Time-Frequency Analysis in High-Resolution SAR Imagery

Spigai, Marc; Tison, C.; Souyris, J.-C.

doi:10.1109/tgrs.2011.2107914

Cited by 36 publications

(24 citation statements)

References 18 publications

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“…The sub-aperture or time-frequency decomposition, which uses a two-dimensional Fourier transform, or Gabor transform to decompose a full resolution image into several sub-aperture images, has been widely used on SAR image application [36][37][38][39][40]. A 2D signal s (l) can be transformed into several different components located around particular spectral coordinates l p ; ω p using a convolution with an analyzing function g () as follows: where l p and ω p denote spatial and frequency locations of the pth component, respectively.…”

Section: Sub-aperture Decompositionmentioning

confidence: 99%

Built-up Area Extraction from PolSAR Imagery with Model-Based Decomposition and Polarimetric Coherence

Xiang

Tang

et al. 2016

Remote Sensing

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Built-up area extraction from polarimetric SAR (PolSAR) imagery has a close relationship with urban planning, disaster management, etc. Since the buildings have complex geometries and may be misclassified as forests due to the significant cross-polarized scattering, built-up area extraction from PolSAR data is still a challenging problem. This paper proposes a new urban extraction method for PolSAR data. First, a multiple-component model-based decomposition method, which was previously proposed by us, is applied to detect the urban areas using the scattering powers. Second, with the sub-aperture decomposition, a new average polarimetric coherence coefficient ratio is proposed to discriminate the urban and natural areas. Finally, these two preliminary detection results are fused on the decision level to improve the overall detection accuracy. We validate our method using one dataset acquired with the Phased Array type L-band Synthetic Aperture Radar (PALSAR) system. Experimental results demonstrate that the decomposed scattering powers and the proposed polarimetric coherence coefficient ratio are both capable of distinguishing urban areas from natural areas with accuracy about 83.1% and 80.1%, respectively. The overall detection accuracy can further increase to 86.9% with the fusion of two detection results.

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Section: Sub-aperture Decompositionmentioning

confidence: 99%

Built-up Area Extraction from PolSAR Imagery with Model-Based Decomposition and Polarimetric Coherence

Xiang

Tang

et al. 2016

Remote Sensing

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“…Generally speaking, a trihedral corner reflector has a flat backscattering envelope during synthetic aperture time while a dihedral corner reflector has a variant one. Thus, in the simulation test, we simplify the types of targets in [8] to two: azimuth invariant target (e.g. Fig.…”

Section: Simulation Testmentioning

confidence: 99%

“…It may degrade the effectiveness of feature extraction algorithm starting with spectrum. To validate this assumption, a FFT-based timefrequency approach [8] and a complex spectral estimation algorithm called APES [16] are used to extract the spectral envelope of T3 respectively. As shown in Fig.…”

Section: Simulation Testmentioning

confidence: 99%

“…It leads to a stable backscattering during the acquisition. Therefore, the backscattering envelope can be the feature of major concern to characterize target properties, especially when a wide-angle SAR is operated [8]. Moreover, the geographical location and relative velocity are equivalently important, since the location denotes the cross-track and along-track positions while the relative velocity reflects the along-track speed.…”

Section: Introductionmentioning

confidence: 99%

“…They use Wigner-Ville decomposition [9], wavelet transforms [10], and Fourier transform [8,11] to realize feature extraction. Starting with spectrum of SAR imagery, these methods are constrained with knowing explicit knowledge of platform velocity and forming a SAR image first.…”

Section: Introductionmentioning

confidence: 99%

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Feature extraction of SAR scattering centers using M-RANSAC and STFRFT-based algorithm

Song

Gao

Zhu

et al. 2016

EURASIP J. Adv. Signal Process.

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This paper introduces a modified random sample consensus (M-RANSAC) and short-time fractional Fourier transform (STFRFT)-based algorithm for feature extraction of synthetic aperture radar (SAR) scattering centers. In this algorithm, the range migration curve (RMC) of a scattering center is formulated as a parametric model. By estimating these parameters, the backscattering envelope of scattering center, corresponding to the backscattering variation in synthetic aperture time, is extracted directly from a time-domain range-compressed signal. The estimated parameters can also reconstruct the geographical location and along-track velocity of scattering centers. Thus, even without knowing explicit knowledge of platform velocity and forming a SAR image, this algorithm is capable of realizing feature extraction. To estimate parameters scatter by scatter, M-RANSAC approach is proposed as an implementary method with iterative procedure. In the iterations, fitting precision indicator (FPI) works cooperatively with construction fitness coefficient (CFC) to determine the optimal parameters of different scattering centers. Adapting this method to more general cases, STFRFT is introduced to separate the overlapped trajectories of RMCs of scattering centers. The root mean squared errors (RMSEs) of parameter estimation are close to their Cramér-Rao lower bounds (CRLB). The effectiveness of feature extraction based on the devised algorithm is validated by both simulated and real SAR data.

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