scholarly journals Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data

2018 ◽  
Vol 8 (9) ◽  
pp. 1513
Author(s):  
Wenguang Wang ◽  
Xin Ren ◽  
Yan Zhang ◽  
Meng Li
Keyword(s):  
Classification Performance ◽  
Synthetic Aperture ◽  
Experimental Result ◽  
Dual Frequency ◽  
High Similarity ◽  
Polarimetric Synthetic Aperture Radar ◽  
Class Separability ◽  
Sparse Autoencoder ◽  
Sar Data ◽  
Sar Imagery

Lithology classification is a crucial step in the prospecting process, and polarimetric synthetic aperture radar (Pol-SAR) imagery has been extensively used for it. However, despite significant improvements in both information content of Pol-SAR imagery and advanced classification approaches, lithology classification using Pol-SAR data may not provide satisfactory classification accuracy due to high similarity of certain classes. In this paper, a novel Pol-SAR lithology classification method based on a stacked sparse autoencoder (SSAE) is proposed. By using superpixel segmentation, new features can be extracted from dual-frequency Pol-SAR data, which can increase the class separability of the input data. Then, these features and the coherency matrices are incorporated into SSAE to classify the lithology. The classification performance is evaluated on an SIR-C dataset acquired over Xinjiang, China. The experimental result shows that this method is effective for lithology classification and can improve the overall accuracy up to 98.90%.

scholarly journals Mapping potential signs of gas emissions in ice of Lake Neyto, Yamal, Russia, using synthetic aperture radar and multispectral remote sensing data

2021 ◽  
Vol 15 (4) ◽  
pp. 1907-1929
Author(s):  
Georg Pointner ◽  
Annett Bartsch ◽  
Yury A. Dvornikov ◽  
Alexei V. Kouraev
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Lake Ice ◽  
Gas Emissions ◽  
L Band ◽  
Optical Imagery ◽  
Sar Data ◽  
Sar Imagery ◽  
Aperture Radar

Abstract. Regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of lake ice of Lake Neyto in northwestern Siberia have been suggested to be caused by emissions of gas (methane from hydrocarbon reservoirs) through the lake’s sediments. However, to assess this connection, only analyses of data from boreholes in the vicinity of Lake Neyto and visual comparisons to medium-resolution optical imagery have been provided due to a lack of in situ observations of the lake ice itself. These observations are impeded due to accessibility and safety issues. Geospatial analyses and innovative combinations of satellite data sources are therefore proposed to advance our understanding of this phenomenon. In this study, we assess the nature of the backscatter anomalies in Sentinel-1 C-band SAR images in combination with very high resolution (VHR) WorldView-2 optical imagery. We present methods to automatically map backscatter anomaly regions from the C-band SAR data (40 m pixel spacing) and holes in lake ice from the VHR data (0.5 m pixel spacing) and examine their spatial relationships. The reliability of the SAR method is evaluated through comparison between different acquisition modes. The results show that the majority of mapped holes (71 %) in the VHR data are clearly related to anomalies in SAR imagery acquired a few days earlier, and similarities to SAR imagery acquired more than a month before are evident, supporting the hypothesis that anomalies may be related to gas emissions. Further, a significant expansion of backscatter anomaly regions in spring is documented and quantified in all analysed years 2015 to 2019. Our study suggests that the backscatter anomalies might be caused by lake ice subsidence and consequent flooding through the holes over the ice top leading to wetting and/or slushing of the snow around the holes, which might also explain outcomes of polarimetric analyses of auxiliary L-band Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) data. C-band SAR data are considered to be valuable for the identification of lakes showing similar phenomena across larger areas in the Arctic in future studies.

scholarly journals PolSAR Image Classification Using a Superpixel-Based Composite Kernel and Elastic Net

2021 ◽  
Vol 13 (3) ◽  
pp. 380
Author(s):  
Yice Cao ◽  
Yan Wu ◽  
Ming Li ◽  
Wenkai Liang ◽  
Peng Zhang
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Classification Performance ◽  
Elastic Net ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Training Samples ◽  
Composite Kernel ◽  
Limited Training Samples ◽  
State Of Art

The presence of speckles and the absence of discriminative features make it difficult for the pixel-level polarimetric synthetic aperture radar (PolSAR) image classification to achieve more accurate and coherent interpretation results, especially in the case of limited available training samples. To this end, this paper presents a composite kernel-based elastic net classifier (CK-ENC) for better PolSAR image classification. First, based on superpixel segmentation of different scales, three types of features are extracted to consider more discriminative information, thereby effectively suppressing the interference of speckles and achieving better target contour preservation. Then, a composite kernel (CK) is constructed to map these features and effectively implement feature fusion under the kernel framework. The CK exploits the correlation and diversity between different features to improve the representation and discrimination capabilities of features. Finally, an ENC integrated with CK (CK-ENC) is proposed to achieve better PolSAR image classification performance with limited training samples. Experimental results on airborne and spaceborne PolSAR datasets demonstrate that the proposed CK-ENC can achieve better visual coherence and yield higher classification accuracies than other state-of-art methods, especially in the case of limited training samples.

