium-wave infra-red (MWIR) dataset provided by the US Army for the development of automatic target recognition (ATR) algorithms. The results of this experiment show that the proposed method outperforms other state-of-the-art methods in rejecting the unknown target types while accurately classifying the known ones.