Suchergebnisse

In the framework of remote sensing, infrared (IR) cameras allow the user to sense IR radiation, similar to common cameras that sense visible light. The radiation acquired from IR cameras is related to the temperature of the observed scene through the Planck's law, in fact IR cameras are also known as "thermal cameras" for their capability of detecting information concerning the heat of the observed scene. Thermal cameras were originally developed for military purposes to detect and track targets on complex scenarios as an alternative to radar systems, in that optical systems - due to their passive nature - are more robust to countermeasures for interception. Nevertheless, over the years IR cameras have been employed also in several civilian fields. In a military context, the images acquired from IR devices are processed to reveal the presence of targets that typically cannot be easily distinguished from both clutter, spatial and temporal noise sources. The aim of automatic target detection (ATD) techniques is to furnish detections to consider reliable in a statistical approach, i.e. in terms of probability of false-alarm and probability of detection. In order to improve ATD performance, appropriate de-noising techniques and clutter removal algorithms have to be developed as a pre-processing step, since the mitigation of both noise sources (spatial and temporal) and the accurate estimation of background clutter improve the performance of ATD. In this framework, this PhD thesis is focused on pre-processing techniques developed to improve the performance of ATD techniques. The first part of the thesis is focused on a de-noising problem known in the literature as non-uniformity correction (NUC) for last generation focal-plane arrays (FPAs) IR devices. Particular emphasis has been posed on scene-based techniques since NUC is operated with the only employment of the sensed radiation. A detailed overview of scene-based NUC techniques presented in the literature is carried out highlighting advantages and drawbacks. Focusing on the performance of the NUC scene-based techniques, the Scribner's algorithm has been deeply analyzed revealing high NUC performance combined with small computational load. In relation to this NUC technique, it has been introduced the problem of ghosting artifacts which is considered as a collateral effect emerging from scene-based NUC techniques based on a statistical approach. Novel methods to mitigate ghosting artifacts are presented in this PhD thesis: (i) Bilateral filter (BF)- based de-ghosting. The bilateral filter has been employed to operate accurate spatial estimates of the FPN; (ii) Temporal statistics (TS)-based de-ghosting. Temporal statistics are computed to predict the trend of an accurate estimate of the NUC parameters. The proposed techniques have been tested and evaluated in terms of de-ghosting capability and global NUC effectiveness by means of IR experimental data sets with simulated FPN. The second part of the PhD thesis concerns a complementary topic of data processing for ATD applications: background estimation and removal in IR images. It has been adopted a well-established scheme for target detection in IR surveillance systems, that is the cascade of background clutter removal plus a strategy of target detection. In such an ATD scheme, the overall detection performance is strongly influenced by the effectiveness of the employed BEA. Particularly, the BEA and its design parameters should be chosen so as to get an accurate estimate of the background signal and to avoid biases caused by the possible presence of targets (target leakage). In this framework, it is proposed a novel method for the choice and setting of the best performing BEA in its best performing configuration for the detection of dim point targets in IR images. The proposed procedure relies on a simulation-based off-line approach. The effectiveness of the proposed BEA selection procedure is evaluated in two case studies typical of IR video-surveillance: maritime and airborne surveillance scenarios. Experimental image sequences have been acquired by MWIR cameras in order to test the proposed BEA selection criterion (BEA-SC).