Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality

Abstract The quality and efficiency of automated Digital Elevation Model (DEM) extraction from stereoscopic satellite imagery is critically dependent on the accuracy of the sensor model used for co-locating pixels between stereo-pair images. In the absence of ground control or manual tie point selection, errors in the sensor models must be compensated with increased matching search-spaces, increasing both the computation time and the likelihood of spurious matches. Here we present an algorithm for automatically determining and compensating the relative bias in Rational Polynomial Coefficients (RPCs) between stereo-pairs utilizing hierarchical, sub-pixel image matching in object space. We demonstrate the algorithm using a suite of image stereo-pairs from multiple satellites over a range stereo-photogrammetrically challenging polar terrains. Besides providing a validation of the effectiveness of the algorithm for improving DEM quality, experiments with prescribed sensor model errors yield insight into the dependence of DEM characteristics and quality on relative sensor model bias. This algorithm is included in the Surface Extraction through TIN-based Search-space Minimization (SETSM) DEM extraction software package, which is the primary software used for the U.S. National Science Foundation ArcticDEM and Reference Elevation Model of Antarctica (REMA) products.

[1]  Changno Lee,et al.  Automated bias-compensation of rational polynomial coefficients of high resolution satellite imagery based on topographic maps , 2015 .

[2]  C. Fraser,et al.  Sensor orientation via RPCs , 2006 .

[3]  Mikel Zatarain,et al.  Self-Calibrated In-Process Photogrammetry for Large Raw Part Measurement and Alignment before Machining , 2017, Sensors.

[4]  Yongjun Zhang,et al.  Relative orientation based on multi-features , 2011 .

[5]  Amin Sedaghat,et al.  Distinctive Order Based Self-Similarity descriptor for multi-sensor remote sensing image matching , 2015 .

[6]  Wei Zhang,et al.  A Unified Framework for Street-View Panorama Stitching , 2016, Sensors.

[7]  Robert E. Wolfe,et al.  Automated registration and orthorectification package for Landsat and Landsat-like data processing , 2009 .

[8]  Gamini Dissanayake,et al.  L2-SIFT: SIFT feature extraction and matching for large images in large-scale aerial photogrammetry , 2014 .

[9]  Ian Dowman,et al.  A procedure for automatic absolute orientation using aerial photographs and a map , 1997 .

[10]  Myoung-Jong Noh,et al.  The Surface Extraction from TIN based Search-space Minimization (SETSM) algorithm , 2017 .

[11]  X. Tong,et al.  Bias-corrected rational polynomial coefficients for high accuracy geo-positioning of QuickBird stereo imagery , 2010 .

[12]  Marco Gianinetto,et al.  Automated Geometric Correction of High-resolution Pushbroom Satellite Data , 2008 .

[13]  Bin Liu,et al.  A Self-Calibration Bundle Adjustment Method for Photogrammetric Processing of Chang $^{\prime}$E-2 Stereo Lunar Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[14]  D. Lichti,et al.  An integrated bundle adjustment approach to range camera geometric self-calibration , 2010 .

[15]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.

[16]  A. Habib,et al.  Bundle Adjustment with Self–Calibration Using Straight Lines , 2002 .

[17]  C. Fraser,et al.  Bias-compensated RPCs for sensor orientation of high-resolution satellite imagery , 2005 .

[18]  Yi Ma,et al.  TILT: Transform Invariant Low-Rank Textures , 2010, ACCV 2010.

[19]  Ayman Habib,et al.  Semi-automatic registration of multi-source satellite imagery with varying geometric resolutions , 2005 .

[20]  Myoung-Jong Noh,et al.  Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions , 2015 .

[21]  Ayman Habib,et al.  Automatic relative orientation of large scale imagery over urban areas using Modified Iterated Hough Transform , 2001 .

[22]  Derek D. Lichti,et al.  Rigorous Geometric Self-Calibrating Bundle Adjustment for a Dual Fluoroscopic Imaging System , 2015, IEEE Transactions on Medical Imaging.

[23]  Ahmed F. Elaksher,et al.  SOLVING THE POINT CORRESPONDING PROBLEM BETWEEN MULTI RESOLUTION SATELLITE IMAGES USING A MODIFIED SVD-MATCHING ALGORITHM , 2008 .

[24]  Myoung-Jong Noh,et al.  Highly Dense 3D Surface Generation Using Multi‐image Matching , 2012 .

[25]  J. Grodecki,et al.  Block Adjustment of High-Resolution Satellite Images Described by Rational Polynomials , 2003 .

[26]  M. Kasser,et al.  Digital photogrammetry , 2001 .

[27]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[28]  Derek D. Lichti,et al.  Photogrammetric Bundle Adjustment With Self-Calibration of the PrimeSense 3D Camera Technology: Microsoft Kinect , 2013, IEEE Access.

[29]  C. Fraser,et al.  Bias compensation in rational functions for Ikonos satellite imagery , 2003 .

[30]  Tang Liang,et al.  Automatic Relative Orientation of Aerial Images , 1996 .