- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Based on Spectrum Modeling and Optimization
摘要： Bistatic synthetic aperture radar (SAR) is able to break through the limitation of monostatic SAR on forward-looking area imaging with appropriate geometry configurations. Thanks to such an ability, bistatic forward-looking SAR (BFSAR) has extensive potential practical applications. For the focusing problem of conventional side-looking SAR, ω–k algorithm is accepted as the ideal solution. In this paper, the ω–k algorithm will be discussed in BFSAR geometry. As for the bistatic configuration, spatial domain linearization procedure should be carried out to extract a range variable from the point target reference spectrum (PTRS) in the existing ω–k algorithms. With respect to the BFSAR geometry, nevertheless, the linearization procedure reduces the accuracy of PTRS seriously. To cope with such a problem, a novel ω–k algorithm for BFSAR is proposed. In the proposed method, the range variable is modeled as a parameterized polynomial, and the corresponding PTRS with respect to two-dimensional frequencies is established. Then, the parameters are estimated by differential evolution to minimize the PTRS errors for each range coordinate and frequency point. Based on the estimated PTRS, the BFSAR data can be focused well by the proposed ω–k algorithm. Simulation results verify the effectiveness of the proposed method.
关键词： Bistatic forward-looking synthetic aperture radar (BFSAR),differential evolution (DE),ω–k,point target reference spectrum (PTRS)
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Ship Discrimination with Deep Convolutional Neural Networks in Sar Images
摘要： With the advantages of all-time, all-weather, and wide coverage, synthetic aperture radar (SAR) systems are widely used for ship detection to ensure marine surveillance. However, the azimuth ambiguity and buildings exhibit similar scattering mechanisms of ships, which cause false alarms in the detection of ships. To address this problem, self-designed deep convolutional neural networks with the capability to automatically learn discriminative features is applied in this paper. Two datasets, including one dataset reconstructed from IEEEDataPort SARSHIPDATA and the other constructed from 10 scenes of Sentinel-1 SAR images, are used to evaluate our approach. Experimental results reveal that our model achieves more than 95% classification accuracy on both datasets, demonstrating the effectiveness of our approach.
关键词： ship discrimination,Sentinel-1 images,synthetic aperture radar,deep convolutional neural networks
更新于2025-09-23 15:23:52
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Automatic bathymetry retrieval from SAR images
摘要： Bathymetry, the topography of the sea floor, is in high demand due to the increase in offshore constructions like wind parks. It is also an important dataset for climate change modelling, when sea level rises and changes in circulation currents are to be simulated. The retrieval of accurate bathymetry data is a cost-intensive task usually requiring a survey vessel charting the respective area. However, bathymetry can also be retrieved remotely using data from Earth observation satellites. The main point of this study is the development of a processor that allows the automatic derivation of gridded bathymetry information from spaceborne Synthetic Aperture Radar (SAR) data. Observations of sea state modifications in SAR images are used to derive the bathymetry in shelf areas using the shoaling effect, which causes wavelengths to become shorter when reaching shallower waters. The water depth is derived using the dispersion relation for surface water waves, which requires wavelength and wave period as input parameters. While the wavelength can be directly retrieved from the SAR image, for the peak period additional information and procedures are required, e.g. local measurements or complex SAR data. A method for automatically deriving the wave period for swell waves in SAR images was developed and tested in this paper. It uses depth data from public databases as initial values which are compared to derived depths iterating through possible peak periods along the calculation grid; the peak period resulting in a minimal root-mean-square deviation is then used for bathymetry calculation. The bathymetry derived from a TerraSAR-X acquisition of the Channel Islands is presented; the resulting peak wave period of 11.3 s fits well to nearby in situ measurement data.
关键词： Bathymetry,Remote sensing,Near-real time processing,Synthetic aperture radar
更新于2025-09-23 15:23:52
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Co-polarization channel imbalance phase estimation by corner-reflector-like targets
摘要： Polarimetric calibration is a critical step to suppress the potential system distortion before implementing any applications for polarimetric synthetic aperture radar (PolSAR). Among all the distortion elements, the crosstalk and cross-pol channel imbalance are generally estimated by the use of natural media, and the co-pol channel imbalance is traditionally solved by the use of corner reflectors (CRs). However, the deployment of ground CRs is costly and may even be impossible in some areas. Many bright point targets, such as poles, lamps, and corner points of structures, are commonly found in manmade regions. In particular, if the object orientation is parallel or perpendicular to the radar flight direction, some points will present similar polarimetric responses to trihedral or dihedral CRs. These points, which are referred to here as "CR-like targets", can be treated as a supplement to approximately solve the system distortion elements when CRs are unavailable. In this paper, we propose a novel step-by-step algorithm to determine the CR-like targets and estimate the co-pol channel imbalance phase in uncalibrated PolSAR imagery. Chinese X-band airborne and C-band satellite PolSAR data were used to test the proposed method. Compared with the CR-derived co-pol channel imbalance phase, the solution errors of the CR-like targets were 1.305° and 0.03° for the X- and C-band experiments, respectively. The results of the experiments confirm that the solutions of the CR-like targets are very close to those of ground-deployed CRs, and the proposed method can be considered as an effective way to calibrate PolSAR images when sufficient CR-like point targets are detected in manmade regions.
