Application of a maximum likelihood algorithm to the detection of targets in synthetic aperture radar (SAR) images

1994 ◽  
Author(s):  
William H. Schoendorf ◽  
Thomas L. Marzetta ◽  
Leonid I. Perlovsky ◽  
Elizabeth A. Martinsen ◽  
Ronald G. Wallace ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Maximum Likelihood Algorithm ◽  
Sar Images ◽  
Aperture Radar

scholarly journals A Novel Orthogonal Waveform Separation Scheme for Airborne MIMO-SAR Systems

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3580 ◽  
Author(s):  
Jie Wang ◽  
Ke-Hong Zhu ◽  
Li-Na Wang ◽  
Xing-Dong Liang ◽  
Long-Yong Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Synthetic Aperture Radar ◽  
3D Imaging ◽  
Synthetic Aperture ◽  
Separation Scheme ◽  
Sar Images ◽  
Wide Swath ◽  
Aperture Radar

In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems is seriously limited. This is mainly because the superposed echoes of the multiple transmitted orthogonal waveforms cannot be separated perfectly. The imperfect separation will introduce ambiguous energy and degrade SAR images dramatically. In this paper, a novel orthogonal waveform separation scheme based on echo-compression is proposed for airborne MIMO-SAR systems. Specifically, apart from the simultaneous transmissions, the transmitters are required to radiate several times alone in a synthetic aperture to sense their private inner-aperture channels. Since the channel responses at the neighboring azimuth positions are relevant, the energy of the solely radiated orthogonal waveforms in the superposed echoes will be concentrated. To this end, the echoes of the multiple transmitted orthogonal waveforms can be separated by cancelling the peaks. In addition, the cleaned echoes, along with original superposed one, can be used to reconstruct the unambiguous echoes. The proposed scheme is validated by simulations.

Co-seismic landslide detection after M 7.4 earthquake on June 23, 2020, in Oaxaca, Mexico, based on rapid mapping method using high and medium resolution synthetic aperture radar (SAR) images

Landslides ◽  
2021 ◽  
Author(s):  
Norma Davila Hernandez ◽  
Alexander Ariza Pastrana ◽  
Lizeth Caballero Garcia ◽  
Juan Carlos Villagran de Leon ◽  
Antulio Zaragoza Alvarez ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Mapping Method ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Rapid Mapping ◽  
Medium Resolution ◽  
Seismic Landslide ◽  
Aperture Radar

scholarly journals RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2919 ◽  
Author(s):  
Agnieszka Chojka ◽  
Piotr Artiemjew ◽  
Jacek Rapiński
Keyword(s):  
Image Processing ◽  
Synthetic Aperture Radar ◽  
Nearest Neighbor ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Sar Images ◽  
Permanent Scatterers ◽  
Level 1 ◽  
Processing Techniques ◽  
Aperture Radar

Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used.

scholarly journals Comparison of C-Band Quad-Polarization Synthetic Aperture Radar Wind Retrieval Models

2018 ◽  
Vol 10 (9) ◽  
pp. 1448 ◽  
Author(s):  
He Fang ◽  
Tao Xie ◽  
William Perrie ◽  
Guosheng Zhang ◽  
Jingsong Yang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Synthetic Aperture Radar ◽  
Surface Wind ◽  
Synthetic Aperture ◽  
Northwest Pacific ◽  
Sar Images ◽  
Retrieval Models ◽  
Wind Retrieval ◽  
Wind Speeds ◽  
Aperture Radar

This work discusses the accuracy of C-2PO (C-band cross-polarized ocean backscatter) and CMOD4 (C-band model) geophysical model functions (GMF) for sea surface wind speed retrieval from satellite-born Synthetic Aperture Radar (SAR) images over in the Northwest Pacific off the coast of China. In situ observations are used for comparison of the retrieved wind speed using two established wind retrieval models: C-2PO model and CMOD4 GMF. Using 439 samples from 92 RADARSAT-2 fine quad-polarization SAR images and corresponding reference winds, we created two subset wind speed databases: the training and testing subsets. From the training data subset, we retrieve ocean surface wind speeds (OSWSs) from different models at each polarization and compare with reference wind speeds. The RMSEs of SAR-retrieved wind speeds are: 2.5 m/s: 2.11 m/s (VH-polarized), 2.13 m/s (HV-polarized), 1.86 m/s (VV-polarized) and 2.26 m/s (HH-polarized) and the correlation coefficients are 0.86 (VH-polarized), 0.85(HV-polarized), 0.87(VV-polarized) and 0.83 (HH-polarized), which are statistically significant at the 99.9% significance level. Moreover, we found that OSWSs retrieved using C-2PO model at VH-polarized are most suitable for moderate-to-high winds while CMOD4 GMF at VV-polarized tend to be best for low-to-moderate winds. A hybrid wind retrieval model is put forward composed of the two models, C-2PO and CMOD4 and sets of SAR test data are used in order to establish an appropriate wind speed threshold, to differentiate the wind speed range appropriate for one model from that of the other. The results show that the OSWSs retrieved using our hybrid method has RMSE of 1.66 m/s and the correlation coefficient are 0.9, thereby significantly outperforming both the C-2PO and CMOD4 models.

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