In mirrored aperture synthesis (MAS), the existing brightness temperature image reconstruction methods include inverse cosine transform and impulse matrix reconstruction methods. However, the quality of the MAS brightness temperature images reconstructed by the existing methods is still poor and needs to be improved. This article proposes a method of MAS brightness temperature image reconstruction with deep convolutional neural network (CNN). The network includes two fully connected (FC) layers, multiple convolutional layers, and deconvolutional layers, which realize the image reconstruction for MAS. This method uses deep CNN to learn the MAS image reconstruction mapping and system errors, so as to improve the performance of the brightness temperature image reconstruction. Both simulation and experimental results verify that the performance of the proposed MAS-CNN method is better than the existing MAS image reconstruction methods.

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.

Setting two reflectors perpendicular to each other and an array produces mirrored aperture synthesis (MAS), which obtains a higher spatial resolution than aperture synthesis (AS) for the same antenna array. The proposal of the MAS theory and the experimental verification suggest that the spatial resolution can be improved by setting the reflector. This article proposes a method in which four tilted planar reflectors are set around an antenna array. This method is named 2-D mirrored AS with four tilted planar reflectors (2-D MAS-T). Because 2-D MAS-T uses more reflectors than MAS, the spatial resolution is further improved. The principle of 2-D MAS-T with an antenna array is given in this article. The relationship between the size of the reflectors, the spatial resolution, and the field of view (FOV) is obtained. The simulation and experimental results demonstrate that 2-D MAS-T can achieve a higher spatial resolution with the same antenna array than 2-D AS and 2-D MAS.

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.

To increase the spatial resolution of passive microwave radiometry, mirrored aperture synthesis (MAS) was presented. In this letter, the method of cosine visibility extension (CVE) is proposed to further enhance the spatial resolution of 1-D MAS. In the CVE method, a convolutional neural network (CNN) is used to learn the distribution of the cosine visibility (CV), specifically the relationship between the low- and high-frequency CV distributions of various scenes. Then, the high-frequency CVs are estimated by the CNN according to the low-frequency CVs obtained by MAS. The high- and low-frequency CVs are combined in MAS image reconstruction to enhance the spatial resolution of MAS. The simulation and experiment indicate that the CVE method can effectively enhance the spatial resolution of MAS.