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

Three-Dimensional Polarimetric InISAR Imaging of Non-Cooperative Targets

A new Polarimetric Interferometry Inverse Synthetic Aperture Radar (Pol-InISAR) 3D imaging method for non-cooperative targets is proposed in this paper. 3D imaging of non-cooperative targets becomes possible by combining additional information of interferometric phase along with conventional 2D ISAR imaging. In the previously reported single-polarimetry InISAR based 3D imaging, only a single-channel based interferometric phase is available that can be exploited to reconstruct the 3D ISAR image. This limits the ability to obtain a full target's scattering response and therefore limits the estimation of an accurate interferometric phase. To overcome this constraint, full-polarimetry information is being exploited in this paper, which allows to select the optimal polarimetric combination through which the highest coherence can be obtained. A higher coherence leads to a reduction (optimally a minimization) of the phase estimation error. Consequently, with an optimal phase estimation, an accurate 3D imaging of the target is possible. To validate this proposed Pol-InISAR based 3D imaging approach, both simulated and real datasets are taken under consideration

RFI suppression for SAR systems based on LMS adaptive filtering

Radio Frequency Interference (RFI) suppression has been an essential technique in Synthetic Aperture Radar (SAR) imaging. SAR is a technique for creating high resolution images of the earth’s surface. Over the area of the surface being observed, these images represent the backscattered microwave energy, which depends on the properties of the surface, such as its slope, roughness, textural in homogeneities and dielectric constant. The radar backscattering cross section also depends strongly on the existence of vegetation. These dependencies allow SAR imagery to be used in conjunction with models of the scattering mechanism to measure various characteristics of the earth’s surface, such as topography. An important characteristic of SAR is its day/night capability, which it possesses because it supplies its own illumination and receives the backscatter from it, as opposed to passive sensors which receive either the earth’s radiation or the reflected illumination from the sun. Furthermore, a SAR sensor has all-weather capability, because microwaves propagate through clouds and rain with only limited attenuation. These features, together with its fine two-dimensional resolution capability, have made SAR a valuable remote sensing tool for both military and civilian users. The VHF/UHF bands has been found to have significant advantages for air and ground survelliance application. However, from the point of view of SAR implementations, the VHF/UHF portion of the spectrum is already in heavy use by other services, such as television, mobile communications, radio and cellular phones. Even in remote locations the interference power often exceeds receiver noise by many dB, becoming the limiting factor on system sensitivity and severely degrading the image quality. Therefore it is important to investigate possible means of suppressing the interference in the received signal. MetaSensing B.V. asked the candidate to investigate this issue on P band data acquired by them with an airborne SAR system during an acquisition campaign in September 2013. In order to alleviate the problem of Radio Frequency Interference (RFI), it is proposed in this thesis to use an Adaptive Line Enhancer(ALE) controlled by the Normalised Least Mean Squares (NLMS) algorithm to eliminate RFI. Experimental results demonstrate that the filter improves the image result. Results from application of this algorithm to real radar data aicted with interference are presented

AN ANALYSIS OF ROTATING RESIDENT SPACE OBJECTS GEOMETRICAL AND DYNAMICAL FEATURE ESTIMATION

The amount of resident space objects (RSOs) orbiting around the Earth has seen a dramatic grow through the recent years. Its rising population increases the potential danger to space missions. At present time, it is urgent to gain as much information as possible in order to characterize this environment. The research presented in this work outlines a methodology for utilizing radar data in the presence of single and multiple looks. This method is based on the use of the inverse Radon transform computed on the time-frequency representation of the received signal. The image in the inverse Radon domain is used to jointly estimate the RSO’s rotation speed and its size with respect to the image plane. Then, this approach is extended for a bistatic case and the development of features fusion rules to integrate data from spatially distributed independent radar systems is studied and carried out. The algorithms are tested on a variety of simulated and real datasets. In particular, real data analysis exploiting a K-band FMCW radar and a turntable has been performed in a controlled environment. A period analysis of real light curve data is also presented inside this thesis. This analysis is done using two different methods to cross check results. Specific accomplishments include: the development of a method for geometrical and dynamical parameter extraction of rotating RSOs validated with measurements obtained in a controlled laboratory environment