This letter presents preliminary results of an attempt to monitor rice crop growth using RADARSAT-2 quad polarization synthetic aperture radar (SAR) data. Three RADARSAT-2 quad-polarization SAR images are collected from transplanting to rice crop harvesting. Ground truth data, such as rice height and biomass, are measured during RADARSAT-2 data acquisition in Hainan Province, South China. The correlation between backscattering coefficient and rice growth parameters is analyzed, and then, a rice field mapping method with quad polarization SAR image is developed. Experiments show that an HV or VH image backscattering coefficient exhibits the best correlation with rice age after transplantation. Furthermore, the HV or VH image is also more suitable for retrieving rice growth parameters, such as rice height and dry biomass, for FQ4 RADARSAT-2 SAR data. The ratio image of HH/VV possesses high separability required to distinguish rice crop from banana, forest, and river. Results indicate that RADARSAT-2 quad polarization SAR data presented enormous potential for monitoring rice crop growth.

High-resolution synthetic aperture radar (SAR) data have been widely used in marine environmental protection, marine environmental monitoring, and marine traffic management. Ship detection is one of the important parts of SAR data for marine applications. This letter focuses on the feature analysis of ships in high-resolution SAR images and proposes an improved optimizing algorithm for ship detection. A fast block detector is designed to extract sea clutter in a uniform local area, and then a constant false alarm rate detector is employed. Based on the kernel density estimation of ships, aspect ratio, and pixel points, ships are identified. TerraSAR-X and COSMO-SkyMed images are used to test our algorithm. The experimental results show that this algorithm can be implemented with time-saving, high-precision ship extraction, feature analysis, and detection.

Synthetic aperture radar (SAR) satellites produce large quantities of remote sensing images that are unaffected by weather conditions and, therefore, widely used in marine surveillance. However, because of the hysteresis of satellite-ground communication and the massive quantity of remote sensing images, rapid analysis is not possible and real-time information for emergency situations is restricted. To solve this problem, this paper proposes an on-board ship detection scheme that is based on the traditional constant false alarm rate (CFAR) method and lightweight deep learning. This scheme can be used by the SAR satellite on-board computing platform to achieve near real-time image processing and data transmission. First, we use CFAR to conduct the initial ship detection and then apply the You Only Look Once version 4 (YOLOv4) method to obtain more accurate final results. We built a ground verification system to assess the feasibility of our scheme. With the help of the embedded Graphic Processing Unit (GPU) with high integration, our method achieved 85.9% precision for the experimental data, and the experimental results showed that the processing time was nearly half that required by traditional methods.

Compared with optical sensors, Synthetic Aperture Radar (SAR) can provide important damage information due to its ability to map areas affected by earthquakes independently from weather conditions and solar illumination. In 2013, a new TerraSAR-X mode named staring spotlight (ST), whose azimuth resolution was improved to 0.24 m, was introduced for various applications. This data source made it possible to extract detailed information from individual buildings. In this paper, we present a new concept for individual building damage assessment using a post-event sub-meter very high resolution (VHR) SAR image and a building footprint map. With the building footprint map, the original footprints of buildings can be located in the SAR image. Based on the building imaging analysis of a building in the SAR image, the features in the building footprint can be extracted to identify standing and collapsed buildings. Three machine learning classifiers, including random forest (RF), support vector machine (SVM) and K-nearest neighbor (K-NN), are used in the experiments. The results show that the proposed method can obtain good overall accuracy, which is above 80% with the three classifiers. The efficiency of the proposed method is demonstrated based on samples of buildings using descending and ascending sub-meter VHR ST images, which were all acquired from the same area in old Beichuan County, China.

Ship monitoring has a wide range of applications in marine activities and maritime management. Spaceborne synthetic aperture radar (SAR) technology has an advantage in ship detection over a vast region compared to other technologies. Recently, high-resolution SAR satellites have made ship recognition and classification possible from space. In July 2010, a ship recognition campaign was performed in the East China Sea. In situ investigation of ship types was carried out during the periods when the Italian COSMO-SkyMed satellites passed over the test site. Automatic Identification System (AIS) information was collected. Based on this information, a novel hierarchical ship classifier for COSMO-SkyMed SAR data was proposed. A total of 41 ship chips were cut from the SAR images for later classification. After preprocessing of the ship chips, geometric and backscattering characteristics of various ship types were analyzed. The ships were classified into bulk carriers, container ships, and oil tankers, with an accuracy of 93.3%, 80.0%, and 72.7%, respectively. Further investigation of the backscattering features with various illumination conditions is still in progress.

With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression problem of the traditional log-ratio method, the spatial distance and intensity similarity are combined to generate a weighting function to obtain a weighted difference image. By fusing the weighted difference image and the bitemporal original images, the three-channel color difference image is generated for building change detection. Due to the complexity of urban environments and the small scale of building changes, the residual U-Net network is used instead of fixed statistical models and the construction and classifier of the network are modified to distinguish between different building changes. Three scenes of Sentinel-1 interferometric wide swath data are used to validate the proposed method. The experimental results and comparative analysis show that our proposed method is effective for urban building change detection and is superior to the original U-Net and SVM method.