Digital Elevation Model Reconstruction Research Articles

A novel multi-strategy hydrological feature extraction (MHFE) method to improve urban waterlogging risk prediction, a case study of Fuzhou City in China

Reliable hydrological data ensure the precision of the urban waterlogging simulation. To reduce the simulation error caused by insufficient basic data, a multi-strategy method (MHFE) for extracting hydrological features is proposed, which includes land use/land cover (LULC) extraction (LE) and digital elevation model (DEM) reconstruction (DR). First, the high-resolution remote image, satellite DEM, precipitation, flood points and depth, and planned LULC were collected. Second, the buildings, roads, and other areas of the satellite image were segmented using the U-Net model, and the LULC data with drainage features were extracted by combining the segmentation result with the planned LULC and drainage data. Then, the terrain features of the road were enhanced to construct high-precision DEM based on the fusion of multi-source data, such as elevation points, LULC, and satellite DEM. Finally, the waterlogging model was implemented under different return periods of rainfalls and typhoon rainfall to obtain the waterlogging distribution and water depth. The simulation results were compared with historical waterlogging event data and water depth observations. The results indicated that the proposed method significantly improved the accuracy of the simulation. In terms of identifying the waterlogging points, the average F1 score increased by 0.36, 0.20, and 0.07 compared to the raw model and the single LE and DR methods, respectively. In terms of water depth simulation, the average Nash-Sutcliffe efficiency (NSE) was increased from −0.24 to 0.86, with DR and LE contributing to 79.1 % and 20.9 %, respectively. The principal contribution and novelty of this paper is to explore the generic method that enhance the hydrological data, and the findings of this study improved the performance of urban waterlogging simulation.

An urban DEM reconstruction method based on multisource data fusion for urban pluvial flooding simulation

A digital elevation model (DEM) is crucial for hydraulic/hydrodynamic simulation of urban pluvial flooding. To reduce the simulation error caused by the low resolution and poor vertical accuracy of DEM data, a novel urban terrain reconstruction method is proposed based on multisource data fusion (MDF). A 1D-2D coupled pluvial flood model under the typhoon rainfall scenario is built to verify the superiority of the proposed method. The results show that the systematic elevation error of terrain is reduced with MDF-kriging and MDF-IDW, and the performances of simulations are improved by 5% and 23% in terms of peak water-level error compared to using conventional kriging and IDW methods, and by 0.13m and 0.11m in terms of manhole water level accuracy compared to using raw DEM. Moreover, the temporospatial patterns of the flooding simulation results are more rational with the scattered flooding eliminated, and the proportion of flood area with ground elevation lower than 8 m increased from 37.9% to 92.34% and 89.33%, respectively. This method is proven to be a notably effective and low-cost DEM reconstruction method for improving the accuracy of urban pluvial flooding simulation.

Estimating and Removing Ionospheric Effects for L-Band Spaceborne Bistatic SAR

One of the challenges of the low-frequency spaceborne synthetic aperture radar (SAR) is that propagation through the ionosphere will introduce nonnegligible errors in the final SAR product. In the low-frequency bistatic SAR (BiSAR) system, the ionosphere will degrade the imaging performance and cause nonnegligible phase errors in the single-pass SAR interferometry application, which results in undesired errors of digital elevation model (DEM). In this article, a method that embedded into the focusing procedure is proposed, which aims to estimate and remove ionospheric effects on L-band spaceborne BiSAR system. First, the impacts of ionospheric effects on the BiSAR system are demonstrated, including the deterioration of imaging performance and the geometric distortion. Then, a method is proposed to correct ionospheric effects. Afterward, the simulations, including point targets and distributed targets, are carried out to verify the effectiveness of the proposed method. The imaging results and the DEM reconstruction results show that the proposed method can effectively estimate and remove ionospheric effects on spaceborne BiSAR systems.

Structure-from-Motion on shallow reefs and beaches: potential and limitations of consumer-grade drones to reconstruct topography and bathymetry

Reconstructing the topography of shallow underwater environments using Structure-from-Motion—Multi View Stereo (SfM-MVS) techniques applied to aerial imagery from Unmanned Aerial Vehicles (UAVs) is challenging, as it involves nonlinear distortions caused by water refraction. This study presents an experiment with aerial photographs collected with a consumer-grade UAV on the shallow-water reef of Fuvahmulah, the Maldives. Under conditions of rising tide, we surveyed the same portion of the reef in ten successive flights. For each flight, we used SfM-MVS to reconstruct the Digital Elevation Model (DEM) of the reef and used the flight at low tide (where the reef is almost entirely dry) to compare the performance of DEM reconstruction under increasing water levels. Our results show that differences with the reference DEM increase with increasing depth, but are substantially larger if no underwater ground control points are taken into account in the processing. Correcting our imagery with algorithms that account for refraction did not improve the overall accuracy of reconstruction. We conclude that reconstructing shallow-water reefs (less than 1 m depth) with consumer-grade UAVs and SfM-MVS is possible, but its precision is limited and strongly correlated with water depth. In our case, the best results are achieved when ground control points were placed underwater and no refraction correction is used.

