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Global navigation satellite system reflectometry (GNSS-R) is a new remote sensing technique, which can be used to measure a wide range of geophysical parameters. GNSS-R makes use of the simultaneous reception of the direct transmission and the coherent surface reflections of the GNSS signal with either a single antenna or multiple separate antennas. This paper presents a new snow depth estimation method using a combination of pseudorange and carrier phase of GNSS dual-frequency signals. The proposed method is geometry-free and is not affected by ionospheric delays. The formulas of the amplitude attenuation factor of reflected signals, multipath-induced carrier-phase error, and pesudorange error for ground-based GNSS receivers are used to describe the combined signals. Using theoretical formulas instead of in situ measurement data, analytical linear models are established in advance to describe the relationship between snow depth and main frequency of combined signal time series. When the main frequency of the combined measurements is obtained by spectrum analysis, the model is used to determine snow depth. Two experimental data sets recorded in two different environments were used to test the proposed method. The results demonstrate that there exists good agreement between the proposed method and the ground-truth measurements. [ABSTRACT FROM AUTHOR]

In this paper, the motion parameters’ estimation problem in synthetic aperture radar (SAR) imaging is extended to the multidimensional case, and the azimuth signal of a moving target with velocities and accelerations in three directions is modeled as a polynomial-phase signal (PPS). To estimate the multidimensional motion parameters of the moving target from the azimuth PPS, a multi-SAR system is required. On this basis, a new method based on the cubic phase function (CPF) is proposed to estimate the Doppler phase coefficients (DPCs) of the PPS. And, all the first- and second-order motion terms can be derived from the DPC. Experimental results have shown that, using the proposed scheme, the motion parameters can be accurately estimated in high signal-to-noise ratio scenarios. [ABSTRACT FROM AUTHOR]

The dreamlet (drumbeat-beamlet) transform can provide us an efficient method to represent physical wavefield, because the dreamlet basis satisfies automatically the wave equation, which is a distinctive feature different from mathematical basis, such as Fourier and curvelet. It can obtain an estimation of true signal from the observed noisy data by abandoning those insignificant components in the dreamlet domain. However, we have found that a more accurate estimation can be achieved by a damped version of the dreamlet representation. We have theoretically derived the damped dreamlet representation and given its geometric interpretation and analysis. Two applications of the proposed method have been explored in this paper: seismic random noise suppression and seismic data interpolation. Various examples demonstrate that the damped dreamlet representation-based technique has a superior performance compared with the mathematical-basis-based representation and rank-reduction-based techniques. [ABSTRACT FROM AUTHOR]

Anomaly detection has become an important topic in hyperspectral imagery (HSI) analysis over the last 20 years. HSIs usually possess complexly cluttered spectral signals due to the complicated conditions of the land-cover distribution. This in turn makes it difficult to obtain an accurate background estimation to distinguish the anomaly targets. The sparse learning technique provides a way to obtain an implicit background representation with the learned dictionary and corresponding sparse codes. In this paper, we explore the background/anomaly information content for each atom of the learned dictionary, from an analysis based on the frequency of the dictionary atoms for HSI reconstruction. From this perspective, we propose a novel sparsity score estimation framework for hyperspectral anomaly detection. First, an overcomplete dictionary and the corresponding sparse code matrix are obtained from the HSI. The frequency of each dictionary atom for reconstruction, which is also called the atom usage probability, is then estimated from the sparse code matrix. Finally, the estimated frequencies are transformed to the sparsity score for each pixel, which can be seen as the degree of “anomalousness.” In the proposed detection framework, two strategies are proposed to enhance the diversity between the background and anomaly information in the learned dictionary: 1) dictionary-based background feature transformation and 2) dictionary iterative reweighting. A series of real-world HSI data sets is utilized to evaluate the performance of the proposed framework. The experimental results show that the proposed framework achieves a superior performance compared to some of the state-of-the-art anomaly detection methods. [ABSTRACT FROM PUBLISHER]

Classical target detection schemes are usually obtained by deriving the likelihood ratio under Gaussian hypothesis and replacing the unknown background parameters by their estimates. In most applications, interference signals are assumed to be Gaussian with zero mean [or with a known mean vector (MV)] and with an unknown covariance matrix (CM). When the MV is unknown, it has to be jointly estimated with the CM. In this paper, adaptive versions of the classical matched filter (MF) and the normalized MF, as well as two versions of the Kelly detector are first derived and then analyzed for the case where the MV of the background is unknown. More precisely, theoretical closed-form expressions for false alarm (FA) regulation are derived and the constant FA rate property is pursued to allow the detector to be independent of nuisance parameters. Finally, the theoretical contributions are validated through simulations. [ABSTRACT FROM AUTHOR]

