The Experts below are selected from a list of 20676 Experts worldwide ranked by ideXlab platform
Jianya Gong - One of the best experts on this subject based on the ideXlab platform.
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Precise Sensor Orientation of High-Resolution Satellite Imagery With the Strip Constraint
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Jinshan Cao, Xiuxiao Yuan, Jianya GongAbstract:To achieve precise Sensor Orientation of high- resolution satellite imagery (HRSI), ground control points (GCPs) or height models are necessary to remove biases in Orientation parameters. However, measuring GCPs is costly, laborious, and time consuming. We cannot even acquire well-defined GCPs in some areas. In this paper, a strip constraint model is established according to the geometric invariance that the biases of image points remain the same in dividing a strip image into standard images. Based on the rational function model and the strip constraint model, a feasible Sensor Orientation approach for HRSI with the strip constraint is presented. Through the use of the strip constraint, the bias compensation parameters of each standard image in the strip can be solved simultaneously with sparse GCPs. This approach remains effective even when the intermediate standard images in the strip are unavailable. Experimental results of the three ZiYuan-3 data sets show that two GCPs in the first image and two GCPs in the last image are sufficient for the Sensor Orientation of all the standard images in the strip. An Orientation accuracy that is better than 1.1 pixels can be achieved in each standard image. Moreover, the inconsistent errors of tie points between adjacent standard images can also be reduced to less than 0.1 pixel. This result can guarantee that the generated complete digital orthophoto map of the whole strip is geometrically seamless.
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Nonlinear bias compensation of ZiYuan-3 satellite imagery with cubic splines
ISPRS Journal of Photogrammetry and Remote Sensing, 2017Co-Authors: Jinshan Cao, Xiuxiao Yuan, Jianya GongAbstract:Abstract Like many high-resolution satellites such as the ALOS, MOMS-2P, QuickBird, and ZiYuan1-02C satellites, the ZiYuan-3 satellite suffers from different levels of attitude oscillations. As a result of such oscillations, the rational polynomial coefficients (RPCs) obtained using a terrain-independent scenario often have nonlinear biases. In the Sensor Orientation of ZiYuan-3 imagery based on a rational function model (RFM), these nonlinear biases cannot be effectively compensated by an affine transformation. The Sensor Orientation accuracy is thereby worse than expected. In order to eliminate the influence of attitude oscillations on the RFM-based Sensor Orientation, a feasible nonlinear bias compensation approach for ZiYuan-3 imagery with cubic splines is proposed. In this approach, no actual ground control points (GCPs) are required to determine the cubic splines. First, the RPCs are calculated using a three-dimensional virtual control grid generated based on a physical Sensor model. Second, one cubic spline is used to model the residual errors of the virtual control points in the row direction and another cubic spline is used to model the residual errors in the column direction. Then, the estimated cubic splines are used to compensate the nonlinear biases in the RPCs. Finally, the affine transformation parameters are used to compensate the residual biases in the RPCs. Three ZiYuan-3 images were tested. The experimental results showed that before the nonlinear bias compensation, the residual errors of the independent check points were nonlinearly biased. Even if the number of GCPs used to determine the affine transformation parameters was increased from 4 to 16, these nonlinear biases could not be effectively compensated. After the nonlinear bias compensation with the estimated cubic splines, the influence of the attitude oscillations could be eliminated. The RFM-based Sensor Orientation accuracies of the three ZiYuan-3 images reached 0.981 pixels, 0.890 pixels, and 1.093 pixels, which were respectively 42.1%, 48.3%, and 54.8% better than those achieved before the nonlinear bias compensation.
Clive S Fraser - One of the best experts on this subject based on the ideXlab platform.
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Georeferencing performance of THEOS satellite imagery
The Photogrammetric Record, 2011Co-Authors: Shijie Liu, Clive S Fraser, Chunsun Zhang, M Ravanbakhsh, Xiaohua TongAbstract:This paper reports on the application of a generic physical Sensor Orientation model for evaluation of the georeferencing performance of 2 m resolution imagery from the Thailand Earth Observation System (THEOS) satellite. Within the generic Sensor Orientation model, orbit and attitude data are employed to describe the satellite trajectory, which is further modelled by splines. The satellite orbit and Sensor attitude errors are then compensated via Sensor Orientation adjustment using a modest number of ground control points (GCPs), resulting in improved georeferencing. The generic Sensor model and the integration of the THEOS Orientation parameters into the model are first described. The presence of errors in the satellite line-of-sight data, which result in imprecise Sensor interior Orientation are then discussed. Such errors can be effectively accounted for through modelling via a cubic polynomial, leading to sub-pixel georeferencing accuracy. An account is then given of an experimental evaluation of THEOS georeferencing conducted in a well-established testfield near Melbourne, Australia. The results demonstrate that sub-pixel 2D geopositioning accuracy is readily achievable with single THEOS images and within strips of up to three images, with as few as six GCPs to effect an orbit adjustment. However, accuracy decreases to near the 2-pixel level over a strip length of five images.
