Along-Track Interferometric Synthetic Aperture Radar (ATISAR) has the potential of measuring the radial velocity of the slowly moving target. The accuracy of the radial velocity is mainly determined by the accuracy of...
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ISBN:
(纸本)9781849196031
Along-Track Interferometric Synthetic Aperture Radar (ATISAR) has the potential of measuring the radial velocity of the slowly moving target. The accuracy of the radial velocity is mainly determined by the accuracy of the interferometric parameters. Sensitivity equations are good ways of analysing the impacts of system parameters for interferometric SAR. Former sensitivity analysis for ATI-SAR has been mainly focused on the case of no squint angle. In this paper, we explain the necessity of analysing the sensitivity equations in the presence of squint angle. Then the expression of the radial velocity in the presence of squint is derived and the sensitivity equations are obtained. Finally, the sensitivity of some interferometric parameters and the impact of squint angles are analysed and simulated for a particular accuracy requirement.
The performance of state-of-art image retrieval systems using Bag-of-Words representation and textual retrieval methods degrades quickly when applied to face images because their local features can not suffer variatio...
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Image registration is an important preprocessing step for image processing such as change detection,image mosaicking in remote sensing,however a crucial problem involved feature-based image registration is how to reli...
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Image registration is an important preprocessing step for image processing such as change detection,image mosaicking in remote sensing,however a crucial problem involved feature-based image registration is how to reliably establish correspondent features between sensed image and reference *** paper presents an automatic self-adaptive image registration method based on analytic energy of control points to improve the traditional ***,an multi-scale segment method based on digital curvelet transform is used to evaluate the quality difference of image pairs,and then self-adaptive parameters of SIFT feature-matching algorithm based on multi-scale grey relation of image quality is proposed to increase the feature points and correspondences;At last,to make sure the accuracy of geometrical transform parameters,a method of correspondences selection based on the distribution and matching energy of control points is *** results demonstrate that the proposed method works well in increasing control points and the correspondences of low quality remote sensing images.
Multi-channel peculiarity is one of the most widely accepted human visual system (HVS) models for perceptual image quality assessment (IQA). Otherwise than extensive studies of channel decomposition and intra-channel ...
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ISBN:
(纸本)9781479923427
Multi-channel peculiarity is one of the most widely accepted human visual system (HVS) models for perceptual image quality assessment (IQA). Otherwise than extensive studies of channel decomposition and intra-channel distortion measure, relatively scant research effort has been devoted to develop efficient multichannel evaluation pooling strategies. In this paper, we review and address the limitations of the conventional pooling models based on HVS sensitivities-weighted average. Instead, we explore the utilization of machine learning for this pooling problem, since machine learning can establish an optimal and generalized mapping that models the highly complex relationship between the multi-channel distortion evaluations and the perceived image quality. Experiments based on available subjective IQA databases demonstrate the rationality, reliability and robustness of our proposed scheme.
In this paper, we propose a novel remote sensing image compression method based on double-sparsity dictionary learning and universal trellis coded quantization (UTCQ). Recent years have seen a growing interest in the ...
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ISBN:
(纸本)9781479923427
In this paper, we propose a novel remote sensing image compression method based on double-sparsity dictionary learning and universal trellis coded quantization (UTCQ). Recent years have seen a growing interest in the study of natural image compression based on sparse representation and dictionary learning. We show that using the double-sparsity model to learn a dictionary gives much better compression results for remote sensing images, the texture of which is much richer than that of natural images. We also show that the compression performance is improved significantly when advanced quantization and entropy coding strategies are used for encoding the sparse representation coefficients. The proposed method outperforms the existing dictionary-based image coding algorithms. Additionally, our method results in better rate-distortion performance and structural similarity results than CCSDS and JPEG2000 standard.
Airborne Along-Track Interferometric Synthetic Aperture Radar (ATI-SAR) baseline error is a main error resource affecting the precision of velocity measurement of moving objects and therefore should be calibrated exte...
