- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyper-Laplacian Regularized Low-Rank Tensor Decomposition for Hyperspectral Anomaly Detection
摘要： This paper presents a novel method for hyperspectral anomaly detection considering the spectral redundancy and exploiting spectral-spatial information at the same time. We proposed a Hyper-Laplacian regularized low-rank tensor decomposition method combing with dimensionality reduction framework. Firstly, k-means++ algorithm is implemented to spectral bands and centers of each group are selected to reduce the HSI dimensionality in spectral direction. To jointly utilize spectral-spatial information, the cubic data (two spatial dimensions and one spectral dimension) is treated as a 3-order tensor. Then the non-local self-similarity is fully explored in our method. For the reason to reduce the ringing artifacts caused by over-lapped segmentation in exploring the non-local self-similarity, we introduce the hyper-Laplacian constrained low-rank tensor decomposition and we get the separated background and residual parts. Finally, to eliminate the effect of Gaussian noise, we use local-RX basic detector to detect the residual matrix. Experimental results on two real hyperspectral data sets verified the effectiveness of the proposed algorithms for HSI anomaly detection.
关键词： low-rank tensor decomposition,hyperspectral anomaly detection,Dimensionality reduction
更新于2025-09-23 15:23:52
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Polarimetric Interferometric SAR Change Detection Discrimination
摘要： A coherent change detection (CCD) image, computed from a geometrically matched, temporally separated pair of complex-valued synthetic aperture radar (SAR) image sets, conveys the pixel-level equivalence between the two observations. Low-coherence values in a CCD image are typically due to either some physical change in the corresponding pixels or a low signal-to-noise observation. A CCD image does not directly convey the nature of the change that occurred to cause low coherence. In this paper, we introduce a mathematical framework for discriminating between different types of change within a CCD image. We utilize the extra degrees of freedom and information from polarimetric interferometric SAR (PolInSAR) data and PolInSAR processing techniques to define a 29-dimensional feature vector that contains information capable of discriminating between different types of change in a scene. We also propose two change-type discrimination functions that can be trained with feature vector training data and demonstrate change-type discrimination on an example image set for three different types of change. Furthermore, we also describe and characterize the performance of the two proposed change-type discrimination functions by way of receiver operating characteristic curves, confusion matrices, and pass matrices.
关键词： polarimetric interferometric synthetic aperture radar (PolInSAR),H/A/α filter,probabilistic feature fusion (PFF) model,feature vector,Coherent change detection (CCD),optimum coherence (OC),H/A/α decomposition
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Gaussian Decomposition of LiDAR Waveform Data Simulated by Dart
摘要： Light Detection And Ranging (LiDAR) techniques have been extensively applied in spaceborne, airborne and ground-based platforms. Understanding LiDAR data requires modeling approaches that can precisely account for the physical interactions between the emitted laser pulse and reflecting targets. Diverse LiDAR data types arise from different systems, platforms, and applications. However, most existing physical models consider only single pulse configurations to simulate large footprint LiDAR waveforms, which do not correspond to standard data formats. Hence, in many cases, model outputs are not well adapted to research conducted with actual LiDAR systems, especially for Aerial and Terrestrial Laser Scanning (ALS and TLS) systems. The Discrete Anisotropic Radiation Transfer (DART) model provides accurate and efficient simulations of multiple LiDAR pulses from all platform types. This paper presents the latest development of the DART LiDAR module: Gaussian decomposition of the simulated ALS and TLS waveforms followed by the provision of LiDAR point cloud and waveforms in text and standard ASPRS LAS formats.
关键词： point cloud,DART,waveform,LiDAR,ALS,Gaussian decomposition,TLS
更新于2025-09-23 15:23:52
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Interference Suppression of Partially Overlapped Signals Using GSVD and Orthogonal Projection
摘要： In order to solve the problem in Automatic Identification System (AIS) that the signal in the target slot cannot be correctly received due to partial overlap of signals in adjacent time slots, the paper introduces a new criterion: maximum expected signal power (MESP) and proposes a novel beamforming algorithm based on generalized singular value decomposition (GSVD) and orthogonal projection. The algorithm employs GSVD to estimate the signal subspace, and adopts orthogonal projection to project the received signal onto the orthogonal subspace of the non-target signal. Then, beamforming technique is used to maximize the output power of the target signal on the basis of MESP. Theoretical analysis and simulation results show the effectiveness of the proposed algorithm.
关键词： blind beamforming,automatic identification system,partial overlapping,generalized singular value decomposition,orthogonal projection
更新于2025-09-23 15:23:52
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A VIE-based algebraic domain decomposition for analyzing electromagnetic scattering from inhomogeneous isotropy/anisotropy dielectric objects
摘要： A VIE-based domain decomposition method (DDM) is proposed for analyzing EM scattering from inhomogeneous electrically large dielectric objects. The volume integral equation (VIE) still uses tetrahedra to model the entire body and uses the SWG basis functions to expand the equivalent electric ?ux density. This new DDM is established by dividing the unknowns on the whole electrically large body into groups, serving as subdomains. Through necessary symmetry treatment of standard MoM impedance matrix, the DDM using subdomain-decoupling technology can be combined with the VIE model to reduce memory requirement. Actually, this is an algebraic DDM, not a geometric DDM. In other words, it has no requirement of physical location of basis functions belonging to the same subdomain. This decoupling procedure is completely eliminating the coupling impact of the primary subdomain with the rest of the dielectric body, until every subdomain is independent with each other. In this work, when solving ultimate decoupled impedance subdomain matrix, the LU decomposition process for solving interpolating coef?cients of multiple right sides is accelerated by GPU parallel technology to signi?cantly decrease CPU time. In brief, this paper ?rst time combines the algebraic DDM with the conventional VIE model (including both isotropy and anisotropy VIE model) to signi?cantly decrease the requirement of memory. At last, a few representative numerical examples are provided to demonstrate validity, ef?ciency and stability of the new method.