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Sensor Orientation via RPCs
ISPRS Journal of Photogrammetry and Remote Sensing, 2006Co-Authors: Clive S Fraser, G. Dial, J. GrodeckiAbstract:The adoption of rational functions as a preferred Sensor Orientation model for narrow field of view line scanner imagery accompanied the introduction of commercial high-resolution satellite imagery (HRSI) at the turn of the millennium. This paper reviews the developments in ground point determination from HRSI via the model of terrain independent rational polynomial coefficients (RPCs). A brief mathematical background to rational functions is first presented, along with a review of the models for generating RPCs from a rigorous Sensor Orientation, and for geopositioning via either forward intersection or monoplotting. The concept of RPC block adjustment with compensation for exterior Orientation biases is then discussed, as is the means to enhance the original RPCs through a bias correction procedure. The potential for RPC block adjustment to yield sub-pixel geopositioning accuracy from HRSI is illustrated using results from experimental testing with two Quickbird stereo image pairs and three multi-image IKONOS blocks. Finally, error propagation issues in RPC block adjustment of HRSI are considered.
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bias compensated rpcs for Sensor Orientation of high resolution satellite imagery
Photogrammetric Engineering and Remote Sensing, 2005Co-Authors: Clive S Fraser, Harry B HanleyAbstract:The demand for higher quality metric products from highresolution satellite imagery (HRSI) is growing, and the number of HRSI Sensors and product options is increasing. There is a greater need to fully understand the potential and indeed shortcomings of alternative photogrammetric Sensor Orientation models for HRSI. To date, rational functions have proven to be a viable alternative model for geo-positioning, and with the recent innovation of bias-compensated RPC bundle adjustment, it has been demonstrated that Sensor Orientation to sub-pixel level can be achieved with minimal ground control. Questions have lingered, however, as to the general suitability of bias-compensated rational polynomial coefficients (RPCs), and indeed rational functions in general. The purpose of this paper is to demonstrate the wide applicability of bias-compensated RPCs for high-accuracy geopositioning from stereo HRSI. The case of stereo imagery over mountainous terrain will be specifically addressed, and results of experimental testing of both Ikonos and QuickBird imagery will be presented.
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insights into the affine model for high resolution satellite Sensor Orientation
Isprs Journal of Photogrammetry and Remote Sensing, 2004Co-Authors: Clive S Fraser, T YamakawaAbstract:Abstract As a model for Sensor Orientation and 3D geopositioning for high-resolution satellite imagery (HRSI), the affine transformation from object to image space has obvious advantages. Chief among these is that it is a straightforward linear model, comprising only eight parameters, which has been shown to yield sub-pixel geopositioning accuracy when applied to Ikonos stereo imagery. This paper aims to provide further insight into the affine model in order to understand why it performs as well as it does. Initially, the model is compared to counterpart, ‘rigorous’ affine transformation formulations which account for the conversion from a central perspective to affine image. Examination of these rigorous models sheds light on issues such as the effects of terrain and size of area, as well as upon the choice of reference coordinate system and the impact of the adopted scanning mode of the Sensor. The results of application of the affine Sensor Orientation model to four multi-image Ikonos test field configurations are then presented. These illustrate the very high geopositioning accuracy attainable with the affine model, and illustrate that the model is not affected by size of area, but can be influenced to a modest extent by mountainous terrain, the mode of scanning and the choice of object space coordinate system. Above all, the affine model is shown to be both a robust and practical Sensor Orientation/triangulation model with high metric potential.
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Sensor Orientation for high resolution satellite imagery
2002Co-Authors: T Yamakawa, Clive S FraserAbstract:An investigation into the use of alternative Sensor Orientation models and their applicability for block adjustment of high-resolution satellite imagery is reported. Ikonos Geo imagery has been employed in the investigation, and since the explicit camera model and precise exterior Orientation information required to apply conventional collinearity-based models is not provided with Ikonos data, alternative Sensor Orientation models are needed. The Orientation models considered here are bias-corrected rational functions (with vendor-supplied rational polynomial coefficients) and the affine projection model. Test results arising from the application of the alternative image Orientation/triangulation models within two multi-strip, stereo blocks of Geo imagery are reported. These results confirm that Geo imagery can yield three-dimensional geopositioning to pixel and even sub-pixel accuracy over areas of coverage extending well beyond the nominal single scene area for Ikonos. The accuracy achieved is not only consistent with expectations for rigorous Sensor Orientation models, but is also readily attainable in practice with only a small number of high-quality ground control points.
