Qiang Yin

Qiang Yin
Institute of Electronics, Chinese Academy of Sciences · National Key Lab. of Microwave Imaging Technology

MS, PhD candidate

About

34
Publications
1,361
Reads
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176
Citations
Citations since 2016
23 Research Items
168 Citations
2016201720182019202020212022020406080
2016201720182019202020212022020406080
2016201720182019202020212022020406080
2016201720182019202020212022020406080
Additional affiliations
February 2014 - January 2015
European Space Agency, ESRIN
Position
  • Research Associate
July 2008 - January 2014
Institute of Electronics, Chinese Academy of Sciences
Position
  • Research Assistant
Education
September 2013 - July 2016
Institute of Electronics, Chinese Academy of Sciences
Field of study
  • Soil moisture estimation with SAR polarimetry or interferometry
September 2005 - June 2008
Institute of Electronics, Chinese Academy of Sciences
Field of study
  • Polarimetric SAR processing and its applications

Publications

Publications (34)
Article
Full-text available
In this paper, we propose a fast Line Segment Detection algorithm for Polarimetric synthetic aperture radar (PolSAR) data (PLSD). We introduce the Constant False Alarm Rate (CFAR) edge detector to obtain the gradient map of the PolSAR image, which tests the equality of the covariance matrix using the test statistic in the complex Wishart distributi...
Article
Time-series polarimetric synthetic aperture radar (PolSAR) has been proven to be an effective technique for crop classification and agricultural activity monitoring. However, the characterization and utilization of time-series PolSAR data by existing methods are still inadequate. They are unable to extract and utilize time-varying features, which c...
Article
The high-resolution soil moisture inversion under vegetation from remote-sensing data is a challenging task. The key issue is to separate or eliminate the influence of vegetation. Therefore, the difficult problem is to select appropriate vegetation descriptors and accurately deal with them. With the great success of convolutional neural network (CN...
Preprint
Full-text available
Airports have an important role in both military and civilian domains. The synthetic aperture radar (SAR) based airport detection has received increasing attention in recent years. However, due to the high cost of SAR imaging and annotation process, there is no publicly available SAR dataset for airport detection. As a result, deep learning methods...
Article
In existing superpixel-wise segmentation algorithms, superpixel generation most often is an isolated preprocessing step. The segmentation performance is determined to a certain extent by the accuracy of superpixels. However, it is still a challenge to develop a stable superpixel generation method. In this article, we attempt to incorporate the supe...
Article
As flood occurs unpredictably, there is not enough time to label the data in practice. The use of clustering inside flood detection deep networks can reduce their demand for labeled data. However, existing clustering algorithms aim at assigning a unique cluster for each pixel. This leads to the fact that clustering process is non-differentiable to...
Article
Full-text available
Compact polarimetry (CP) has attracted much attention in recent years due to its hybrid dual polarization imaging mode. CP synthetic aperture radar (SAR) has a larger swath and can provide more polarimetric information compared with the traditional dual polarization imaging mode (HH/HV or VH/VV). Pseudo quad-polarimetric (quad-pol) data reconstruct...
Article
Full-text available
The polarimetric synthetic aperture radar (PolSAR) can be used to obtain soil moisture by inverting scattering models at high resolution. The convolutional neural network (CNN) has been recently introduced to estimate soil roughness for PolSAR data, which need to be driven by a large amount of data. In this paper, a dual-channel CNN based on polari...
Article
Full-text available
The compact polarimetric (CP) synthetic aperture radar (SAR) can alleviate the drawbacks of the fully polarimetric (FP) SAR and provide more target information as compared to the conventional single polarimetric and dual polarimetric mode. However, the present decomposition methods for CP SAR have the problem of overestimation of volume scattering...
Article
Since the number of superpixels is lower than that of pixels, superpixels can substantially speed up subsequent processing steps and have been widely used in synthetic aperture radar (SAR) image segmentation. However, in most of the existing superpixel-wise segmentation algorithms, superpixel prediction is an isolated preprocessing step and is inde...
Article
Full-text available
Polarimetric synthetic aperture radar (PolSAR) image classification is one of the basic methods of PolSAR image interpretation. Deep learning algorithms, especially convolutional neural networks (CNNs), have been widely used in PolSAR image classification due to their powerful feature learning capabilities. However, a single neuron in the CNN canno...
Article
Convolutional neural networks (CNNs) have demonstrated impressive ability to achieve promising results in PolSAR image classification. However, the traditional CNN performs convolution on local square regions with fixed sizes. The selection of these local square regions (patches) cannot fully take advantage of the boundary information of land cover...
Article
