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.

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.

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 article 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 article proposes a joint inversion algorithm for SST from combining microwave brightness temperature (BT) and infrared BT, 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.

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 paper proposes a method in which four tilted planar reflectors are set around an antenna array. This method is named two-dimensional (2-D) mirrored aperture synthesis 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 paper. 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.

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.

Mirrored aperture synthesis (MAS) can improve spatial resolution compared with conventional aperture synthesis (AS). In this paper, for 1D MAS and 2D MAS, the array optimization models with the autocorrelation are established. The optimal array configurations based on the models are presented. The simulation results demonstrate that the optimal array configurations have good performance in imaging and can achieve higher spatial resolution than other MAS array configurations with the same antenna number.