Jinshan Cao - One of the best experts on this subject based on the ideXlab platform.
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Sensor Orientation of HaiYang-1C ultraviolet imager based on a piecewise rational function model
Journal of Applied Remote Sensing, 2020Co-Authors: Jinshan Cao, Zhiqi Zhang, Shuying JinAbstract:Due to the large field angle and large geometric distortions of the HaiYang-1C ultraviolet imager (UVI), a single rational function model (SRFM) is unable to fully absorb the geometric distortions. The SRFM-based Sensor Orientation accuracy of the UVI images is thereby worse than expected. In order to improve the Sensor Orientation accuracy, a feasible Sensor Orientation method for the UVI images based on a piecewise rational function model (PRFM) is proposed. A complete UVI image is first logistically divided into five subimages, and the adjacent subimages have an overlap. Then, an SRFM is used to mathematically fit the physical Sensor model of each subimage. Finally, the five SRFMs form a continuous PRFM, and the PRFM-based Sensor Orientation of the UVI images is performed. Three HaiYang-1C UVI images were tested. The experimental results showed that the unabsorbed geometric distortions fully propagated into the SRFM-based Sensor Orientation results. The Orientation accuracy of the three images reached ∼7 pixels. In the PRFM-based Sensor Orientation, both the PRFM fitting errors and the inconsistent errors between the adjacent subimages could be negligible. The PRFM-based Orientation accuracy was thereby noticeably improved and reached >1 pixel.
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Jitter compensation of ZiYuan-3 satellite imagery based on object point coincidence
International Journal of Remote Sensing, 2019Co-Authors: Jinshan Cao, Bo Yang, Mi WangAbstract:Satellite jitter is a very important factor that affects the Sensor Orientation of ZiYuan-3 imagery based on a rational function model (RFM). The conventional affine transformation model is unable to compensate such periodic jitters. The Sensor Orientation accuracy is thereby worse than expected. To eliminate the influence of jitters and improve the Orientation accuracy, a feasible jitter compensation method for ZiYuan-3 imagery based on object point coincidence is presented in this study. In this method, no actual ground control points (AGCPs) are required to estimate the jitter compensation parameters. First, numerous virtual object points are projected onto the image by using the RFM. Then, the residual errors between the image-space coordinates of the projected and corresponding points are used to detect the satellite jitters. Finally, two sinusoidal functions are used to model and compensate the jitters. Experimental results of the three ZiYuan-3 satellite images show that before the jitter compensation, the residual errors of the independent check points obviously show a sinusoidal pattern. These periodic errors cannot be effectively compensated by the affine transformation model even if the number of AGCPs is increased from 4 to 16. After the jitters are compensated with the estimated sinusoidal coefficients, the influence of jitters can be eliminated. The Sensor Orientation accuracies of the three images reach 0.852 pixels, 0.798 pixels, and 0.921 pixels, which are respectively 49.7%, 55.1%, 65.7% better than those achieved before the jitter compensation.
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Precise Sensor Orientation of High-Resolution Satellite Imagery With the Strip Constraint
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Jinshan Cao, Xiuxiao Yuan, Jianya GongAbstract:To achieve precise Sensor Orientation of high- resolution satellite imagery (HRSI), ground control points (GCPs) or height models are necessary to remove biases in Orientation parameters. However, measuring GCPs is costly, laborious, and time consuming. We cannot even acquire well-defined GCPs in some areas. In this paper, a strip constraint model is established according to the geometric invariance that the biases of image points remain the same in dividing a strip image into standard images. Based on the rational function model and the strip constraint model, a feasible Sensor Orientation approach for HRSI with the strip constraint is presented. Through the use of the strip constraint, the bias compensation parameters of each standard image in the strip can be solved simultaneously with sparse GCPs. This approach remains effective even when the intermediate standard images in the strip are unavailable. Experimental results of the three ZiYuan-3 data sets show that two GCPs in the first image and two GCPs in the last image are sufficient for the Sensor Orientation of all the standard images in the strip. An Orientation accuracy that is better than 1.1 pixels can be achieved in each standard image. Moreover, the inconsistent errors of tie points between adjacent standard images can also be reduced to less than 0.1 pixel. This result can guarantee that the generated complete digital orthophoto map of the whole strip is geometrically seamless.
