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Objectives: Multi-temporal interferometric synthetic aperture radar (InSAR) technology is widely used in deformation monitoring over wide areas due to its characteristics of large-scale, high spatial-temporal resolution and the capability to monitor deformation signal with millimeter precision even submillimeter. For the time-consuming problem in acquiring image and data preprocessing over research area, this paper uses looking inside the continents from space with synthetic aperture radar (LiCSAR) products and takes a new time series analysis LiCSBAS method, which greatly improve the computational efficiency. Methods: The quality check and the closure error of the phase loop are used to detect the unwrapping error in the two-dimensional phase unwrapping, and the interferogram with low coherence and the large error pixels are excluded. To minimize atmospheric artifacts, utilizing the generic atmospheric correction online service for In‑ SAR (GACOS) products and based on this, a method for joint estimation of deformation and atmospheric phase is proposed. Taking the Chengdu Plain and western mountainous area as the study area, totally 90 [ABSTRACT FROM AUTHOR]

Objectives: In the research of time-series interferometric synthetic aperture radar (TS-InSAR) deformation monitoring, the conventional triangular reflectors are increasingly used as point targets. But the conventional triangular reflectors cannot meet the requirements of obtaining multi-angle deformation information on ascending and descending track scenes. They need to be tailored for specific datasets to meet monitoring needs. Methods: A set schemes for corner reflector design and accuracy verification are constructed. According to the characteristics of X-band radar satellite satellite, a two-way adjustable rectangular corner reflector is designed to meet the monitoring scene of the ascending and descending track. Due to the relationship between the radar cross section(RCS) and the size of the corner reflector, the side length of the bidirectional rectangular corner reflector is calculated to be 0.35 m. Ten bidirectional rectangular corner reflectors are placed on the side slope of the highway in the monitoring area. By artificially simulating the two-dimensional deformation, the detection accuracy and reliability of the TS-InSAR on the bidirectional rectangular corner reflector is verified. Results: Under the condition of effectively eliminating the sidelobe effect of the pixel, the designed bidirectional rectangular corner reflector maintains a high and stable backscatter ing intensity, which can be easily extracted. Through the analysis of the weather conditions at the time of imaging, heavy rainfall has a certain influence on the backscattering intensity of corner reflectors. Under the blind experimental conditions of artificially adjusting the height of the corner reflector, CR1 and CR2 are moved between May 13, 2018 and May 24, 2018. The root mean square error is 0.6 mm. The deformation detection accuracy reaches the sub-millimeter level. Conclusions: The TS-InSAR is highly sensitive to the deformation of the designed bidirectional rectangular corner reflector. The bidirectional rectangular corner reflector can meet the requirements of obtaining multi-angle deformation information on ascending and descending track scenes. [ABSTRACT FROM AUTHOR]

This paper constructed a new TerraSAR-X dataset named GDUT-Nansha and then proposed a new lightweight semantic segmentation algorithm using synthetic aperture radar images（ SAR）. It is difficult to apply traditional deep learning models to SAR datasets with small volume due to the tremendous amount of parameters. A revision scheme is, therefore, put forward to deal with such problem. This paper accomplished an improved lightweight convolutional neural network model named revised weighted loss ENet（ RWL-ENet） for SAR images. The current study introduced a weighted loss function to solve the problem of imbalance of training datasets. Compared with other classical convolution neural network models, the efficiency and robustness of the new dataset were validated. Meanwhile, RWL-ENet model attained 0. 884,0. 804, and 0. 645 in terms of three quantitative metrics, including pixel accuracy（ PA）, mean pixel accuracy（ m PA）, and mean intersection over union（ m IoU）. In addition, the parameters of this new proposed model are much less than other classic network models. [ABSTRACT FROM AUTHOR]

In order to reduce the multiplicative speckle noise of synthetic aperture radar SAR image. an improved non-local mean (NLM) algorithm is proposed in this paper. Firstly, the coefficient of variation (CV) was used to estimate the SAR image region division threshold and estimate area division factor based on threshold. Thus. the SAR image was divided into texture area and homogeneous area by area division factor. Then. similarity windows of different scales were used to estimate the ESM corresponding to pixels in different regions, and the ESM matrix of each pixel in the search window under the similarity window and the ESM matrix of the center pixel were selected further. Moreover. the two-norm of the ratio of the two corresponding elements were used to construct the similarity measurement parameter that adapts to the multiplicative noise. filter value was obtained by combining the similarity parameter with the adaptive de cay factor dominated by CV. Finally, the non local average weight filtering of the SAR image was realized by different regions with different filtering values. Compared with NLM filtering algorithm based on the mean ratio and the coefficient of variation. the proposed algorithm performs better in that the equivalent numbers of looks increased by more than 0,3% and edge preservation index increased by more than 0.1% [ABSTRACT FROM AUTHOR]

Aiming at the key problem of low precision of the deformation data due to the inaccurate correction of the original image interference phase error based on the ground-based synthetic aperture radar(GB-SAR) in open pit slope， the sources and distribution characteristics of interference phase error in original image were analyzed with the original GB-SAR image of Malanzhuang open-pit iron mine as the data source. The method of extracting PS points and high-quality PS points by using triple threshold was also proposed. In addition， a multi-regression model was established based on the high-quality PS point phase and pixel coordinates. According to the model， the PS point phase was corrected to obtain the accurate deformation phase and the single image deformation， and the superposition deformation analysis was performed on the time series. The results show that the GB-SAR deformation monitoring error correction method based on multiple regression model can accurately correct the GB-SAR deformation monitoring error and improve the accuracy of deformation monitoring data. [ABSTRACT FROM AUTHOR]

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Objective The geometric correction of SAR imagery is an important step in SAR image processing. This process has a certain degree of computational complexity and requires a certain geometric positioning model. A GPU-supported massively parallel processing method is presented for the geometric correction of space-borne SAR imagery. Method This method exploits the RPC model. The method takes full advantage of two facts. A GPU has large computational resources, and the processing steps are the same for each pixel in geometric correction. In the course of massively parallel processing, a large amount of pixels are imported into the GPU at each time, and one thread is allocated for one pixel. Each thread performs the steps, which include the calculation of rational function, transformation for projection, resampling, and so on, with high computational complexity. The optimal configuration of two parameters, i. e., dimGrid and dimBlock, improves parallel performance. Large SAR image frames with different sizes can be processed by block partition. Result Experimental results show that the proposed method can achieve computational speedups ranging from 38 to 44. Meanwhile, the speedup for the whole procedure is recorded to analyze the features of the GPU-based parallel computing objectively and thoroughly. The factors affecting the speedup for the whole procedure are discussed according to the results of several experiments, and an optimal approach that reads and writes a large block to promote I/O performance is proposed. Conclusion The straightforward method has broad applicability and can be used for most space-borne SAR sensors and for different image frame sizes. The method also achieves obvious acceleration. [ABSTRACT FROM AUTHOR]