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Airborne Along-Track Interferometric Synthetic Aperture Radar (ATI-SAR) baseline error is a main error resource affecting the precision of velocity measurement of moving objects and therefore should be calibrated externally. The Jet Propulsion laboratory (JPL) has proposed a calibration scheme for tasks of PacRim98 and PacRim2000 based on several static objects on the ground. In this paper, the influence of phase center uncertainty on baseline determination by using PacRim method proposed by JPL is analyzed. According to the analysis, the phase center uncertainty can cause a constant part of error to the result of baseline calibration. In order to deal with this problem, an improved calibration method on the basis of sensitivity equations and some ground moving targets, whose velocities are already known, is proposed in this paper. The simulation results show that our proposed calibration method has improved the accuracy of baseline calibration and has obviously prohibited the effect of antennas' phase center uncertainty.
geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version...
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geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version. The proposed method learns an optimal kernel combination and the associated classifier simultaneously. Two feature types are extracted from images, forming basis kernels. Then these basis kernels are weighted combined and resulted the composite kernel exploits interesting points and appearance information of objects simultaneously. Weights and the detection model are finally learnt by a new algorithm. Experimental results show that the proposed method improve detection accuracy to above 88%, yields good interpretation for the selected subset of features and appears sparser than traditional single-kernel RVMs.
Road pavement reflectance is usually assumed to be invariant in short periods of time in some quantitative remote sensing *** examine its variability,reflectance sequences of concrete and asphalt pavement are measured...
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Road pavement reflectance is usually assumed to be invariant in short periods of time in some quantitative remote sensing *** examine its variability,reflectance sequences of concrete and asphalt pavement are measured in field for half a day in visible and near-infarecd（VNIR）spectral range using dual-beam *** much as 20.7%and 3.52%of relative changes are found in asphalt and concrete reflectance data at 550 nm,and all VNIR bands demonstrate similar variations found to correlate with both illumination geometry and the relative portion of diffuse *** this letter,this effect is interpreted from a mathematic *** studies are needed to model the dynamics of reflectance physically.
The Two-step algorithm (TSA) is widely used for trajectory deviation compensation of airborne Synthetic aperture radar (SAR), by which most of the mo- tion errors are compensated before Range curve migration compensat...
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The Two-step algorithm (TSA) is widely used for trajectory deviation compensation of airborne Synthetic aperture radar (SAR), by which most of the mo- tion errors are compensated before Range curve migration compensation (RCMC) and the residual after the RCMC. We found that the RCMC in the presence of the residual motion errors results in additional range shift errors hard to be compensated for. Based on theoretical investiga- tions, this shortage of TSA is reported and a new compen- sation scheme which greatly alleviates the RCMC induced errors is proposed. Besides, range resampling considering the residual motion errors is very convinent, which, how- ever, is boresome in TSA. The new method is effective to compensate the high resolution SAR systems for large tra- jectory deviations, which is hard to be achieved by TSA because of the uncompensated errors. Results on the sim- ulated data are provided to demonstrate the effectiveness of the new method.
Band-to-band registration accuracy is an important parameter of multispectral data. A novel band-to-band registration approach with high precision is proposed for the multi-spectral images of HJ-1A/B. Firstly, the mai...
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Band-to-band registration accuracy is an important parameter of multispectral data. A novel band-to-band registration approach with high precision is proposed for the multi-spectral images of HJ-1A/B. Firstly, the main causes resulted in misregistration are analyzed, and a high-order polynomial model is proposed. Secondly, a phase fringe filtering technique is employed to Phase Correlation Method based on Singular Value Decomposition (SVD-PCM) for reducing the noise in phase difference matrix. Then, experiments are carried out to build nonlinear registration models, and images of green band and red band are aligned to blue band with an accuracy of 0.1 pixels, while near infrared band with an accuracy of 0.2 pixels.
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