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Nonlinear bias compensation of ZiYuan-3 satellite imagery with cubic splines
ISPRS Journal of Photogrammetry and Remote Sensing, 2017Co-Authors: Jinshan Cao, Xiuxiao Yuan, Jianya GongAbstract:Abstract Like many high-resolution satellites such as the ALOS, MOMS-2P, QuickBird, and ZiYuan1-02C satellites, the ZiYuan-3 satellite suffers from different levels of attitude oscillations. As a result of such oscillations, the rational polynomial coefficients (RPCs) obtained using a terrain-independent scenario often have nonlinear biases. In the Sensor Orientation of ZiYuan-3 imagery based on a rational function model (RFM), these nonlinear biases cannot be effectively compensated by an affine transformation. The Sensor Orientation accuracy is thereby worse than expected. In order to eliminate the influence of attitude oscillations on the RFM-based Sensor Orientation, a feasible nonlinear bias compensation approach for ZiYuan-3 imagery with cubic splines is proposed. In this approach, no actual ground control points (GCPs) are required to determine the cubic splines. First, the RPCs are calculated using a three-dimensional virtual control grid generated based on a physical Sensor model. Second, one cubic spline is used to model the residual errors of the virtual control points in the row direction and another cubic spline is used to model the residual errors in the column direction. Then, the estimated cubic splines are used to compensate the nonlinear biases in the RPCs. Finally, the affine transformation parameters are used to compensate the residual biases in the RPCs. Three ZiYuan-3 images were tested. The experimental results showed that before the nonlinear bias compensation, the residual errors of the independent check points were nonlinearly biased. Even if the number of GCPs used to determine the affine transformation parameters was increased from 4 to 16, these nonlinear biases could not be effectively compensated. After the nonlinear bias compensation with the estimated cubic splines, the influence of the attitude oscillations could be eliminated. The RFM-based Sensor Orientation accuracies of the three ZiYuan-3 images reached 0.981 pixels, 0.890 pixels, and 1.093 pixels, which were respectively 42.1%, 48.3%, and 54.8% better than those achieved before the nonlinear bias compensation.
Lawrence Carin - One of the best experts on this subject based on the ideXlab platform.
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Adaptive multiaspect target classification and detection with hidden Markov models
IEEE Sensors Journal, 2005Co-Authors: Xuejun Liao, Lawrence CarinAbstract:Target detection and classification are considered based on backscattered signals observed from a sequence of target-Sensor Orientations, with the measurements performed as a function of Orientation (angle) at a fixed range. The theory of optimal experiments is applied to adaptively optimize the sequence of target-Sensor Orientations considered. This is motivated by the fact that if fewer, better-chosen measurements are used then targets can be recognized more accurately with less time and expense. Specifically, based on the previous sequence of observations O/sub t/={O/sub 1/,...,O/sub t/}, the technique determines what change in relative target-Sensor Orientation /spl Delta//spl theta//sub t+1/ is optimal for performing measurement t+1, to yield observation O/sub t+1/. The target is assumed distant or hidden, and, therefore, the absolute target-Sensor Orientation is unknown. We detail the adaptive-sensing algorithm, employing a hidden Markov model representation of the multiaspect scattered fields, and example classification and detection results are presented for underwater targets using acoustic scattering data.
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dual hidden markov model for characterizing wavelet coefficients from multi aspect scattering data
Signal Processing, 2001Co-Authors: Nilanjan Dasgupta, P. Runkle, Luise S. Couchman, Lawrence CarinAbstract:Abstract Angle-dependent scattering (electromagnetic or acoustic) is considered from a general target, for which the scattered signal is a non-stationary function of the target-Sensor Orientation. A statistical model is presented for the wavelet coefficients of such a signal, in which the angular non-stationarity is characterized by an “outer” hidden Markov model (HMMo). The statistics of the wavelet coefficients, within a state of the outer HMM, are characterized by a second, “inner” HMMi, exploiting the tree structure of the wavelet decomposition. This dual-HMM construct is demonstrated by considering multi-aspect target identification using measured acoustic scattering data.
