Infrared small target detection technology is widely used in infrared search and tracking, infrared precision guidance, low and slow small aircraft detection, and other projects. Its detection ability is very important in terms of finding unknown targets as early as possible, warning in time, and allowing for enough response time for the security system. This paper combines the target characteristics of low-resolution infrared small target images and studies the infrared small target detection method under a complex background based on the attention mechanism. The main contents of this paper are as follows: (1) by sorting through and expanding the existing datasets, we construct a single-frame low-resolution infrared small target (SLR-IRST) dataset and evaluate the existing datasets on three aspects—target number, target category, and target size; (2) to improve the pixel-level metrics of low-resolution infrared small target detection, we propose a small target detection network with two stages and a corresponding method. Regarding the SLR-IRST dataset, the proposed method is superior to the existing methods in terms of pixel-level metrics and target-level metrics and has certain advantages in model processing speed.

For the tilted mirrored aperture synthesis (MAS), it can obtain different fields of view (FOV) by adjusting the inclination angle of the reflector. However, the tilted MAS microwave radiometer system is more complex. The antennas need to receive both direct signals and signals reflected by different reflectors, and reflector errors will affect the performance of the tilted MAS system. At present, there is no effective method to correct various errors in tilted MAS systems (especially reflector errors), which results in poor quality of brightness temperature (BT) image reconstruction for the tilted MAS. In this paper, we design a network to reconstruct BT images based on CNN and Transformer, and present a method to reconstruct the tilted MAS BT image by using this network. Based on the analysis of the FOV and spatial resolution of the tilted MAS system, a method for collecting the measured training dataset is presented. Using the measured training dataset for network training, the network learns the information of different errors in the tilted MAS system, including the error information of different reflectors and the error information of amplitude and phase. The simulation and experimental results show that the proposed method can obtain BT images with higher reconstruction quality than the impulse matrix reconstruction method.

The mirrored aperture synthesis (MAS) was proposed to improve the spatial resolution in passive microwave remote sensing. In MAS, the elements in the spatial impulse response matrix ( H matrix) are the sampled values of the h function. In this paper, the h function considering practical factors is derived. These practical factors include the normalized antenna pattern, the reflector reflectivity, the reflector length, and the distance between the array and the scene. The normalized antenna pattern modulates the received signals. The reflector reflectivity affects amplitudes of the reflected signals. The reflector length determines whether the reflected signals can be received. The distance between the array and the scene influences the incident angles of the signals from the same point source in the scene. The distance also influences the path differences between signals. The formulas and related analysis are verified through the numerical simulations and experiments.

The demand for high precision Sea Surface Temperature (SST) has been growing rapidly in recent years, because SST is one of the key parameters to describe the thermal state of the sea surface. This paper analyzes the differences between microwave remote sensing and infrared remote sensing for SST, including the spatial resolution difference and the penetration depth difference. In order to improve the accuracy of retrieved SST, this paper proposes a joint inversion algorithm for SST from combining microwave brightness temperature and infrared brightness temperature, which has also taken the influence of wind speed and atmosphere into consideration. Experiments confirm that SST data obtained from the joint inversion algorithm are more accurate than those obtained from the existing inversion algorithms.

In order to improve the spatial resolution of a one-dimensional aperture synthesis (1-D AS) radiometer without increasing the size of the antenna array, the method of visibility extension (VE) is proposed in this article. In the VE method, prior information about the visibility distribution of various scenes is learnt by a residual convolutional neural network (ResCNN). Specifically, the relationship between the distribution of low-frequency visibility and that of high-frequency visibility is learnt. Then, the ResCNN is used to estimate the high-frequency visibility samples from the low-frequency visibility samples obtained by the AS system. Furthermore, the low- and high-frequency visibility samples are combined to reconstruct the brightness temperature image of the scene, to enhance the spatial resolution of AS. The simulation and experiment both demonstrate that the VE method can enhance the spatial resolution of 1-D AS.