SATELLITE STEREO BASED DIGITAL SURFACE MODEL GENERATION USING SEMI GLOBAL MATCHING IN OBJECT AND IMAGE SPACE

This paper presents methodology and evaluation of Digital Surface Models (DSM) generated from satellite stereo imagery using Semi Global Matching (SGM) applied in image space and georeferenced voxel space. SGM is a well known algorithm, used widely for DSM generation from airborne and satellite imagery. SGM is typically applied in the image space to compute disparity map corresponding to a stereo image pair. As a different approach, SGM can be applied directly to the georeferenced voxel space similar to the approach of volumetric multi-view reconstruction techniques. The matching in voxel space simplifies the DSM generation pipeline because the stereo rectification and triangulation steps are not required. For a comparison, the complete pipeline for generation of DSM from satellite pushbroom sensors is also presented. The results on the ISPRS satellite stereo benchmark using Worldview stereo imagery of 0.5m resolution shows that the SGM applied in image space produce slightly better results than its object space counterpart. Furthermore, a qualitative analysis of the results on Worldview-3 stereo and Pleiades tri-stereo images are presented.

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RPC STEREO PROCESSOR (RSP) – A SOFTWARE PACKAGE FOR DIGITAL SURFACE MODEL AND ORTHOPHOTO GENERATION FROM SATELLITE STEREO IMAGERY

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-1-77-2016 ◽

2016 ◽

Vol III-1 ◽

pp. 77-82 ◽

Cited By ~ 17

Author(s):

R. Qin

Keyword(s):

Software Package ◽

Large Scale ◽

Surface Model ◽

Matching Methods ◽

Sensing Applications ◽

Rational Polynomial ◽

Stereo Imagery ◽

Global Matching ◽

Commercial Purpose ◽

Matching Strategy

Large-scale Digital Surface Models (DSM) are very useful for many geoscience and urban applications. Recently developed dense image matching methods have popularized the use of image-based very high resolution DSM. Many commercial/public tools that implement matching methods are available for perspective images, but there are rare handy tools for satellite stereo images. In this paper, a software package, RPC (rational polynomial coefficient) stereo processor (RSP), is introduced for this purpose. RSP implements a full pipeline of DSM and orthophoto generation based on RPC modelled satellite imagery (level 1+), including level 2 rectification, geo-referencing, point cloud generation, pan-sharpen, DSM resampling and ortho-rectification. A modified hierarchical semi-global matching method is used as the current matching strategy. Due to its high memory efficiency and optimized implementation, RSP can be used in normal PC to produce large format DSM and orthophotos. This tool was developed for internal use, and may be acquired by researchers for academic and non-commercial purpose to promote the 3D remote sensing applications.

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SEMI – GLOBAL MERGING OF DIGITAL SURFACE MODELS FROM MULTIPLE STEREOPAIRS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w4-267-2017 ◽

2017 ◽

Vol IV-2/W4 ◽

pp. 267-271

Author(s):

S. Pang ◽

X. Hu ◽

M. Zhang ◽

L. Ye

Keyword(s):

Global Optimization ◽

Optimization Algorithm ◽

Experimental Results ◽

Surface Model ◽

Digital Surface Model ◽

Global Optimization Algorithm ◽

Smoothness Constraint ◽

Dense Matching ◽

Surface Models ◽

Global Matching

The semi-global optimization algorithm, which approximates a global 2D smoothness constraint by combining several 1D constraints, has been widely used in the field of image dense matching for digital surface model (DSM) generation. However, due to occlusion, shadow and textureless area of the matching images, some inconsistency may exist in the overlapping areas of different DSMs. To address this problem, based on the DSMs generated by semi-global matching from multiple stereopairs, a novel semi-global merging algorithm is proposed to generate a reliable and consistent DSM in this paper. Two datasets, each covering 1&thinsp;km2, are used to validate the proposed method. Experimental results show that the optimal DSM after merging can effectively eliminate the inconsistency and reduce redundancy in the overlapping areas.

