Yongshou Dai

PCA-based sea-ice image fusion of optical data by HIS transform and SAR data by wavelet transform

Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral information, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be improved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transformation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar (ASAR) loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensity-saturation (HIS) space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue (RGB) space, and the optical image from the China-Brazil Earth Resources Satellite (CBERS-02B) was transferred from the RGB space to the HIS space. The principal component analysis (PCA) method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.

Seismic Wavelet Estimation Based on Adaptive Chaotic Embedded Particle Swarm Optimization Algorithm

Cumulants matching method for seismic wavelet extraction is ultimately a multi-parameters, multi-extremes nonlinear functional optimization process. in this paper, a novel improved PSO algorithm (Adaptive Chaotic Embedded Particle Swarm Optimization, ACEPSO) has been proposed to be applied in wavelet parameters estimation. ACEPSO embeds chaotic variables in standard particle swarm optimization algorithm, and adjusts parameters nonlinearly and adaptively. It also estimates particles whether being focusing or discrete by judging the population fitness variance of particle swarm and average distance amongst points, then chaotic researching is applied to escaping from premature convergence. Simulation experimental results in wavelet extraction of synthetic seismogram and real seismic records show that this algorithm has high precision, good applicability and stability in seismic wavelet estimation.

Seismic Wavelet Estimation Using High-Order Statistics and Chaos-Genetic Algorithm

Accurate and effective seismic wavelet estimation has an extreme significance in the seismic data processing of high resolution, high signal-to-noise ratio and high fidelity. The emerging non-liner optimization methods enhance the applied potential for the statistical method of seismic wavelet extraction. Because non-liner optimization algorithms in the seismic wavelet estimation have the defects of low computational efficiency and low precision, Chaos-Genetic Algorithm (CGA) based on the cat mapping is proposed which is applied in the multi-dimensional and multi-modal non-linear optimization. The performance of CGA is firstly verified by four test functions, and then applied to the seismic wavelet estimation. Theoretical analysis and numerical simulation demonstrate that CGA has better convergence speed and convergence performance.

Research on Co-Evolutionary Genetic Algorithm of Long-Distance Gas Pipelines Optimal Model Economy

Based on the analysis of the current long-distance pipeline network running conditions, an economic optimal mathematical model of the gas transmission network including compressor station is used. The natural gas pipeline network is divided into different parts, and adopting the cooperation co-evolutionary genetic algorithm, the subpopulations are created. The fitness function is established by taking advantage of the punish function. The results of the simulation show that this approach has better convergence. It is an effective method to solve the optimization problem.

A time-varying wavelet phase extraction method using the wavelet amplitude spectra

ABSTRACT The vertical resolution of seismic data is greatly affected by time-varying seismic wavelets, and the phase extraction is the key topic for the accurate wavelet extraction. The traditional methods are limited to the piecewise stationary and constant-phase assumptions, so that the extracted results are not satisfactory. In this paper, we try to explore a new time-varying wavelet phase extraction method using the wavelet amplitude information. The propagation and attenuation law of seismic wavelets is analyzed, and the relationship between the amplitude and phase spectra of time-varying wavelets is obtained by deriving the wave equation for a viscoelastic medium. We find that accurately estimating the amplitude spectra at different time points and the initial phase spectrum is necessary to extract accurate time-varying wavelet phase spectra. Based on this conclusion, the wavelet amplitude spectra and initial phase spectrum are estimated based on time–frequency spectral modelling and higher-order cumulants, respectively, so that the phase spectra of time-varying wavelets are estimated by applying the obtained relationship. The numerical simulation and real seismic data processing results demonstrate that the proposed method can improve the accuracy of time-varying wavelet phase extraction compared to the traditional method.

