Abstract:During the study of nonuniformity correction algorithms for Infrared Focal Plane Arrays (IRFPA), a large number of target images containing nonuniformity information need to be obtained by simulation. According to the generation mechanism of nonuniformity of IRFPAs, a detection element nonuniformity model, a readout circuit nonuniformity model and a drift noise model are established respectively. By superposing the nonuniformity of the three models on the ideal image respectively, the target image is obtained eventually. In the process of simulation, it is assumed that the nonuniformity noise obeys the Gaussian distribution. By setting the model coefficients with different mean and variant, different degrees of nonuniformity simulation can be achieved. Because the drift noise is considered, the simulation results of the proposed method are more consistent to actual situation.

Abstract:To reduce aberration, aspheric mirrors are usually used to fold the optical path in an optical system for a space remote sensor. In the optical detection of aspheric mirrors, the supporting structure is necessary for a compensator. On the basis of the definition of degree of freedom and coordination of the compensator, the degree of freedom required to adjust the compensator is analyzed by incorporating the position and function of the compensator in the detection optical path. According to the analysis result, the function and structure form which should be achieved for the compensator are confirmed. A supporting structure is designed and the analysis result of the adjusting precision of the supporting structure is given. The analysis result shows that the supporting structure has its displacement adjusting precision of 1.49 μm/deg and angle adjusting precision of 0.007 μrad/deg. The actual engineering application affirms that the supporting structure satisfies the requirements for use.

Abstract:In practical applications, the traditional adaptive nonuniformity correction algorithm based on Neural Network (NN-NUC) has a limited correction capability and is easy to generate ghosting artifacts. To solve this problem, the ghosting artifact generating process in the NN-NUC algorithm is analyzed in detail and the common methods for removing ghosting artifacts are given. Then, by incorporating the characteristics of actual infrared imagers, an improved NN-NUC algorithm is proposed. The simulation experimental results show that the proposed method can suppress the generation of ghosting artifacts in a scene extremely and can reduce the nonuniformity noise of the image effectively. Moreover, the proposed algorithm has a small calculation amount and is easy to be implemented by hardware. So it is of good value to practical applications.

Abstract:Since infrared detectors' observation is easy to be affected by cloud and the simulation of cloud brightness temperature by a fast radiative transfer mode is not precise, cloud detection should be carried out first in order to obtain clear field-of-view or clear channel information when the bright temperature data from the Infrared Atmospheric Sounder (IRAS) onboard FY-3B satellite are assimilated. The cloud detection study is carried out on the data from IRAS on the basis of the Minimum Residual Method (MRM). The method not only can distinguish if there exists cloud in the field-of-view, but also can obtain the cloud parameters (effective cloud fraction) in the field-of-view. At the same time, the cloud detection effectiveness is verified by the cloud images from FY-2E satellite. The result shows that the method is feasible for IRAS data cloud detection.

Abstract:The inverse of liquid-level based on the surface temperature of a tank is a new liquid-level recognition method with high identification accuracy and wide application range. A physical model for a storage tank is established. The surface temperature field of the tank is derived by a finite volume method and the liquid-level and temperature inside the tank are inverted. In order to verify the recognition accuracy, an experiment for pressure vessel comparison is designed. The experimental results show that the simulation recognition error is less than 1% in the range of acceptable measurement error (σ=2℃) and the experimental recognition level error is about 2%. The liquid temperature inside the cylinder can be recognized by the inversion algorithm and the recognition precision is higher. The experimental result shows that this recognition method is effective and can provide the theoretical and experimental basis for infrared liquid-level detection and temperature recognition.

Abstract:To let an infrared thermal imager measure the temperature of micrometer-level heating structures in micro-electronic devices accurately and overcome the problem of device deformation due to expansion and contraction, a single-temperature emissivity measurement method is proposed. The influence of expansion and contraction on the detection result when a traditional dual-temperature emissivity measurement method is used to detect micro-electronic devices is analyzed. In the process of temperature transformation, the deformation effect of the device may affect the measurement result seriously. The proposed single-temperature emissivity measurement method is designed for the microscopic infrared test of micro-electronic devices. It can measure the emissivity of a device as long as it is kept at a fixed temperature. The theoretical analysis result shows that the single-temperature method and the dual-temperature are equivalent in the final measurement results. However, the single-temperature method can avoid the deformation of devices. So, the accuracy and reliability of its measurement result is ensured.

Abstract:Identification of cotton and linen fabric is difficult in the qualitative analysis of textile fibers. Its request to testers is high and its test result is easy to be affected by artificial factors. Through comprehensive analysis of the mid-infrared spectra of cotton and linen and the crystallinity and orientation degree which reflect the crystal structures of cotton and linen by the methods of correlation analysis and peaks' intensity ratio, the characteristic spectra and reference spectra of cotton and linen are obtained. Because there is a significant difference between cotton and linen in orientation degree, the mid-infrared characteristic wavelength of orientation degree is selected. Then, through regression analysis, the regression equation for orientation degree and mid-infrared spectra of cotton and linen is obtained. The regression equation can be used to identify cotton and linen. The method is simple, convenient, nondestructive and environmental. A test person even without any textile knowledge can complete the test in 2 minutes less. The result shows that the method can simplify the identification of cotton and linen so as to improve operation efficiency.