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Target identification with wave-based matched pursuits and hidden Markov models
IEEE Transactions on Antennas and Propagation, 1999Co-Authors: P. Bharadwaj, P. Runkle, Lawrence CarinAbstract:The method of matched pursuits is an algorithm by which a waveform is parsed into its fundamental constituents here, in the context of short-pulse electromagnetic scattering, wavefronts, and resonances (constituting what we have called wave-based matched pursuits). The wave-based matched-pursuits algorithm is used to develop a codebook of features that are representative of time-domain scattering from a target of interest, accounting for the variability of such as a function of target-Sensor Orientation. This codebook is subsequently used in the context of a hidden Markov model (HMM) in which the probability of measuring a particular codebook element is quantified as a function of target-Sensor Orientation. We review the wave-based matched-pursuits algorithm and its use in the context of an HMM (for target identification). Finally, this new wave-based signal processing algorithm is demonstrated with simulated scattering data, with additive noise.
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Hidden Markov models for multiaspect target classification
IEEE Transactions on Signal Processing, 1999Co-Authors: P. Runkle, P. Bharadwaj, Luise S. Couchman, Lawrence CarinAbstract:This article presents a new approach for target identification, in which we fuse scattering data from multiple target-Sensor Orientations. The multiaspect data is processed via hidden Markov model (HMM) classifiers, buttressed by physics-based feature extraction. This approach explicitly accounts for the fact that the target-Sensor Orientation is generally unknown or "hidden". Discrimination results are presented for measured scattering data.
T Yamakawa - One of the best experts on this subject based on the ideXlab platform.
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insights into the affine model for high resolution satellite Sensor Orientation
Isprs Journal of Photogrammetry and Remote Sensing, 2004Co-Authors: Clive S Fraser, T YamakawaAbstract:Abstract As a model for Sensor Orientation and 3D geopositioning for high-resolution satellite imagery (HRSI), the affine transformation from object to image space has obvious advantages. Chief among these is that it is a straightforward linear model, comprising only eight parameters, which has been shown to yield sub-pixel geopositioning accuracy when applied to Ikonos stereo imagery. This paper aims to provide further insight into the affine model in order to understand why it performs as well as it does. Initially, the model is compared to counterpart, ‘rigorous’ affine transformation formulations which account for the conversion from a central perspective to affine image. Examination of these rigorous models sheds light on issues such as the effects of terrain and size of area, as well as upon the choice of reference coordinate system and the impact of the adopted scanning mode of the Sensor. The results of application of the affine Sensor Orientation model to four multi-image Ikonos test field configurations are then presented. These illustrate the very high geopositioning accuracy attainable with the affine model, and illustrate that the model is not affected by size of area, but can be influenced to a modest extent by mountainous terrain, the mode of scanning and the choice of object space coordinate system. Above all, the affine model is shown to be both a robust and practical Sensor Orientation/triangulation model with high metric potential.
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Geopositioning from high-resolution satellite imagery: experiences with the affine Sensor Orientation model
IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2003Co-Authors: C.s. Fraser, T Yamakawa, H.b. Hanley, P.m. DareAbstract:Sensor Orientation based on a 3D affine transformation is appealing for high-resolution satellite imagery for three reasons: 1) Narrow-angle imaging Sensors can be characterised by a skew parallel projection, 2) the affine model is linear, and 3) it is a straightforward computational model requiring a minimum of only four ground control points. Application of the affine model with Ikonos imagery has produced sub-pixel 3D geopositioning accuracy. In spite of this, there remain questions about the fidelity of the model where the area covered is large and where there is a significant terrain height variation. This paper describes the application of the affine model to multi-scene Ikonos stereo imagery covering large block areas. It is shown that area is not a factor influencing the impressive metric performance of the affine for pixel-level geopositioning.
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Sensor Orientation for high resolution satellite imagery
2002Co-Authors: T Yamakawa, Clive S FraserAbstract:An investigation into the use of alternative Sensor Orientation models and their applicability for block adjustment of high-resolution satellite imagery is reported. Ikonos Geo imagery has been employed in the investigation, and since the explicit camera model and precise exterior Orientation information required to apply conventional collinearity-based models is not provided with Ikonos data, alternative Sensor Orientation models are needed. The Orientation models considered here are bias-corrected rational functions (with vendor-supplied rational polynomial coefficients) and the affine projection model. Test results arising from the application of the alternative image Orientation/triangulation models within two multi-strip, stereo blocks of Geo imagery are reported. These results confirm that Geo imagery can yield three-dimensional geopositioning to pixel and even sub-pixel accuracy over areas of coverage extending well beyond the nominal single scene area for Ikonos. The accuracy achieved is not only consistent with expectations for rigorous Sensor Orientation models, but is also readily attainable in practice with only a small number of high-quality ground control points.