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RPC STEREO PROCESSOR (RSP) – A SOFTWARE PACKAGE FOR DIGITAL SURFACE MODEL AND ORTHOPHOTO GENERATION FROM SATELLITE STEREO IMAGERY

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-1-77-2016 ◽

2016 ◽

Vol III-1 ◽

pp. 77-82 ◽

Cited By ~ 4

Author(s):

R. Qin

Keyword(s):

Software Package ◽

Large Scale ◽

Surface Model ◽

Matching Methods ◽

Sensing Applications ◽

Rational Polynomial ◽

Stereo Imagery ◽

Global Matching ◽

Commercial Purpose ◽

Matching Strategy

Large-scale Digital Surface Models (DSM) are very useful for many geoscience and urban applications. Recently developed dense image matching methods have popularized the use of image-based very high resolution DSM. Many commercial/public tools that implement matching methods are available for perspective images, but there are rare handy tools for satellite stereo images. In this paper, a software package, RPC (rational polynomial coefficient) stereo processor (RSP), is introduced for this purpose. RSP implements a full pipeline of DSM and orthophoto generation based on RPC modelled satellite imagery (level 1+), including level 2 rectification, geo-referencing, point cloud generation, pan-sharpen, DSM resampling and ortho-rectification. A modified hierarchical semi-global matching method is used as the current matching strategy. Due to its high memory efficiency and optimized implementation, RSP can be used in normal PC to produce large format DSM and orthophotos. This tool was developed for internal use, and may be acquired by researchers for academic and non-commercial purpose to promote the 3D remote sensing applications.

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POINT CLOUD AND DIGITAL SURFACE MODEL GENERATION FROM HIGH RESOLUTION MULTIPLE VIEW STEREO SATELLITE IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-363-2018 ◽

2018 ◽

Vol XLII-2 ◽

pp. 363-370 ◽

Cited By ~ 6

Author(s):

K. Gong ◽

D. Fritsch

Keyword(s):

High Resolution ◽

Satellite Imagery ◽

Point Cloud ◽

Relative Bias ◽

Surface Model ◽

Model Generation ◽

Digital Surface Model ◽

Multiple View ◽

Surface Models ◽

Image Pairs

Nowadays, multiple-view stereo satellite imagery has become a valuable data source for digital surface model generation and 3D reconstruction. In 2016, a well-organized multiple view stereo publicly benchmark for commercial satellite imagery has been released by the John Hopkins University Applied Physics Laboratory, USA. This benchmark motivates us to explore the method that can generate accurate digital surface models from a large number of high resolution satellite images. In this paper, we propose a pipeline for processing the benchmark data to digital surface models. As a pre-procedure, we filter all the possible image pairs according to the incidence angle and capture date. With the selected image pairs, the relative bias-compensated model is applied for relative orientation. After the epipolar image pairs’ generation, dense image matching and triangulation, the 3D point clouds and DSMs are acquired. The DSMs are aligned to a quasi-ground plane by the relative bias-compensated model. We apply the median filter to generate the fused point cloud and DSM. By comparing with the reference LiDAR DSM, the accuracy, the completeness and the robustness are evaluated. The results show, that the point cloud reconstructs the surface with small structures and the fused DSM generated by our pipeline is accurate and robust.