关键词： Corner reflector,Polarimetric synthetic aperture radar,Calibration,Target detection
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Change Detection in Semantic Level for SAR Images
摘要： Considering that the traditional change detection algorithms only focus on extracting the change area but ignore the change content identification, a novel change detection framework for synthetic aperture radar (SAR) images is proposed. The framework integrates the merits of unsupervised and supervised learning to detect changes in semantic level. First, the residual convolutional auto-encoder (RCAE) is designed to convert SAR image slices to the histogram representation. Then, we calculate the difference vectors and extract the change area by their norms. Finally, we classify the difference vectors of change region and identify the content of change. Experimental results indicate that the proposed method significantly achieves performance improvement over existing algorithms.
关键词： semantic,bag of visual words,synthetic aperture radar,auto-encoder,change detection
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Novel Tool for Unsupervised Flood Mapping Using Sentinel-1 Images
摘要： In this paper, we present a novel method for mapping flooded areas exploiting Sentinel-1 ground range detected products. The work introduces two novelties. As first, the input products. In fact, as far we know, no applications using these products has been so far presented in literature. Secondly, a new unsupervised methodology, based on the usage of opportune layers combined in a fuzzy decision system, is presented. Experimental results, obtained both on the single SAR image and on a couple of acquisitions in a change detection framework showed that our method is able to outperform the most popular classification techniques in terms of standard assessment parameters.
关键词： flooding,sentinel-1,classification,fuzzy systems,Synthetic aperture radar
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Temporal Difference and Density-Based Learning Method Applied for Deforestation Detection Using ALOS-2/PALSAR-2
摘要： Remote sensing has established as key technology for monitoring of environmental degradation such as forest clearing. One of the state-of-the-art microwave EO systems for forest monitoring is Japan’s L-band ALOS-2/PALSAR-2 which provides outstanding means for observing tropical forests due its cloud and canopy penetration capability. However, the complexity of the physical backscattering properties of forests and the associated spatial and temporal variabilities, render straightforward change detection methods based on simple thresholding rather inaccurate with high false alarm rates. In this paper, we develop a framework to alleviate problems caused by forest backscatter variability. We define three essential elements, namely “structures of density”, “speed of change”, and “expansion patterns” which are obtained by differential computing between two repeat-pass PALSAR-2 images. To improve both the detection and assessing of deforestation, a “deforestation behavior pattern” is sought through temporal machine learning mechanism of the three proposed elements. Our results indicate that the use of “structure of density” can introduce a more robust performance for detecting deforestation. Meanwhile, “speed of change” and “expansion pattern” are capable to provide additional information with respect to the drivers of deforestation and the land-use change.
关键词： Density-Based,Temporal Difference Learning,Synthetic Aperture Radar (SAR)
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Sea Ice Change Detection in SAR Images Based on Collaborative Representation
摘要： Sea ice change detection from synthetic aperture radar (SAR) images is important for navigation safety and natural resource extraction. This paper proposed a sea ice change detection method from SAR images based on collaborative representation. First, neighborhood-based ratio is used to generate a difference image (DI). Then, some reliable samples are selected from the DI by hierarchical fuzzy C-means (FCM) clustering. Finally, based upon these samples, collaborative representation method is utilized to classify pixels from the original SAR images into unchanged and changed class. From there, the final change map can be obtained. Experimental results on two real sea ice datasets demonstrate the superiority of the proposed method over two closely related methods.
关键词： sea ice change detection,synthetic aperture radar,clustering method,collaborative representation
更新于2025-09-23 15:23:52
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GLRT Detection of Micromotion Targets for the Multichannel SAR-GMTI System
摘要： This letter investigates the micromotion target detection problem for the multichannel synthetic aperture radar (SAR)- ground moving target indication system. The multichannel SAR signal models of the micromotion target and the ground clutter in the raw data domain are established firstly. Then the generalized likelihood ratio test (GLRT) of the micromotion target is derived. Based on the analysis of the probability density functions of the test statistics, theoretical detection performance dependent on the micromotion parameters is provided. Simulated heterogeneous SAR data validate the effectiveness of the GLRT detector.
关键词： Ground moving target indication (GMTI),synthetic aperture radar (SAR),micromotion
更新于2025-09-23 15:23:52
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Imbalanced Learning-Based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net
摘要： Change detection is a quite challenging task due to the imbalance between unchanged and changed class. In addition, the traditional difference map generated by log-ratio is subject to the speckle, which will reduce the accuracy. In this letter, an imbalanced learning-based change detection is proposed based on PCA network (PCA-Net), where a supervised PCA-Net is designed to obtain the robust features directly from given multitemporal synthetic aperture radar (SAR) images instead of a difference map. Furthermore, to tackle with the imbalance between changed and unchanged classes, we propose a morphologically supervised learning method, where the knowledge in the pixels near the boundary between two classes is exploited to guide network training. Finally, our proposed PCA-Net can be trained by the data sets with available reference maps and applied to a new data set, which is quite practical in change detection projects. Our proposed method is veri?ed on ?ve sets of multiple temporal SAR images. It is demonstrated from the experiment results that with the knowledge in training samples from the boundary, the learned features bene?t change detection and make the proposed method outperform than supervised methods trained by randomly drawing samples.
关键词： Change detection,imbalance learning,synthetic aperture radar (SAR) images,PCA network (PCA-Net)
更新于2025-09-23 15:23:52