A Wavelet Decomposition and Polynomial Fitting-Based Method for the Estimation of Time-Varying Residual Motion Error in Airborne Interferometric SAR

Fu, Hai Qiang; Zhu, Jianjun; Wang, Chang Cheng; Wang, Hui Qiang; Zhao, Rong

doi:10.1109/tgrs.2017.2727076

Cited by 22 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays, data fusion and data integration are two common combination modes of multi-source data. Particularly, compared with the data integration methods, there are more data fusion methods, as PCA fusion method [ 33 , 34 ], Brovey fusion method [ 35 , 36 ], Gram-Schmidt transform fusion method [ 37 , 38 ], wavelet transform method [ 39 , 40 , 41 , 42 ]. However, the dimension of feature sets extracted in data fusion is generally three, corresponding to the RGB channels for visual representation.…”

Section: Introductionmentioning

confidence: 99%

A Crop Classification Method Integrating GF-3 PolSAR and Sentinel-2A Optical Data in the Dongting Lake Basin

Gao

Wang

et al. 2018

Sensors

View full text Add to dashboard Cite

With the increasing of satellite sensors, more available multi-source data can be used for large-scale high-precision crop classification. Both polarimetric synthetic aperture radar (PolSAR) and multi-spectral optical data have been widely used for classification. However, it is difficult to combine the covariance matrix of PolSAR data with the spectral bands of optical data. Using Hoekman’s method, this study solves the above problems by transforming the covariance matrix to an intensity vector that includes multiple intensity values on different polarization basis. In order to reduce the features redundancy, the principal component analysis (PCA) algorithm is adopted to select some useful polarimetric and optical features. In this study, the PolSAR data acquired by satellite Gaofen-3 (GF-3) on 19 July 2017 and the optical data acquired by Sentinel-2A on 17 July 2017 over the Dongting lake basin are selected for the validation experiment. The results show that the full feature integration method proposed in this study achieves an overall classification accuracy of 85.27%, higher than that of the single dataset method or some other feature integration modes.

show abstract

Section: Introductionmentioning

confidence: 99%

A Crop Classification Method Integrating GF-3 PolSAR and Sentinel-2A Optical Data in the Dongting Lake Basin

Gao

Wang

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…On comparing with the DI methods, DF has more number of methods. They are: PCA fusion, Gram-Schmidt transforms fusion (Tao et al, 2015), wavelet transform (Fu et al, 2018) and Brovey fusion. Nevertheless, the dimension of feature sets that are extracted in DF in proportion to the RGB channels for visual representation is usually '3ʹ.…”

Section: Introductionmentioning

confidence: 99%

Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques

Natteshan

Kumar

2020

European Journal of Remote Sensing

View full text Add to dashboard Cite

Crop classification is a significant requirement to estimate crop area, structure, and spatial distribution, as well as provide important input parameters for crop yield models. Different techniques were considered in this system and providing betterment in automation. But none of them gave promising results. So here, a Convolutional Deep Neural Network (CDNN) is proposed to identify the crop areas with the help of Synthetic-Aperture Radar (SAR) satellite images as well as the cultivation status of the crop. First, in training phase, the segmented image of the crop is preprocessed using HLS, then feature is extracted using BRIEF, then, they are classified using CDNN. Then after in testing phase, the input SAR image from the database is further processed using MRG algorithm and classified centered on the training results. After classification, the cultivation status of each classified crop can be identified by taking the Euclidean distance (ED) betwixt the standard parameters and resultant parameters of a specific crop. After computing ED, the ED is contrasted with the threshold value and the cultivation status of a particular crop can be identified. The results are analyzed to ascertain the performance shown by the proposed technique with other existent techniques.

show abstract

“…In the case of inaccuracy for available navigation information, there are many approaches to estimate RME, such as autofocusing methods [12,13], wavelet decomposition and polynomial fitting methods [14], and a widely applied multisquint (MSQ) technique as well as its improved methods [15,16,17,18]. Among those approaches, the MSQ algorithm is most wildly used.…”

Section: Introductionmentioning

confidence: 99%

Residual Motion Error Correction with Backprojection Multisquint Algorithm for Airborne Synthetic Aperture Radar Interferometry

Xie

Zhang

et al. 2019

Sensors

View full text Add to dashboard Cite

For airborne interferometric synthetic aperture radar (InSAR) data processing, it is essential to achieve precise motion compensation to obtain high-quality digital elevation models (DEMs). In this paper, a novel InSAR motion compensation method is developed, which combines the backprojection (BP) focusing and the multisquint (MSQ) technique. The algorithm is two-fold. For SAR image focusing, BP algorithm is applied to fully use the navigation information. Additionally, an explicit mathematical expression of residual motion error (RME) in the BP image is derived, which paves a way to integrating the MSQ algorithm in the azimuth spatial wavenumber domain for a refined RME correction. It is revealed that the proposed backprojection multisquint (BP-MSQ) algorithm exploits the motion error correction advantages of BP and MSQ simultaneously, which leads to significant improvements of InSAR image quality. Simulation and real data experiments are employed to illustrate the effectiveness of the proposed algorithm.

show abstract

A Wavelet Decomposition and Polynomial Fitting-Based Method for the Estimation of Time-Varying Residual Motion Error in Airborne Interferometric SAR

Cited by 22 publications

References 28 publications

A Crop Classification Method Integrating GF-3 PolSAR and Sentinel-2A Optical Data in the Dongting Lake Basin

A Crop Classification Method Integrating GF-3 PolSAR and Sentinel-2A Optical Data in the Dongting Lake Basin

Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques

Residual Motion Error Correction with Backprojection Multisquint Algorithm for Airborne Synthetic Aperture Radar Interferometry

Contact Info

Product

Resources

About