Proportional similarity-based Openmax classifier for open set recognition in SAR images

Most of the existing Non-Cooperative Target Recognition (NCTR) systems follow the “closed world” assumption, i.e., they only work with what was previously observed. Nevertheless, the real world is relatively “open” in the sense that the knowledge of the environment is incomplete. Therefore, unknown targets can feed the recognition system at any time while it is operational. Addressing this issue, the Openmax classifier has been recently proposed in the optical domain to make convolutional neural networks (CNN) able to reject unknown targets. There are some fundamental limitations in the Openmax classifier that can end up with two potential errors: (1) rejecting a known target and (2) classifying an unknown target. In this paper, we propose a new classifier to increase the robustness and accuracy. The proposed classifier, which is inspired by the limitations of the Openmax classifier, is based on proportional similarity between the test image and different training classes. We evaluate our method by radar images of man-made targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Moreover, a more in-depth discussion on the Openmax hyper-parameters and a detailed description of the Openmax functioning are given

Transfer Learning-Based Fully-Polarimetric Radar Image Classification with a Rejection Option

In this paper, convolutional neural networks based on transfer learning are employed for the classification of fully polarimetric radar images with a rejection option. In a conventional supervised classification problem, the network has to choose from one of the known classes. However, a classifier, in a real-world scenario, may deal with an open set recognition problem in which the target under test is not included in the classifier training set. The capability of a classifier to discriminate between known and unknown targets may enable the classifier to self-learn from the experience as well as to enrich the system memory, which may in turn open the door to cognitive classifiers. This paper aims at testing the capability of transfer learning approaches, using VGG16 and AlexNet, to recognize unknown target classes of ISAR images. A threshold-based scheme has been applied to the softmax scores to discriminate among known and unknown targets. Data augmentation techniques such as translation and Gaussian noise addition are also employed in order to expand the diversity of the training dataset.</p

Convolutional Neural Network for Joint Communication and Radar Signals Classification

Automatic modulation classification (AMC) plays an important role in the development of cognitive radio and cognitive radar systems. Due to the distinct aims of radar and communication systems, their commonly used modulation techniques may differ significantly. However, AMC in the radar and communication domains can be closely aligned. Considering the widespread applications of deep learning architectures, particularly, convolutional neural networks (CNN) in classification problems, we propose a CNN-based framework for the joint classification of communication and radar signals. In the proposed framework, a CNN is first trained by signals in the in-phase and quadrature domain and then another CNN is trained by using constellation diagrams to differentiate between close modulations. A publicly available benchmark dataset, which consists of different modulation schemes in the communication domain, has been augmented with our simulated linear frequency modulated radar signals under the same noise condition to validate the accuracy of the proposed framework.</p

Convolutional Neural Network for Joint Communication and Radar Signals Classification

Automatic modulation classification (AMC) plays an important role in the development of cognitive radio and cognitive radar systems. Due to the distinct aims of radar and communication systems, their commonly used modulation techniques may differ significantly. However, AMC in the radar and communication domains can be closely aligned. Considering the widespread applications of deep learning architectures, particularly, convolutional neural networks (CNN) in classification problems, we propose a CNN-based framework for the joint classification of communication and radar signals. In the proposed framework, a CNN is first trained by signals in the in-phase and quadrature domain and then another CNN is trained by using constellation diagrams to differentiate between close modulations. A publicly available benchmark dataset, which consists of different modulation schemes in the communication domain, has been augmented with our simulated linear frequency modulated radar signals under the same noise condition to validate the accuracy of the proposed framework.</p