Polarimetric synthetic aperture radar (PolSAR) image classification is an important part of SAR data interpretation and provides more intuitive and detailed SAR polarization information. To bridge the PolSAR data and applications, it is necessary to design a comprehensive PolSAR classification framework to achieve satisfactory results. The deep neu...
Article
Full-text available
Decision tree method has been applied to POLSAR image classification, due to its capability to interpret the scattering characteristics as well as good classification accuracy. Compared with popular machine learning classifiers, decision tree approach can explain the scattering process of certain type of targets by use of the polarimetric features...
Article
Polarimetric features of PolSAR images include inherent scattering mechanisms of terrain types, which are important for classification and other Earth observation applications. By using target decomposition methods, many polarimetric scattering components can be obtained. Besides, the elements of a coherency/covariance matrix, as well as polarimetr...
Article
Circular synthetic aperture radar (CSAR) can provide distinctive multiaspect anisotropic scattering signatures. However, it is impossible to retain the anisotropic signatures in an SAR image that combines all the subapertures coherently or incoherently. In this letter, we propose a polarimetric CSAR anisotropic scattering detection framework to cha...
Article
In conventional synthetic aperture radar (SAR), sensors with a fixed look angle are assumed, and the scattering properties are considered as invariant in the azimuth. In some new SAR modes such as wide-angle SAR and circular SAR (CSAR), the azimuthal angle of view is much larger. Anisotropic targets which have different physical shapes from differe...
Article
We propose a model for soil moisture change detection using phase information of synthetic aperture radar data. It is expected to be applied for drought monitoring over grasslands in north China. This model is developed from the coherent scattering model, which was originally studied for random oriented volume over ground scattering. Compared to th...
Article
In this letter, an accurate topographical phase is applied to the model-based (odd-bounce, double-bounce, and volume scattering) decomposition of synthetic aperture radar (SAR) interferometry data. The decomposition procedure considered here is a determined nonlinear equation system that can be solved numerically. The accurate topographical phase i...
Conference Paper
This paper presents estimation technique of ground topography in forest areas with Polarimetric SAR Interferometry (PolInSAR) data, and analyses the impact of different directional slops on the topographic phase. On the basis of the Random Volume over Ground (RVoG) scattering model, the complex coherence of the actual experiment PolInSAR data for d...
Conference Paper
Full-text available
In this paper we propose a method for soil moisture inversion using phase information of SAR data, which is ex-pected to be applied in the drought monitoring of soil water content using multi-temporal SAR data. It is devel-oped from the coherent scattering model, which is first studied for random oriented volume over ground scatter-ing in vegetated...
Article
First, a polarimetric three-scale scattering model, including a large-scale-slope-induced polarisation orientation angle shift, a moderate-scale orientation angle disturbance and a small-scale Bragg resonance, to describe the polarimetric coherence matrix for a sloped rough surface is introduced. Then, a look-up table is created to demonstrate the...
Article
In this work, we borrow a calibration algorithm originally developed for polarimetric scattering matrix measurement for our wide-band polarimetric ground based SAR(GB-SAR) system. The principle and experimental results of this calibration algorithm are presented. The calibration targets include a only a dihedral rotated at different angles.
Article
The paper focuses on two basic concepts, the high order scattering, proposed due to the high order solution of analytic scattering formulations, and the multiple scattering, defined according to the physical mechanism, in frequently-mentioned models that describe scattering from a randomly rough surface. These two concepts are related in this paper...
Article
This paper analyzes the two scattering models and inversion techniques for bare soil surface, which were proposed by Oh and Dubois, respectively. A method to validate the parameter inversion results of both two models was developed under the condition that no in-situ measurements were collected. Since Integral Equation Method (IEM) provides the rel...
Conference Paper
Full-text available
In this paper we improve the range analysis method in soil inversion models, using entropy/alpha space in the Cloude- Pottier decomposition theory. The ranges in data where inversion models can be applied are called the valid ranges of the inversion models. The improved valid ranges are considered more accurate through the Integral Equation Method...
Conference Paper
A new method is developed to validate the parameter inversion results of the empirical models proposed by Oh et al. for bare soil surface. Oh et al. developed a series of empirical models for retrieving bare soil moisture and root mean square (rms) height, while Fung et al. proposed integral equation method (IEM) to provide the relationship between...

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Project (1)
Project
Ocean information retrieval from SAR images