Pressure is a critical factor affecting the dielectric properties of rock materials. Previous studies have shown that the microwave dielectric properties of mineral rocks undergo regular changes under uniaxial pressure. In this study, diorite was selected as the specimen, and an open coaxial resonant microwave dielectric testing system was employed to investigate the microwave dielectric (2–18.3 GHz) properties of rock specimen under uniaxial and biaxial loading modes, respectively. Experimental results indicate that under uniaxial pressure, the dielectric constant at all frequency bands decreased gradually with the increasing of compressive stress, with an average decrease amplitude of 4.5%. Nevertheless, under the biaxial loading process, the change in dielectric constant exhibited a more complex pattern, which was closely related to the relationship between the axial stress and the lateral stress. When the axial stress was lower than the lateral stress, the dielectric constant increases significantly with the rise of axial stress. However, when the axial stress increased and got greater than the lateral stress, the dielectric constant began to decrease. The theoretical analysis based on dielectric physics indicate that both the changes in the number of polarization units and the polarization abilities of these polarization units influenced the dielectric properties of rocks under different loading conditions. This study is greatly important for revealing the change patterns and mechanisms of microwave dielectric properties of rocks, which implies that stress induced disturbances is an important factor to be considered in applying radar investigation and microwave remote sensing for the geological exploration, and geo-hazard perception.

The arrangement of the antenna array of the mirrored aperture synthesis (MAS) determines the transformation matrix for solving the cosine visibilities. When the transformation matrix is rank deficient, the solved cosine visibilities will introduce the rank-deficient error. For the two-dimensional (2-D) mirrored aperture synthesis (2-D MAS), the existing literature has discussed the relationship between the arrangement of the antenna array and the rank-deficient error but has not proposed an array that can achieve a full rank transformation matrix, which is called the rank-full array (RFA). Based on a new method for calculating the rank of the transformation matrix (RTM) and the literature on one-dimensional (1-D) RFAs, this letter proves that 2-D RFA cannot be obtained when the reflectors are all metal, and by introducing a reflector made of a magnetic conductor, 2-D RFA can be obtained. Besides, this letter obtained the existence conditions for 2-D RFA. Based on those conditions, two methods are proposed to obtain 2-D RFAs.

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.

When observing the Earth from space, the synthetic aperture radiometer antenna array is sometimes set as a non-uniform array. In non-uniform synthetic aperture radiometer image reconstruction, the existing brightness temperature image reconstruction methods include the grid method and array factor forming (AFF) method. However, when using traditional methods for imaging, errors are usually introduced or some prior information is required. In this article, we propose a new IASR imaging method with deep convolution neural network (CNN). The frequency domain information is extracted through multiple convolutional layers, global pooling layers, and fully connected layers to achieve non-uniform synthetic aperture radiometer imaging. Through extensive numerical experiments, we demonstrate the performance of the proposed imaging method. Compared to traditional imaging methods such as the grid method and AFF method, the proposed method has advantages in image quality, computational efficiency, and noise suppression.

In two-dimensional mirrored aperture synthesis (2-D MAS), the rank of the transformation matrix can affect the accuracy of the solved cosine visibilities; therefore, it has an impact on the accuracy of the reconstructed brightness temperature image. In this article, the influence of the rank deficient on the accuracy of the reconstructed brightness temperature image of 2-D MAS is discussed. An analysis of the rank-deficient error is performed by computing its impact on the radiometric accuracy for a reference scene. Two correction methods based on multiple measurements are proposed to correct the rank-deficient error. The simulations and experiments are carried out to demonstrate the effectiveness of the proposed methods.