Background feature extraction is an important step in hyperspectral anomaly detection. However, the lack of prior information about anomaly targets and the complex spectral mixture result in a challenge for robust background feature extraction. Can we solve the anomaly detection problem other than with background feature extraction? Relative to anomalies, the background spectral signal is usually stable and slowly varying. In view of this point, slowly varying background analysis is introduced into anomaly detection in this paper. The desired background signals are obtained through a generalized eigenvalue decomposition problem based on the original data and the differential image. The extracted signals are then combined with a Mahalanobis distance metric to construct the detection estimation. Different data processing procedures and signal extraction patterns are respectively formulated to construct different versions of the slowly varying background-signal-based detector. The performances of the proposed methods were validated on both synthetic and real hyperspectral data. The experimental results reveal that the proposed methods outperform the state-of-the-art anomaly detectors, with superior receiver operating characteristic (ROC) curves, area-under-ROC values, and background–target separation. The sensitivity of the relevant parameters was also analyzed in an experimental analysis. [ABSTRACT FROM PUBLISHER]

Ice sounding radars are able to measure ice sheets by profiling their glaciological features from the surface to the bedrock. The current airborne and, in particular, future space-based systems are suffering from off-nadir surface clutter, which can mask the depth signal of interest. The most recent surface clutter suppression techniques are based on multi-phase-center systems combined with sophisticated coherent postprocessing. The performance of the techniques can be improved by accurate direction-of-arrival (DOA) estimates of the surface clutter. This paper deals with data-driven DOA estimation for surface clutter signals, which includes a formulation of the mathematical foundation of spatial aliasing. DOA estimation is applied to data acquired with the P-band POLarimetric Airborne Radar Ice Sounder at the Jutulstraumen Glacier, Antarctica. The effects of spatial aliasing related to a large phase center spacing are analyzed, and an unwrapping procedure is presented and applied to the data. Finally, DOA estimation of full-scene data is analyzed and used to show an along-track and incidence (off-nadir) angle dependent variation of the effective scattering center of the surface return, which is caused by a varying penetration depth. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel covariation approach for seismic wavelet estimation under the assumption that a real seismic signal follows non-Gaussian α-stable distributions. Since the non-Gaussian α-stable signals do not have finite second or higher order moments, the traditional methods of Gaussian distribution may not get a suitable solution. Based on the principle of fractional lower order statistics, the covariation approach deconvolution objective function matrix was given, and the details of wavelet estimation with the covariation approach were presented. Furthermore, computer simulation experiments on theoretical synthetic data and real seismic data were conducted. In the experiments, the effect of moments was considered. Among the estimated wavelets with different moments, the best wavelet should be the one with moment less than characteristic but close to characteristic. To verify the correctness and effectiveness of the proposed method, the extracted real wavelet was applied in real seismic acoustic impedance inversion. The result from the inversion of the 2-D real data set is consistent with the well log interpretation very well. [ABSTRACT FROM PUBLISHER]

Seismic noise attenuation to improve signal-to-noise ratio plays an important role in seismic data processing. In recent years, time-frequency peak filtering (TFPF) has been introduced and applied to seismic random noise attenuation successfully. However, in the conventional TFPF, the window length (WL) is fixed and used for all frequency components. As a consequence, serious loss of the effective components is unavoidable due to the inappropriate WL. The recently proposed radial-trace TFPF adapts radial-trace transform to reduce the dominant frequencies of the effective signals. Nevertheless, the radial traces with a fixed inclination angle have some limitations for bent reflection events. To resolve these shortcomings, this paper presents a novel variable-eccentricity hyperbolic-trace TFPF. In this novel method, the noisy record is first resampled along a family of spatial-temporal hyperbolic filtering traces of different bending degrees. In this way, the spatial correlation between the adjacent channels is taken into account, the linearity of the input signals is enhanced, and the estimation bias of the instantaneous frequency is reduced. Moreover, there is little difference between the reduced dominant frequencies. A fixed WL is suitable for all reduced dominant frequencies without distortion of the effective components. Finally, we evaluate the performance of our method on some synthetic records and field data. The experimental results illustrate that our proposed method attenuates random noise effectively and recovers the effective reflection events smoothly and more continuously compared with the other methods. [ABSTRACT FROM PUBLISHER]

The automatic seismic signal detection constitutes a very interesting and challenging task. The main difficulty in solving this problem is attributed to the fact that both the statistical properties of seismic noise and the characteristics of the recorded events are in general unknown. In this paper, by exploiting the particular nature of the signals we are treating, as well as a number of very interesting properties possessed by exchangeable random variables, we formulate the problem at hand as a record segmentation one and propose the use of two functionally linked test statistics for its efficient and robust solution, in a two-step procedure. By following this approach, we succeed not only in detecting a seismic wave arrival, but also in identifying the entire interval occupied by the signal, while minimizing the number of the required parameters. The performance of the proposed technique is confirmed by a series of experiments, both in synthetic and real seismic data sets. [ABSTRACT FROM PUBLISHER]