The matched filter (MF) and adaptive coherence estimator (ACE) show great effectiveness in hyperspectral target detection applications. Practical applications in which on-board processing is generally required demand real-time or near-real-time implementation of these detectors. However, a vast amount of hyperspectral data may make real-time or near-real-time implementation of the detection algorithms almost impossible. Band selection can be one of the solutions to this problem by reducing the number of spectral bands. We propose a new band selection method that prioritizes spectral bands based on their influence on the detection performance of the MF and ACE and discards the least influential bands. We validate the performance of our method using real hyperspectral images. We also demonstrate our technique on near-real-time detection tasks and show it to be a feasible approach to the tasks.
IR Target detection is one of the key technologies in military applications. However, IR sensor has limitations of passive sensor such as low detection capability to weather and atmospheric effects. In recent years, sensor fusion is active research topic to overcome the limitations. Additional active SAR sensor is selected for sensor fusion because SAR sensor is robust to various weather conditions. The state-of-the-art detector, BMVT, has good performance in clear environment such as sky and sea background for small target. However, it shows poor performance when the target has extended size or the target is located in complex background such as ground-background with dense clutters. Therefore, we presents an improved ground target detection method based on the BMVT and Morphology filter (BMVT-M). The proposed algorithm consists of two parts: The first part is target enhancement based on the BMVT. The second part is clutter rejection and target enhancement based on the Morphology filter. In addition, conventional BMVT is not suitable to SAR image for target detection because SAR image has many shot noises. Therefore we apply a median filter before the BMVT in SAR image to suppress the shot noise. For the verification of the performance, experiments are performed in various cluttered backgrounds, such as ground, sea, and sky generated by the OKTAL-SE tool. The proposed algorithm showed upgraded detection performance than the BMVT in terms of detection rate and false alarm rate. Moreover, we discuss the applicability of the proposed method to the SAR and IR sensor fusion research.
Various scene-based nonuniformity correction (SBNUC) methods have been proposed to diminish the residual nonuniformity (RNU) of the infrared focal plane array (IRFPA) sensors. Most existing SBNUC techniques require a relatively large number of image frames to reduce the RNU. In some applications, however, there is not enough time for capturing a large number of image frames prior to the camera operation, or only several image frames are available to users. A new scene-based approach that can correct the RNU using only several image frames is proposed. The proposed method formulates the SBNUC process as an energy minimization problem. In the proposed energy function, we introduce regularization terms for the parameter regarding the responsivity of the IRFPA as well as for the true scene irradiance. Correction results are obtained by minimizing the energy function using a numerical technique. Experimental results demonstrate the effectiveness of the proposed method.
Coast mode is one of tracking modes that deals with the target occlusion, where tracking is halted for a while and the servo slew rate is maintained according to the target movement just before the occlusion. As the last step of the coast mode tracking, this paper presents a target re-locking algorithm to resume the target tracking after the blind time. First, during the normal image tracking stage, as a target model, a gray-level histogram ratio of the target and background is computed for each frame of the normal stage images thereby updating the target model at each time step. When entering the coast mode due to occlusion, we run the re-locking algorithm for each frame of the coast mode images so that it can immediately resume the tracking right after the end of the blind time. The re-locking algorithm divides the input image into blocks and for each block of the image, it takes an average of histogram ratios over the block and selects candidate target blocks of large histogram ratios, where the histogram ratio is evaluated at the gray-level of each pixel in the block and those histogram ratios are averaged over the pixels in the block. Due to the block-based averaging, the overall decision is robust to noise in the IR image, and the re-locking process afterward is of reduced computational complexity. With the target candidate blocks, a clustering is performed to make target candidate clusters, where each cluster is a set of connected blocks of large histogram ratios. As a final step, the first-ranked target candidate cluster is selected by computing an overall score that combines the histogram ratios and the prior knowledge of the target size, location, and variation of the intensity obtained during the normal tracking stage. We present experimental results based on both computer simulation and test under real environment with EOTS demonstrating the effectiveness of the proposed algorithm.
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