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Learned Collaborative Stereo Refinement

International Journal of Computer Vision ◽

10.1007/s11263-021-01485-5 ◽

2021 ◽

Author(s):

Patrick Knöbelreiter ◽

Thomas Pock

Keyword(s):

Structural Properties ◽

Gradient Method ◽

Color Image ◽

Image Space ◽

Activation Functions ◽

Disparity Map ◽

Proximal Gradient Method ◽

Variational Structure ◽

Disparity Maps ◽

The Individual

AbstractIn this work, we propose a learning-based method to denoise and refine disparity maps. The proposed variational network arises naturally from unrolling the iterates of a proximal gradient method applied to a variational energy defined in a joint disparity, color, and confidence image space. Our method allows to learn a robust collaborative regularizer leveraging the joint statistics of the color image, the confidence map and the disparity map. Due to the variational structure of our method, the individual steps can be easily visualized, thus enabling interpretability of the method. We can therefore provide interesting insights into how our method refines and denoises disparity maps. To this end, we can visualize and interpret the learned filters and activation functions and prove the increased reliability of the predicted pixel-wise confidence maps. Furthermore, the optimization based structure of our refinement module allows us to compute eigen disparity maps, which reveal structural properties of our refinement module. The efficiency of our method is demonstrated on the publicly available stereo benchmarks Middlebury 2014 and Kitti 2015.

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Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0053.1 ◽

2017 ◽

Vol 18 (3) ◽

pp. 897-915 ◽

Cited By ~ 20

Author(s):

Jennifer L. Jefferson ◽

Reed M. Maxwell ◽

Paul G. Constantine

Keyword(s):

Land Surface ◽

Stomatal Resistance ◽

Hydrologic Model ◽

Surface Model ◽

Land Surface Models ◽

Common Land Model ◽

Surface Models ◽

Input Parameters ◽

Rate Of Photosynthesis ◽

Quantities Of Interest

Abstract Land surface models, like the Common Land Model component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California, Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO2 Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.

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Developing a triple tree-ring constraint for tree growth and physiology in a global land surface model

10.5194/egusphere-egu21-13546 ◽

2021 ◽

Author(s):

Jonathan Barichivich ◽

Philippe Peylin ◽

Valérie Daux ◽

Camille Risi ◽

Jina Jeong ◽

...

Keyword(s):

Tree Ring ◽

Land Surface ◽

Forest Ecosystems ◽

Ring Width ◽

Land Surface Model ◽

Surface Model ◽

Growth Variability ◽

Tree Ring Data ◽

Surface Models ◽

Global Land

Gradual anthropogenic warming and parallel changes in the major global biogeochemical cycles are slowly pushing forest ecosystems into novel growing conditions, with uncertain consequences for ecosystem dynamics and climate. Short-term forest responses (i.e., years to a decade) to global change factors are relatively well understood and skilfully simulated by land surface models (LSMs). However, confidence on model projections weaken towards longer time scales and to the future, mainly because the long-term responses (i.e., decade to century) of these models remain unconstrained. This issue limits confidence on climate model projections. Annually-resolved tree-ring records, extending back to pre-industrial conditions, have the potential to constrain model responses at interannual to centennial time scales. Here, we constrain the representation of tree growth and physiology in the ORCHIDEE global land surface model using the simulated interannual variability of tree-ring width and carbon (&#916;13C) and oxygen (&#948;18O) stable isotopes in six sites in boreal and temperate Europe.&#160; The model simulates &#916;13C (r = 0.31-0.80) and &#948;18O (r = 0.36-0.74) variability better than tree-ring width variability (r < 0.55), with an overall skill similar to that of other state-of-the-art models such as MAIDENiso and LPX-Bern. These results show that growth variability is not well represented, and that the parameterization of leaf-level physiological responses to drought stress in the temperate region can be improved with tree-ring data. The representation of carbon storage and remobilization dynamics is critical to improve the realism of simulated growth variability, temporal carrying over and recovery of forest ecosystems after climate extremes. The simulated physiological response to rising CO2 over the 20th century is consistent with tree-ring data in the temperate region, despite an overestimation of seasonal drought stress and stomatal control on photosynthesis. Photosynthesis correlates directly with isotopic variability, but correlations with &#948;18O combine physiological effects and climate variability impacts on source water signatures. The integration of tree-ring data (i.e. the triple constraint: width, &#916;13C and &#948;18O) and land surface models as demonstrated here should contribute towards reducing current uncertainties in forest carbon and water cycling.

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