BLOCK ADJUSTMENT OF MULTISPECTRAL IMAGES WITHOUT GCP AIDED BY STEREO TLC IMAGES FOR ZY-3 SATELLITE

Multispectral images are the main data source of optical satellite remote sensing application. Consistent geometric accuracy is the basis of image registration and fusion. But the multispectral images usually collected by the nadir imaging camera are of weak geometric intersection, which will lead to the rank defect of the adjustment equation when performing block adjustment (BA) directly, making the solution unstable. Thus, a planar BA aided by additional digital surface model (DSM) which can overcome this weak geometry is often used to improve the geometric accuracy and consistency of regional planar images. However, the inevitable elevation error and the indispensable ground control points (GCPs) make the method limited in practical application. In this paper, a new method aiming to the BA of planer multispectral images of ZY-3 satellite without use of GCPs is presented. This method introduces the constraint of stereo images to assist the BA of planar images. By configuring appropriate weights of different observations, the integrated optimization of positioning models of planar and stereo images can be achieved together. The effectiveness of the method was verified by the planar multispectral images and stereo three linear camera (TLC) images collected by ZY-3 satellite. The satisfactory results indicated the rationality and effectiveness of the presented method.

Estimability in Rank-Defect Mixed-Integer Models: Theory and Applications

<p><strong>G1.1 Session: Recent Developments in Geodetic Theory</strong></p><p><strong> </strong></p><p><strong>Estimability in Rank-Defect Mixed-Integer Models: Theory and Applications</strong></p><p><strong> </strong></p><p>PJG Teunissen<sup>1,2</sup></p><p><sup>1</sup>GNSS Research Centre, Curtin University, Perth, Australia</p><p><sup>2</sup>Geoscience and Remote Sensing, Delft University of Technology, The Netherlands</p><p>Email: [email protected]; [email protected]</p><p><strong> </strong></p><p>Although estimability is one of the foundational concepts of todays’ estimation theory, we show that the current concept of estimability is not adequately equipped to cover the estimation requirements of mixed-integer models, for instance like those of interferometric models, cellular base transceiver network models or the carrier-phase based models of Global Navigation Satellite Systems (GNSSs). We therefore need to generalize the estimability concept to that of integer-estimability. Next to being integer and estimable in the classical sense, functions of integer parameters then also need to guarantee that their integerness corresponds with integer values of the parameters the function is taken of. This is particularly crucial in the context of integer ambiguity resolution. Would this condition not be met, then the integer fixing of integer functions that are not integer-estimable implies that one can fix the undifferenced integer ambiguities to non-integer values and thus force the model to inconsistent and wrong constraints.</p><p>In this paper we present a generalized concept of estimability and one that now also is applicable to mixed-integer models. We thereby provide the operationally verifiable necessary and sufficient conditions that a function of integer parameters needs to satisfy in order to be integer-estimable. As one of the conditions we have that estimable functions become integer-estimable if they can be unimodulair transformed to canonical form. Next to the conditions, we also show how to create integer-estimable functions and how a given design matrix can be expressed in them. We then show how these results are to be applied to interferometric models, cellular base transceiver network models and FDMA GNSS models.</p><p> </p><p><strong>Keywords: </strong>Estimability, S-system theory, Mixed-integer Models, Integer-Estimability, Admissible Ambiguity Transformation, Interferometry, Global Navigation Satellite Systems (GNSSs)</p><p> </p>

A new biased estimation method based on Neumann series for solving ill-posed problems

The ill-posed least squares problems often arise in many engineering applications such as machine learning, intelligent navigation algorithms, surveying and mapping adjustment model, and linear regression model. A new biased estimation (BE) method based on Neumann series is proposed in this article to solve the ill-posed problems more effectively. Using Neumann series expansion, the unbiased estimate can be expressed as the sum of infinite items. When all the high-order items are omitted, the proposed method degenerates into the ridge estimation or generalized ridge estimation method, whereas a series of new biased estimates can be acquired by including some high-order items. Using the comparative analysis, the optimal biased estimate can be found out with less computation. The developed theory establishes the essential relationship between BE and unbiased estimation and can unify the existing unbiased and biased estimate formulas. Moreover, the proposed algorithm suits for not only ill-conditioned equations but also rank-defect equations. Numerical results show that the proposed BE method has improved accuracy over the existing robust estimation methods to a certain extent.