scholarly journals HETEROGENEITY MEASUREMENT BASED ON DISTANCE MEASURE FOR POLARIMETRIC SAR DATA

2018 ◽  
Vol IV-3 ◽  
pp. 233-237
Author(s):  
Xiaoli Xing ◽  
Qihao Chen ◽  
Xiuguo Liu
Keyword(s):  
Standard Deviation ◽  
Synthetic Aperture Radar ◽  
Covariance Matrix ◽  
Distance Measure ◽  
Synthetic Aperture ◽  
Polarimetric Sar ◽  
Polarimetric Synthetic Aperture Radar ◽  
Local Window ◽  
The Mean ◽  
Sar Data

To effectively test the scene heterogeneity for polarimetric synthetic aperture radar (PolSAR) data, in this paper, the distance measure is introduced by utilizing the similarity between the sample and pixels. Moreover, given the influence of the distribution and modeling texture, the K distance measure is deduced according to the Wishart distance measure. Specifically, the average of the pixels in the local window replaces the class center coherency or covariance matrix. The Wishart and K distance measure are calculated between the average matrix and the pixels. Then, the ratio of the standard deviation to the mean is established for the Wishart and K distance measure, and the two features are defined and applied to reflect the complexity of the scene. The proposed heterogeneity measure is proceeded by integrating the two features using the Pauli basis. The experiments conducted on the single–look and multilook PolSAR data demonstrate the effectiveness of the proposed method for the detection of the scene heterogeneity.

scholarly journals Performance Evaluation of Azimuth Offset Method for Mitigating the Ionospheric Effect on SAR Interferometry

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Wu Zhu ◽  
Wen-Ting Zhang ◽  
Yu-Fang He ◽  
Wei Qu
Keyword(s):  
Estimation Error ◽  
Random Noise ◽  
Scaling Factor ◽  
Phase Delay ◽  
Sar Interferometry ◽  
Synthetic Aperture ◽  
Experimental Result ◽  
Ionospheric Effect ◽  
Ionospheric Correction ◽  
Sar Data

Synthetic aperture radar (SAR) signals interact with the ionosphere layer when they propagate through the atmosphere, leading to the phase delay error for SAR interferometry (InSAR). To mitigate this error for SAR interferometry, azimuth offset method is proposed. However, the performance of it has not been fully investigated. In this situation, this study makes a comprehensive performance analysis of azimuth offset method through processing the simulated and real SAR data. The experimental result indicates that this method can effectively mitigate the ionospheric phase delay error, where the standard deviation of phase difference after correction (2.6 rad.) decreased by almost 2 times, compared to those before correction (5.3 rad.) for the real SAR data. However, it is also found that the method is affected by the random noise, which may induce the improper estimation of integral constants and consequently affect the ionospheric correction. Moreover, the severe deformation signals in the interferogram may lead to the estimation error of integral constants and scaling factor. Therefore, it should mask out the deformation signals when using the azimuth offsets method to correct the ionospheric error. This study may provide useful information when using azimuth offset method to mitigate the ionospheric phase delay error on InSAR.

scholarly journals Review of the Comprehensive SAR Approach to Identify Scattering Mechanisms of Radar Backscatter from Vegetated Terrain

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1098 ◽  
Author(s):  
Motofumi Arii ◽  
Hiroyoshi Yamada ◽  
Shoichiro Kojima ◽  
Masato Ohki
Keyword(s):  
Remote Sensing ◽  
Numerical Model ◽  
Model Simulation ◽  
Synthetic Aperture ◽  
Decomposition Techniques ◽  
Scattering Mechanism ◽  
Radar Backscatter ◽  
Polarimetric Synthetic Aperture Radar ◽  
Scattering Mechanisms ◽  
Sar Data

In a field of polarimetric synthetic aperture radar (SAR) remote sensing, various kinds of polarimetric decomposition techniques have been proposed. However, poor validations prevent them from operational applications. A true composition ratio of scattering mechanisms within a radar backscatter plays a key role. To overcome the issue, a novel comprehensive SAR approach to accurately identify a contribution of each scattering mechanism has been introduced. This is based on multiparametric SAR observation combined with a numerical model simulation. In this article, a comprehensive SAR approach is concisely reviewed to accelerate the research in this field. First, popular model-based polarimetric decompositions are introduced and their limitations are shown. Then, a behavior of scattering mechanisms is analyzed by the discrete scatterer model with some results using real multiparametric SAR data. A comprehensive SAR approach must be essential to realize an operational use of polarimetric SAR data.

scholarly journals Detecting Emergence, Growth, and Senescence of Wetland Vegetation with Polarimetric Synthetic Aperture Radar (SAR) Data

Water ◽  
2014 ◽  
Vol 6 (3) ◽  
pp. 694-722 ◽  
Author(s):  
Alisa Gallant ◽  
Shannon Kaya ◽  
Lori White ◽  
Brian Brisco ◽  
Mark Roth ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Wetland Vegetation ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Sar Data ◽  
Aperture Radar
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