Reconstruction and Evaluation of DEMs From Bistatic Tandem-X SAR in Mountainous and Coastal Areas of China

TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) mission is designed to generate 3-D images of the Earth as the first bistatic synthetic aperture radar (SAR). However, few quantitative studies of TanDEM-X digital elevation model (DEM) quality validation have been conducted specifically in China. This article presents an iterative method to generate high-resolution TanDEM-X DEMs and assesses the vertical accuracy with high-accuracy GPS observations, 1 arc second global DEMs available [Advanced Land Observing Satellite World 3-D-30 m v2.2, Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) v3, Shuttle Radar Topography Mission (SRTM) v3.1, NASADEM, and X-band SRTM], and TanDEM-X 90 m DEM. The results demonstrate remarkable elevation quality and consistency in coastal areas with root mean square error of 1.7 m and 90% linear error (LE90) of 0.4 m, whereas 3–4 times weaker accuracies in steep mountainous areas. A positive bias of 1–2 m for an overall LE90 measure exists in the dense vegetation and steep-slope mountainous areas. TanDEM-X DEM-based SAR interferometry deformation uncertainty simulation indicates a low or even negligible topographic error contribution of 2–4 mm in mountainous areas and less than 1 mm in coastal areas. It indicates that the TanDEM-X DEM performs better than other global DEMs overall and shows a better elevation consistence with SRTM C-band DEM in the coastal area. As an excellent source of up-to-date information, the TanDEM-X DEM are expected be an advantage for understanding dynamic land use changes and improving identification and delineation of coastal lands, mountainous landslides, and earthquakes disasters.

A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

SfM 多視点ステレオ写真測量による過去の空中写真からの 三次元地形モデルの構築

SfM 多視点ステレオ写真測量による過去の空中写真からの 三次元地形モデルの構築

Coherent Markov Random Field-Based Unreliable DSM Areas Segmentation and Hierarchical Adaptive Surface Fitting for InSAR DEM Reconstruction.

A digital elevation model (DEM) can be obtained by removing ground objects, such as buildings, in a digital surface model (DSM) generated by the interferometric synthetic aperture radar (InSAR) system. However, the imaging mechanism will cause unreliable DSM areas such as layover and shadow in the building areas, which seriously affect the elevation accuracy of the DEM generated from the DSM. Driven by above problem, this paper proposed a novel DEM reconstruction method. Coherent Markov random field (CMRF) was first used to segment unreliable DSM areas. With the help of coherence coefficients and residue information provided by the InSAR system, CMRF has shown better segmentation results than traditional traditional Markov random field (MRF) which only use fixed parameters to determine the neighborhood energy. Based on segmentation results, the hierarchical adaptive surface fitting (with gradually changing the grid size and adaptive threshold) was set up to locate the non-ground points. The adaptive surface fitting was superior to the surface fitting-based method with fixed grid size and threshold of height differences. Finally, interpolation based on an inverse distance weighted (IDW) algorithm combining coherence coefficient was performed to reconstruct a DEM. The airborne InSAR data from the Institute of Electronics, Chinese Academy of Sciences has been researched, and the experimental results show that our method can filter out buildings and identify natural terrain effectively while retaining most of the terrain features.

A framework for high-precision DEM reconstruction based on the radargrammetry technique

ABSTRACTIn recent years, with the continuous development of synthetic aperture radar (SAR) techniques, radargrammetry technique has become an important method to obtain high-precision digital elevation model (DEM). Based on radargrammetry techniques, a new processing framework is proposed in this letter. In the new framework, in respect of radiometry, a one-to-many stereo image pairs structure and a multiple image matching algorithm based on this structure are introduced, which improves the robustness and quality of the results in the matching process. In terms of geometry, a fusion algorithm of stereo pairs with different intersection angle based on the terrain relief slope threshold is proposed, which improves the accuracy of the results. Subsequently, the experiment is conducted in the Hengduan Mountains in Southwest China and demonstrate the effectiveness of this framework compared with the classical radargrammetry technique.

Contextual Information-Based Multichannel Synthetic Aperture Radar Interferometry: Addressing DEM reconstruction using contextual information

Interferometric synthetic aperture radar (InSAR) systems are capable of providing an estimate of the digital elevation model (DEM) of the imaged ground scene. This is usually done by means of a phase unwrapping (PU) operation. In the absence of additional regularity constraints, PU is an ill-posed problem, because the solution is not unique. Multichannel (MCh) techniques, using stacks of images of the same scene, can be used for restoring the solution uniqueness and reducing the effect of phase noise. Moreover, statistical techniques exploiting the contextual information contained in the data can provide satisfactory results. In this article, an overview of the main MCh statistical DEM reconstruction methods, developed both in the classical and in the Bayesian estimation framework, is presented. In particular, the effectiveness of the exploitation of contextual statistical models is shown by means of numerical experiments on simulated and real data sets.