ALGORITHM AND APPLICATION OF GCP-INDEPENDENT BLOCK ADJUSTMENT FOR SUPER LARGE-SCALE DOMESTIC HIGH RESOLUTION OPTICAL SATELLITE IMAGERY

The accurate positioning of optical satellite image without control is the precondition for remote sensing application and small/medium scale mapping in large abroad areas or with large-scale images. In this paper, aiming at the geometric features of optical satellite image, based on a widely used optimization method of constraint problem which is called Alternating Direction Method of Multipliers (ADMM) and RFM least-squares block adjustment, we propose a GCP independent block adjustment method for the large-scale domestic high resolution optical satellite image – GISIBA (GCP-Independent Satellite Imagery Block Adjustment), which is easy to parallelize and highly efficient. In this method, the virtual "average" control points are built to solve the rank defect problem and qualitative and quantitative analysis in block adjustment without control. The test results prove that the horizontal and vertical accuracy of multi-covered and multi-temporal satellite images are better than 10 m and 6 m. Meanwhile the mosaic problem of the adjacent areas in large area DOM production can be solved if the public geographic information data is introduced as horizontal and vertical constraints in the block adjustment process. Finally, through the experiments by using GF-1 and ZY-3 satellite images over several typical test areas, the reliability, accuracy and performance of our developed procedure will be presented and studied in this paper.

Detection of varied defects in diverse fabric images via modified RPCA with noise term and defect prior

Purpose – The purpose of this paper is to present a novel method for fabric defect detection. Design/methodology/approach – The method based on joint low-rank and spare matrix recovery, since patterned fabric is manufactured by a set of predefined symmetry rules, and it can be seen as the superposition of sparse defective regions and low-rank defect-free regions. A robust principal component analysis model with a noise term is designed to handle fabric images with diverse patterns robustly. The authors also estimate a defect prior and use it to guide the matrix recovery process for accurate extraction of various fabric defects. Findings – Experiments on plain and twill, dot-, box- and star-patterned fabric images with various defects demonstrate that the method is more efficient and robust than previous methods. Originality/value – The authors present a RPCA-based model for fabric defects detection, and show how to incorporate defect prior to improve the detection results. The authors also show that more robust detection and less running time can be obtained by introducing a noise term into the model.

Autonomous Navigation of Mars Probes by Combining Optical Data of Viewing Martian Moons and SST Data

Autonomous navigation has become a key technology for deep space exploration missions. Phobos and Deimos, the two natural moons of Mars, are important optical navigation information sources available for Mars missions. However, during the phase of the probe orbiting close to Mars, the ephemeris bias and the difference between the barycentre and the centre of brightness of a Martian moon will result in low navigation accuracy. On the other hand, Satellite-to-Satellite Tracking (SST) can achieve convenient and high accuracy observation for autonomous navigation. However, this cannot apply for a Mars mission during the Mars orbit phase only by SST data because of a rank defect problem of the Jacobian matrix. To improve the autonomous navigation accuracy of Mars probes, this paper presents a new autonomous navigation method that combines SST radio data provided by two probes and optical measurement by viewing the natural Martian moons. Two sequential orbit determination algorithms, an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are compared. Simulation results show this method can obtain high autonomous navigation accuracy during the probe's Mars Orbit phase.

Using Rank-Defect Free Net Adjustment to Optimize GPS

Navigation type GPS cheap, portable and measure quickly. But the accuracy is not high. We can through the rank-defect free net adjustment method of navigation type to carry on the adjustment, the coordinates of GPS measurement to improve the accuracy of it. So we can improve the navigation type GPS in agriculture in terms of practical measurement.