Stripe nonuniformity is very typical in line infrared focal plane arrays (IR-FPA) and uncooled staring IR-FPA. In this paper, the mechanism of the stripe nonuniformity is analyzed, and the gray-scale co- occurrence matrix theory and optimization theory are studied. Through these efforts, the stripe non uniformity correction problem is translated into the optimization problem. The goal of the optimization is to find the minimal energy of the image’s line gradient. After solving the constrained nonlinear opti mization equation, the parameters of the stripe nonuniformity correction are obtained and the stripe nonuniformity correction is achieved. The experiments indicate that this algorithm is effective and efficient.

In the system of tracking and detection based on the four-quadrant detector (4-QD), the energy distribution of the incident spot and the blind area of the photosensitive surface will affect the location accuracy. The current model of the spot is based on the ideal circular Gauss spot, which makes the error caused by the spot shape easily ignored. In this paper, the model of the spot energy distribution is improved, which can adapt to the elliptical Gauss distribution. The width of the blind area is also added to the response models of the detector so that the output of each quadrant and the error of the localization algorithm can be calculated more accurately. The simulation results show that the measurement accuracy of 4-QD decreases with the increase of the blind area, the shape, and the inclination of the light spot. In the experiment, we first verify the correctness and practicability of the improved model of the spot energy distribution, and then the improved model is proved to be able to make the response and error calculation more accurate.

Nowadays, convergence and ghosting artifacts are common problems in scene-based nonuniformity correction (NUC) algorithms. In this study, we introduce the idea of space frequency to the scene-based NUC. Then the convergence speed factor is presented, which can adaptively change the convergence speed by a change of the scene dynamic range. In fact, the convergence speed factor role is to decrease the statistical data standard deviation. The nonuniformity space relativity characteristic was summarized by plenty of experimental statistical data. The space relativity characteristic was used to correct the convergence speed factor, which can make it more stable. Finally, real and simulated infrared image sequences were applied to demonstrate the positive effect of our algorithm.

Both the Jones and Mueller matrices encounter difficulties when physically modeling mixed materials or rough surfaces due to the complexity of light–matter interactions. To address these issues, we derived a matrix called the paths correlation matrix (PCM), which is a probabilistic mixture of Jones matrices of every light propagation path. Because PCM is related to actual light propagation paths, it is well suited for physical modeling. Experiments were performed, and the reflection PCM of a mixture of polypropylene and graphite was measured. The PCM of the mixed sample was accurately decomposed into pure polypropylene’s single reflection, pure graphite’s single reflection, and depolarization caused by multiple reflections, which is consistent with the theoretical derivation. Reflection parameters of rough surface can be calculated from PCM decomposition, and the results fit well with the theoretical calculations provided by the Fresnel equations. These theoretical and experimental analyses verify that PCM is an efficient way to physically model light-matter interactions.

Thermal remote sensing is an effective technique for marine oil slick detection. However, many factors, such as the oil type, slick thickness, sensor capability, and the background environment, can together have an impact on the remotely sensed thermal imagery. These cross-coupling effects can usually be clarified by ground-based experiments. In this letter, four different types of oil slicks on water bodies were prepared and their brightness temperatures (BTs) measured periodically in an outdoor experiment. The results indicated that there are obvious differences in the BTs between the different types of oil, especially between crude and refined oil. Defined BT time-changing contrast coefficient of different type of oil slicks numerically displays these significant difference in different observed periods. These results imply that thermal sensors may be used to discern the type of oil slick and that time series of thermal observations will be able to help with oil-type detection in the future. Moreover, the optimal strategy is to make a series of observations covering the cooling period from noon (the optimal detection time) to around sunset.