We present a novel echo-based synthetic aperture radar (SAR) image simulation method that comprehensively utilizes both SAR effective view and ray tracing algorithms. To improve the fidelity of simulated SAR images, we first design an SAR effective view algorithm to process the selected facet target model, with the purpose of discretizing the facets in the SAR effective view into lattice targets and ensuring that the interval of lattice targets is set strictly following the Nyquist sampling law. Then, we combine the ray tracing algorithm and SAR echo time-domain simulation and perform SAR echo time-domain simulation of lattice targets based on ray tracing. Various kinds of backscatter coefficient for each point target can be recorded, corresponding to the number of transmitted pulses within the synthetic aperture time. The echo matrixes of lattice targets are superimposed to generate the raw echo signal. Finally, the raw echo signal is processed by using the range-Doppler (RD) imaging algorithm to obtain the simulated SAR image. We conducted SAR image simulation tests on several facet target models, including car body, assault boat, and airplane, with different material properties. The simulated SAR images obtained by the proposed method were qualitatively and quantitatively evaluated. The experimental results verify the effectiveness of the proposed method.

We present a novel fast synthetic aperture radar (SAR) image simulation method based on the echo matrix cell algorithm including multiple scattering. To improve the efficiency of SAR image simulation while ensuring the fidelity of the simulated results, we first discretized the target facets set in the SAR beams footprint into lattice targets using the range-Doppler (RD) imaging geometry model and provided the basis for simulating electromagnetic wave transmission. Based on the simulation of electromagnetic waves transmission, we used the ray tracing algorithm to calculate the multiple backscattering field including multi-polarimetric information and various material properties. Then, based on the echo matrix cell algorithm, we set the echo matrix cell as the subfield of the target backscattering field and designed the CUDA kernel function to implement a computation parallelization for SAR echo generation. All the echo matrix cells are traversed in parallel to obtain the total backscattering field of the target, reproducing the time-varying characteristic of the backscatter coefficient for each lattice target within the synthetic aperture time. The echo signal is processed using the RD imaging algorithm to obtain the simulated SAR image. Finally, we select some targets including aircraft carrier and airplane models for simulation tests. The computation efficiency is improved over 170-fold by comparing the computations of the proposed method and CPU single-thread. We also performed some qualitative and quantitative evaluations on the fidelity of the simulated SAR images. The experimental results verify the effectiveness of the proposed method.

Although winter wheat has been mapped by remote sensing in several studies, such mapping efforts did not sufficiently utilize contextual information to reduce the noise and still depended heavily on optical imagery and exhausting classification approaches. Furthermore, the influence of similarity measures on winter wheat identification remains unclear. To overcome these limitations, this study developed an object-based automatic approach to map winter wheat using multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. First, after S1 and S2 images were preprocessed, the Simple Non-Iterative Clustering (SNIC) algorithm was used to conduct image segmentation to obtain homogeneous spatial objects with a fusion of S1 and S2 bands. Second, the temporal phenology patterns (TPP) of winter wheat and other typical land covers were derived from object-level S1 and S2 imagery based on the collected ground truth samples, and two improved distance measures (i.e., a composite of Euclidean distance and Spectral Angle Distance, (ESD) and the difference–similarity factor distance (DSF)) were built to evaluate the similarity between two TPPs. Third, winter wheat objects were automatically identified from the segmented spatial objects by the maximum between-class variance method (OTSU) with distance measures based on the unique TPP of winter wheat. According to ground truth data, the DSF measure was superior to other distance measures in winter wheat mapping, since it achieved the best overall accuracy (OA), best kappa coefficient (Kappa) and more spatial details for each feasible band (i.e., NDVI, VV, and VH/VV), or it obtained results comparable to those for the best one (e.g., NDVI + VV). The resultant winter wheat maps derived from the NDVI band with the DSF measure achieved the best accuracy and more details, and had an average OA and Kappa of 92% and 84%, respectively. The VV polarization with the DSF measure produced the second best winter wheat maps with an average OA and Kappa of 91% and 80%, respectively. The results indicate the great potential of the proposed object-based approach for automatic winter wheat mapping for both optical and Synthetic Aperture Radar (SAR) imagery.

Line detection on synthetic aperture radar (SAR) images is still challenging due to the existence of strong speckles. This article proposes a novel line detection method for SAR images based on vector Radon transform (VRT). First, the ratio of exponentially weighted averages operator is determined to calculate the edge map. Then, the VRT is proposed to transform the edge map into the parameter map and decompose the parameter map along the angle of projection into two mutually perpendicular components, i.e., parallel component and vertical component, which strips off part of the random edge information and ensures the sharpness of the peaks (local extremum). Peaks are detected in the parallel component. Finally, the intersection analysis technique is adopted to remove the redundant lines. Experiments are carried out with six amplitude-format SAR images of various bands, resolutions, and polarizations from Chinese airborne SAR systems, GaoFen-3 (GF-3) satellite, and TerraSAR-X satellite. The results demonstrate the outperformance of the proposed method. Furthermore, a road edge detection scheme is designed based on the proposed line detection method. The experimental results of the road edge detection further confirm the effectiveness of the proposed method and show its potential for practical use.

Using traditional neural network algorithms to adapt to high-resolution range profile (HRRP) target recognition is a complex problem in the current radar target recognition field. Under the premise of in-depth analysis of the long short-term memory (LSTM) network structure and algorithm, this study uses an attention model to extract data from the sequence. We build a dual parallel sequence network model for rapid classification and recognition and to effectively improve the initial LSTM network structure while reducing network layers. Through demonstration by designing control experiments, the target recognition performance of HRRP is demonstrated. The experimental results show that the bidirectional long short-term memory (BiLSTM) algorithm has obvious advantages over the template matching method and initial LSTM networks. The improved BiLSTM algorithm proposed in this study has significantly improved the radar HRRP target recognition accuracy, which enhanced the effectiveness of the improved algorithm.

Lake ice thickness (LIT) is one of the key climate variables in the lake ice domain, but there are currently large uncertainties in the retrieval of LIT. We present and validate a new LIT retrieval method that utilizes ICESat-2 data to assist CryoSat-2 echo peak selection, aiming to improve the accuracy of LIT retrieval and enable data acquisition without on-site measurements. The method involves screening out similar ICESat-2 and CryoSat-2 tracks based on time and space constraints. It also involves dynamically adjusting the range constraint window of CryoSat-2 waveforms based on the high-precision lake ice surface ellipsoid height obtained from ICESat-2/ATL06 data. Within this range constraint window, the peak selection strategy is used to determine the scattering interfaces between snow-ice and ice-water. By utilizing the distance between the scattering horizons, the thickness of the lake ice can be determined. We performed the ice thickness retrieval experiment for Baker Lake in winter and verified it against the on-site measurement data. The results showed that the accuracy was about 0.143 m. At the same time, we performed the ice thickness retrieval experiment for Great Bear Lake (GBL), which does not have on-site measurement data, and compared it with the climate change trend of GBL. The results showed that the retrieval results were consistent with the climate change trend of GBL, confirming the validity of the proposed method.