- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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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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
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Retrieval of Ionospheric Faraday Rotation Angle in Low-frequency Polarimetric SAR Data
摘要： A low-frequency spaceborne synthetic aperture radar (SAR) working system, e.g., operating at the L-band or P-band, has great advantages of military target detection and biomass monitoring. Nevertheless, it is more susceptible to ionospheric effects compared with the higher frequency system. A trans-ionospheric wave propagation model is established in this paper to incorporate ionospheric effects on SAR signals. As one of the signi?cant distortion sources for the polarimetric SAR (PolSAR), Faraday rotation (FR) is mainly imposed by background ionosphere, and its spatial variation is discussed. FR estimators have been devised in succession to estimate FR angle (FRA), and various potential novel estimators can still be derived. But, from a viewpoint of theoretical expressions, the earliest estimator is bound to be the optimal one. Based on PolSAR real data, this mathematical conclusion is further validated via comprehensive performance analysis as to estimation bias and standard deviation rather than the existent root-mean-square principle. Finally, a step-by-step procedure of the FRA map is proposed and operated with an application of the airborne P-band PolSAR data. In particular, the ambiguity error of FRA estimates within a SAR observation is simulated and resolved. By processing the ALOS-2 real data, the spatial distribution of FRAs is retrieved and used to operate ionospheric total electron content soundings.
关键词： FR angle (FRA) map,ionospheric TEC soundings,Faraday rotation (FR) estimators,Spaceborne 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 - A Car-Borne SAR System for Interferometric Measurements: Development Status and System Enhancements
摘要： Terrestrial radar systems are used operationally for area-wide measurement and monitoring of surface displacements on steep slopes, as prevalent in mountainous areas or also in open pit mines. One limitation of these terrestrial systems is the decreasing cross-range resolution with increasing distance of observation due to the limited antenna size of the real aperture radar or the limited synthetic aperture of the quasi-stationary SAR systems. Recently, we have conducted a first experiment using a car-borne SAR system at Ku-band, demonstrating the time-domain back-projection (TDBP) focusing capability for the FMCW case and single-pass interferometric capability of our experimental Ku-band car-borne SAR system. The cross-range spatial resolution provided by such a car-based SAR system is potentially independent from the distance of observation, given that an adequate sensor trajectory can be built. In this paper, we give (1) an overview of the updated system hardware (radar setup and high-precision combined INS/GNSS positioning and attitude determination), and (2) present SAR imagery obtained with the updated prototype Ku-band car-borne SAR system.
关键词： azimuth focusing,Ku-band,SAR imaging,ground-based SAR system,car-borne SAR,parallelization,SAR interferometry,GPU,CUDA,interferometry,CARSAR,Synthetic aperture radar (SAR)
更新于2025-09-23 15:22:29
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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 - An Tensor-Based Corn Mapping Scheme with Radarsat-2 Fully Polarimetric Images
摘要： As one of the most essential economic and industrial crops globally, corn holds a very important position in China’s agricultural industry. Corn mapping is one of the most concerned fields in agricultural surveillance. However, compared with the utilization of backscattering coefficients, the polarimetric information was not fully discussed in previous corn mapping researches. In this paper, we use the coherency matrix of mid to late term multi-temporal fully polarimetric synthetic aperture radar (FP SAR) data to discriminate corn cultivation areas. The tensor representation is adopted for PolSAR analysis, with the help of multilinear principal component analysis (MPCA) to reduce feature dimensions. The importance of polarimetric information is discussed. This paper illustrates that good corn discrimination could be achieved with only mid to late term FP SAR data.
关键词： corn mapping,synthetic aperture radar (SAR),decision tree,multi-temporal SAR,multilinear principal component analysis (MPCA),polarimetric SAR (PolSAR)
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on RFID Technology & Application (RFID-TA) - Macau, Macao (2018.9.26-2018.9.28)] 2018 IEEE International Conference on RFID Technology & Application (RFID-TA) - Research on Fast Algorithm for General Bistatic SAR Raw Signal
摘要： A fast algorithm for general bistatic synthetic aperture radar (SAR) raw signal based on two-dimensional frequency domain is proposed. The SAR echo signal model is established by the algorithm, the accurate expression of two-dimensional frequency domain for the bistatic SAR echo signal are deduced with series reversion. Simulating large range and azimuth coupling, the accurate computation of echo signal is realized and the complexity of the algorithm is analyzed. The simulation results show that the simulation speed of SAR echo is greatly improved under the premise of ensuring the high phase accuracy, and the validity of the algorithm is proved.
关键词： synthetic aperture radar (SAR),bistatic,series reversion,echo simulation
更新于2025-09-23 15:22:29
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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 - Desnet: Deep Residual Networks for Descalloping of Scansar Images
摘要： Scalloping is one of the critical problems in ScanSAR images. It not only affects image visualization, but also influences the quantitative applications such as surface wind and wave retrievals in the ocean area. The existing method of descalloping needs artificial parameter setting and lacks generality in the image domain. A novel deep neural network based on residual learning for descalloping of ScanSAR images is proposed in this paper. The proposed method can eliminate scalloping patterns and has strong adaptive ability, which can handle inhomogeneous scalloping patterns and different scenarios. Experiments on GF-3 ScanSAR images verify the good performance of this method. The code for our models is available online.
关键词： synthetic aperture radar (SAR),deep neural network,scalloping patterns,ScanSAR,Residual learning
更新于2025-09-23 15:22:29
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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 - Two-dimensional Spectrum for Diving Stage SAR Processing with High-order Equivalent Range Model
摘要： For airborne synthetic aperture radar (SAR) processing in its diving stage, the diving velocity brings additional range variance to the range history in the synthetic aperture time. The traditional hyperbolic range model is not accurate enough to approximate the actual range equation and the precise two-dimensional spectrum cannot be achieved. In order to address this problem, a highly accurate spectrum deduction based on high-order equivalent range model is proposed in this paper. By introducing the high-order terms, more degrees-of-freedom are obtained for the equivalent range model and the actual range history can be accurately fitted. Based on the achieved range model, the two-dimensional spectrum for diving stage SAR can be accessed. Simulation experiments are carried out to validate the effectiveness of proposed spectrum.
关键词： two-dimensional spectrum,diving stage,range model,Synthetic aperture radar (SAR)
更新于2025-09-23 15:22:29
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Ratio-Based Multitemporal SAR Images Denoising: RABASAR
摘要： In this paper, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well preserved thanks to the multitemporal mean. The proposed approach can be divided into three steps: 1) estimation of a 'superimage' by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the 'superimage'; and 3) computation of the denoised image by remultiplying the denoised ratio by the 'superimage.' Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising images from the original multitemporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the 'superimage' that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio and structure similarity index) as well as visually on simulated and synthetic aperture radar (SAR) time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures.
关键词： ratio image,speckle reduction,Multitemporal synthetic aperture radar (SAR) series,superimage
更新于2025-09-23 15:22:29