关键词： MoM,volume integral equation,domain decomposition
更新于2025-09-23 15:23:52
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A detail preserving variational model for image Retinex
摘要： In this paper, we propose a detail preserving variational model for Retinex to simultaneously estimate the illumination and the reflectance from an observed image. Most previous models use the log-transform as pretreatment which results in loss of details in reflectance. From this observation, a detail preserving variational method is proposed for better decomposition. Different from the log-transform based models, the proposed model performs the decomposition directly in the image domain. Mathematically, we prove the existence of a solution for the proposed model. Numerically, we derive an efficient iterative algorithm by utilizing alternating direction method of multipliers (ADMM) method. Experimental results demonstrate the effectiveness of the proposed method. Compared with other closely related Retinex methods, the proposed method achieves competitive results on both subjective and objective assessments.
关键词： Reflectance,Retinex,Illumination,Variational model,Image decomposition
更新于2025-09-23 15:23:52
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[IEEE 2018 17th International Conference on Ground Penetrating Radar (GPR) - Rapperswil, Switzerland (2018.6.18-2018.6.21)] 2018 17th International Conference on Ground Penetrating Radar (GPR) - Full-polarimetric GPR for detecting ice fractures
摘要： The real-time monitoring of ice fracture width and direction is of great significance for the safety of Antarctic scientific expedition and the understanding of the process of ice fracture induced by avalanches. Using full-polarimetric ground penetrating radar (GPR) system to detect ice cracks, the scattering matrix is obtained and processed by polarization decomposition. Compared with the traditional pulse radar, more comprehensive and more intuitive information of ice cracks can be obtained. By using the Pauli decomposition method in polarization decomposition, the forward data obtained by the three-dimensional finite difference time domain (FDTD) method is processed, and the applicability of the proposed decomposition method is proved. For testing the feasibility, we performed numerical and laboratory experiments and applied the Pauli decomposition to the GPR data for ice fracture characterization. On this basis, the method is used to imaging small scale ice cracks on the lake surface, and good results are achieved, which paves the way for further practical application.
关键词： full-polarimetric GPR,decomposition,ice fracture detection
更新于2025-09-23 15:22:29
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Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in Images
摘要： Noise level estimation is a challenging area of digital image processing with a variety of applications, including image enhancement, image segmentation, and feature extraction. In this paper, an adaptive estimation of additive white Gaussian noise level based on the singular value decomposition (SVD) of images is proposed. The proposed algorithm aims to improve the performance of noise level estimation in the SVD domain at low noise levels. An initial noise level estimate is used to adjust the parameters of the algorithm in order to increase the accuracy of noise level estimation. The proposed algorithm exhibits the ability to adapt the number of considered singular values and to accordingly adjust the slope of a linear function that describes how the average value of the singular value tail varies with noise levels. Although, for each image, the proposed algorithm performs the noise level estimation twice in two distinct stages, the singular value decompositions are only performed in the first stage of the algorithm. The experimental results demonstrate that the proposed algorithm improves the noise level estimation at low noise levels without a significant increase in computational complexity. At noise level σ = 15, the improvements in the mean square level are about 39% at the expense of slightly higher additional computational time.
关键词： artificial neural networks,singular value decomposition,image analysis,noise level estimation,Digital images,AWGN,least square methods
更新于2025-09-23 15:22:29
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[IEEE 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO) - Kobe, Japan (2018.5.28-2018.5.31)] 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) - Multi-Scale Gradient Domain Underwater Image Enhancement
摘要： Underwater images often suffer from low visibility, due to the attenuation and scattering of the propagated light, which are caused by the dense and non-uniform medium. Based on the observation that degradation of underwater images occurs both in contrast and color, this paper aims at compensating the contrast and color saturation of the image respectively. To achieve this, we first white balance the degraded image to remove the color casts while producing a natural appearance of the underwater image. Then propose a multi-scale gradient domain contrast enhancement strategy to increase the visibility, and compensate the attenuation of color saturation according to the estimated transmission. Both qualitative and quantitative results demonstrate the effectiveness of the proposed method. Our method yields accurate results with significantly enhanced contrast and superior color, even better than other state-of-the-art methods.
关键词： Underwater image,Gradient Domain,Contrast enhancement,Edge-preserving image decomposition,Color correction
更新于2025-09-23 15:22:29
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[IEEE 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO) - Kobe (2018.5.28-2018.5.31)] 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) - DEMON Spectrum Extraction Method Using Empirical Mode Decomposition
摘要： The noise radiated by a ship is modulated at a rate dictated by some parameters of the propeller and engine (number of blades, rotational speed). Evaluation of that modulation provides information on the ship, such as the shaft rotation frequency, that can be used for ship classification. The method for estimation of the envelope modulation is known as DEMON (Detection of Envelope Modulation on Noise). Traditionally, the ship noise is bandpass filtered in different frequency bands before the envelope analysis. The bandwidth and the number of the bandpass filters is not known. In this paper a new DEMON spectrum extraction method is proposed using empirical mode decomposition (EMD), in which the band number and width are automatically determined. In performance test, a feedforward neural network is used for 5 kinds ship noise classification, and the percentage of correct classification reaches 91.6%.
关键词： DEMON,empirical mode decomposition,Detection of envelope modulation on noise,feedforward neural network,EMD
更新于2025-09-23 15:22:29