Infrared imaging technology has great applications in various fields of social life, especially in the field of remote sensing. Monitoring the ground through infrared loads on satellites can explore natural resources and improve human production. However, due to limitations of equipment, space and other factors, the cost of performance calibration of space load in space is relatively high. To solve the calibration problem of spectral parameters and imaging parameters of space load in the mid-range infrared range, the parameter calibration technique is studied. The mid-far infrared space load comprehensive performance parameter calibration test system is designed by simulating the space vacuum environment on the ground, and the mid-far infrared space load can be tested in an all-round way before going into the space. The test system consists of a vacuum chamber, an infrared collimation system, a medium-far infrared monochromatic source, a standard surface source differential black body, and a series of standard targets. It can realize comprehensive calibration of spectral parameters and imaging parameters on the same device. The load to be tested is placed in a vacuum chamber to simulate a space vacuum environment. The radiation source is radiated into the vacuum chamber through an optical window to simulate ground radiation, which can achieve relative spectral responsivity, MRTD, NETD, MTF, field of view, and magnification, distortion and other parameters of the test, and achieved good experimental results. The results show that the test system can realize the calibration of the spectral parameters and imaging parameters of the mid-far infrared spatial load.
MRTD (Minimum Resolvable Temperature Difference) is an important parameter for comprehensive evaluation of temperature resolution and spatial resolution of infrared imaging systems. It has become one of the necessary detection parameters for manufacturers of thermal imaging cameras. The traditional subjective MRTD parameter test method is gradually replaced by objective test methods due to its long test time and high labor cost. At present, the objective test method has developed the video MTF method and the photometric camera method, but both methods have their corresponding limitations. This paper proposes a new objective MRTD parameter test method based on CNN neural network. Firstly, the four-bar target image used to test the MRTD parameters is analyzed. It is concluded that the process of testing the MRTD parameters is essentially an image classification, which lays a foundation for the learning of CNN neural networks. Then the network model of CNN neural network interpretation of four-bar target image is expounded, and the accuracy of MRTD test results under different network models is analyzed. It was found that the network structure should not be complicated in the classification process of the four-bar target image. Based on the classic CNN neural network LeNet model, this paper proposes a CNN neural network suitable for four-bar target image classification problem by optimizing the convolution layer size, changing the activation function and adjusting the network structure. The experimental results show that the optimized CNN neural network improves the accuracy and repeatability of the MRTD parameter test.