Time-frequency analysis-based time-windowing algorithm for the inverse synthetic aperture radar imaging of ships

Abstract. An algorithm based on time-frequency analysis is proposed to select an imaging time window for the inverse synthetic aperture radar imaging of ships. An appropriate range bin is selected to perform the time-frequency analysis after radial motion compensation. The selected range bin is that with the maximum mean amplitude among the range bins whose echoes are confirmed to be contributed by a dominant scatter. The criterion for judging whether the echoes of a range bin are contributed by a dominant scatter is key to the proposed algorithm and is therefore described in detail. When the first range bin that satisfies the judgment criterion is found, a sequence composed of the frequencies that have the largest amplitudes in every moment’s time-frequency spectrum corresponding to this range bin is employed to calculate the length and the center moment of the optimal imaging time window. Experiments performed with simulation data and real data show the effectiveness of the proposed algorithm, and comparisons between the proposed algorithm and the image contrast-based algorithm (ICBA) are provided. Similar image contrast and lower entropy are acquired using the proposed algorithm as compared with those values when using the ICBA.

The parameter design results of near space airship SAR system

Near space airship SAR gets more and more attention around the word, because of its advantages of wide coverage, strong survival ability, high cost-effectiveness and long-time observation ability. At present and even the recent period of time, its hard to acquire radar parameters of SAR system on near space airship. This brings great inconvenience to the imaging processing researchers. So, according to the parameter design theory of SAR system, we design radar parameters of SAR system on near space airship operating at X-, C-, L-bands. These parameters can provide reference for the SAR imaging processing researchers.

Study on wave energy resource assessing method based on altimeter data—A case study in Northwest Pacific

Wave energy resource is a very important ocean renewable energy. A reliable assessment of wave energy resources must be performed before they can be exploited. Compared with wave model, altimeter can provide more accurate in situ observations for ocean wave which can be as a novel method for wave energy assessment. The advantage of altimeter data is to provide accurate significant wave height observations for wave. In order to develop characteristic and advantage of altimeter data and apply altimeter data to wave energy assessment, in this study, we established an assessing method for wave energy in local sea area which is dedicated to altimeter data. This method includes three parts including data selection and processing, establishment of evaluation indexes system and criterion of regional division. Then a case study of Northwest Pacific was performed to discuss specific application for this method. The results show that assessing method in this paper can assess reserves and temporal and spatial distribution effectively and provide scientific references for the siting of wave power plants and the design of wave energy convertors.

The microwave scattering characteristics of sea ice in the Bohai Sea

Microwave remote sensing has become the primary means for sea-ice research, and has been supported by a great deal of field experiments and theoretical studies regarding sea-ice microwave scattering. However, these studies have been barely carried in the Bohai Sea. The sea-ice microwave scattering mechanism was first developed for the thin sea ice with slight roughness in the Bohai Sea in the winter of 2012, and included the backscattering coefficients which were measured on the different conditions of three bands (L, C and X), two polarizations (HH and VV), and incident angles of 20° to 60°, using a ground-based scatterometer and the synchronous physical parameters of the sea-ice temperature, density, thickness, salinity, and so on. The theoretical model of the sea-ice electromagnetic scattering is obtained based on these physical parameters. The research regarding the sea-ice microwave scattering mechanism is carried out through two means, which includes the comparison between the field microwave scattering data and the simulation results of the theoretical model, as well as the feature analysis of the four components of the sea-ice electromagnetic scattering. It is revealed that the sea-ice microwave scattering data and the theoretical simulation results vary in the same trend with the incident angles. Also, there is a visible variant in the sensitivity of every component to the different bands. For example, the C and X bands are sensitive to the top surface, the X band is sensitive to the scatterers, and the L and C bands are sensitive to the bottom surface, and so on. It is suggested that the features of the sea-ice surfaces and scatterers can be retrieved by the further research in the future. This experiment can provide an experimental and theoretical foundation for research regarding the sea-ice microwave scattering characteristics in the Bohai Sea.

Application of an existing approach to refocusing maritime moving targets on Radarsat-2 SLC images

Advanced synthetic aperture radar (SAR) imaging systems provide high-resolution images for a number of applications in maritime surveillance. However, the relative movement between the radar and the moving target will cause defocusing phenomenon, leading to decreasing image resolution. In this paper, we adopt an existing approach to refocus ship targets starting from formed Single Look Complex (SLC) SAR images. Refocused images are obtained by applying inversion mapping and ISAR processing. The refocusing results on the use of RADARSAT-2 SLC images prove the effectiveness of the method.