With the continuous expansion of traffic volume in China, pavement deterioration recognition is a key technique to reflect the real state of the roads and to provide the basis for decision of the maintenance plan. In this paper, a new pavement defect detection system based on Image Processing was developed which utilized Unmanned Aerial Vehicle (UAV) to avoid blocking the traffic flow and to enlarge monitoring range. It was divided into two subsystems: UAV intelligent control modulus and defect detection modulus. For the former, special programs were designed to achieve the functions such as automatically taking off and landing, automatically trajectory control, and camera and gimbal control. For the latter, based on the comparative analysis of the basic principles and multiple image groups experiments of normal edge detection algorithms, a combined method was proposed. This proposed method can not only automatically provide the high threshold that Canny algorithm needs to input manually, but also solve the lack of edge detection ability in OTSU segmentation algorithm. The efficiency and accuracy of the image processing is verified through the experiments. It is also showed that the weak edges in the image can be better retained by taking 1/3 to 1/2 of the high threshold as a low threshold in this combined method. According to both qualitative and quantitative analysis, it can be concluded that, among the normal algorithms taken into study, the proposed combination algorithm performs best in terms of recognition accuracy of crack types and crack geometry features extraction.

In order to reduce the complexity of the receiving system for wideband signals, a novel space-borne high-resolution synthetic aperture radar (SAR) system with the non-uniform hybrid sampling technology is proposed in this paper. The non-uniform hybrid sampling technology is firstly applied in the SAR imaging system for the detection of space targets. The non-uniform hybrid sampling technology is able to optimize the transmitted and receiving timing of SAR signals, reducing the requirement of the sampling rate of the analog-to-digital converter (ADC) for the reception of the wideband echoes from space targets. Meanwhile, according to the oversampling requirement of SAR imaging in the azimuth direction, a theoretical model of non-uniform hybrid sampling parameters and relative velocity between the SAR system and the space targets is established. A series of simulation experiments with different targets and different non-uniform hybrid parameters are performed, and an X-band SAR imaging experimental system is constructed to verify the effectiveness of the proposed non-uniform hybrid sampling technology. The experimental results show that the imaging resolution is better than 8 cm. When the non-uniform hybrid sampling interval is 15 us, the imaging quality is consistent with imaging results of the Nyquist real-time sampling, and it is easier to implement in the high-resolution imaging for space targets.

Non-imaging radar can achieve the detection and localization of moving targets, but it faces difficulties in the classification of targets. Aiming for the application of moving target classification by low/medium resolution non-imaging radar, this paper proposes a novel feature extraction method, based on the statistical characteristics of echo amplitude with the L-band fully polarimetric radar. A feature plane, composed of the 3rd-order central moment (skewness) and 4th-order central moment (kurtosis) as the statistical characteristics of the echo envelope, is established. In addition, two types of moving targets, pedestrians and non-motorized vehicles, are able to be effectively distinguished according to whether the echo signals of different polarizations have overlapping areas in the feature plane. The L-band fully polarimetric radar has been developed and field experiments have been conducted. The experimental results verify that the kurtosis and skewness of the fully polarimetric echo amplitude of the pedestrians are very close, and there is an overlapping area in the feature plane, while the statistical characteristics of the fully polarimetric echo signal of non-motor vehicles are different, and there is no overlapping area in the feature plane. This proposed feature extraction method has the advantage of being simple and robust, compared to the traditional imaging approach.

Soil moisture is a key variable governing land–atmosphere water and energy exchanges. This letter proposes a novel soil moisture retrieval technique based on GNSS-S (Global Navigation Satellite System–Scattering) radar. Unlike conventional GNSS-R systems that are restricted to specular reflection points, the proposed GNSS-S radar exploits distributed GNSS backscattering to enable continuous bistatic imaging of surface soil moisture. The system is mounted on an airborne or spaceborne platform and uses navigation satellites as illuminators of opportunity. GNSS-S imaging is achieved through signal-level preprocessing, backprojection-based image formation, geometric correction, and relative radiometric calibration. Airborne experiments demonstrate that continuous surface imaging with a spatial resolution of approximately 100 m × 100 m can be achieved using L-band GNSS signals. Comparative analysis with in-situ soil moisture measurements shows consistent spatial distribution trends, and quantitative evaluation reveals moderate to strong correlations depending on bistatic geometry. These results validate the feasibility of GNSS-S radar for area-wide soil moisture monitoring and highlight its potential as a complementary technique to conventional active SAR systems.