High Accuracy DEM Reconstruction Using SAR Clinometry and Interferometry Method

In this paper, a novel method is proposed by combining of radar clinometry and interferometry to improve the accuracy of digital elevation model (DEM) reconstruction. In synthetic aperture radar (SAR) signal processing, the interferometry method uses a couple of high-coherence SAR complex images and the clinometry method uses only one. On one hand, interferometry-derived DEM is much more accurate in regions of higher coherence than the clinometry one. However, in regions of lower coherence, some pronounced errors are produced with the interferometry method due to phase filtering and unwrapping problems. On the other hand, the clinometry method can produce a more robust DEM result by using the intensity of SAR image. Therefore the clinometry-and interferometry-derived DEM is fused by introducing a user-defined weighting factor, where in regions of higher coherence, the DEM results with the interferometry method are remained, and in regions of lower coherence, the interferometry-derived DEM is updated with the clinometry-derived one. Finally, the experimental results with Envisat data show the effectiveness of our approach.

High-precision, fast DEM reconstruction method for spaceborne InSAR

The primary objective of the spaceborne interferometric synthetic aperture radar (InSAR) system is to develop a consistent global digital elevation model (DEM). The improvement of the processing speed in global interferometry missions while maintaining the accuracy is becoming an important issue. DEM reconstruction is the most time-consuming step in the data processing required for spaceborne InSAR. Based on a DEM reconstruction principle analysis, we exploited two basic characteristics of the mapping relation between the interferometric phase and 3-D position of target point. First, the relation between the 3-D position of the target point and the interferometric phase can be approximated using polynomials. Second, the corresponding polynomials change slowly in the local SAR images. Also, we verified the foundations of the two mentioned characteristics theoretically. Then, we proposed a fast method of DEM reconstruction based on this analysis. Detailed descriptions of the step and key parameters of the fast algorithm are provided. Finally, the repeat-pass interferometric data obtained using TerraSAR-X was used to test the presented method. The experimental results showed that a significant reduction in computation time is achieved with only a small loss in accuracy. The effectiveness and correctness of the proposed method were also validated.

DEM Reconstruction in Layover Areas From SAR and Auxiliary Input Data

In this letter, a methodology to overcome the layover problem and obtain the 3-D reconstruction of urban areas will be discussed. Interferometric synthetic aperture radar (SAR) (InSAR) systems allow the estimation of height profiles of the Earth surface, but in the case of urban scenarios, estimation becomes a hard task due to the presence of SAR geometrical distortions, with layover above all. First, the layover signal in InSAR images is investigated; then, a procedure to specifically manage layover areas is presented. The proposed method consists of introducing an auxiliary data exploitation, optical data or SAR shadowing, in the maximum a posteriori statistical estimation technique to improve the digital elevation model reconstruction, particularly on phase discontinuities. We test the method on simulated data, showing its effectiveness.

3D Fractal reconstruction of terrain profile data based on digital elevation model

Digital Elevation Model (DEM) often makes it difficult for terrain reconstruction and data storage due to the failure in acquisition of details with higher resolution. If original terrain of DEM can be simulated, resulting in geographical details can be represented precisely while reducing the data size, then an effective reconstruction scheme is essential. This paper adopts two sets of real-world 3D terrain profile data to proceed data reducing, i.e. data sampling randomly, then reconstruct them through 3D fractal reconstruction. Meanwhile, the quantitative and qualitative difference generated from different reduction rates were evaluated statistically. The research results show that, if 3D fractal interpolation method is applied to DEM reconstruction, the higher reduction rate can be obtained for DEM of larger data size with respect to that of smaller data size under the assumption that the entire terrain structure is still maintained.

Reconstruction of DEMs From ERS-1/2 Tandem Data in Mountainous Area Facilitated by SRTM Data

A new approach is presented in this paper to produce Digital Elevation Model (DEM) in mountainous areas with steep slope using ERS-1/2 tandem data. In order to reduce the impact of phase errors on the Interferometric Synthetic Aperture Radar (InSAR)-generated DEM, an external DEM such as that from Shuttle Radar Topography Mission (SRTM) is utilized in this approach. The proposed algorithm includes two steps: The first step is to model and remove phase trends with a linear regression analysis before converting phase to height; the second step is to filter unreliable height points before interpolating the DEM from the InSAR height map. The critical points are the following: 1) determining the one-to-one correspondence between the interferogram and the SRTM DEM before knowing the InSAR-derived elevation values and 2) estimating the elevation range of every pixel from SRTM DEM. To solve the first problem, an iteratively geocoding algorithm is performed. A DEM interpolation error model solves the second one. For InSAR data processing, the SRTM DEM is not only usable for modeling systematic phase errors but also for filtering gross height errors. The experiments in Zhangbei and the Three Gorges areas in China show that our approach has improved the accuracy of the resulting DEMs significantly without any ground control points.

The integration of JERS-1 and ERS SAR in differential interferometry for measurement of complex glacier motion

AbstractComplex glacier motion in the Grove Mountains region, Antarctica, is measured using four-pass differential synthetic aperture radar (SAR) interferometry (InSAR). The components of the motion vector field are resolved using a 44 day-separation Japanese Earth Resources Satellite-1 (JERS-1) InSAR pair and a European Remote-sensing Satellite-1/-2 (ERS-1/-2) tandem InSAR pair. The 44 day temporal baseline provides the sensitivity required to observe the range of ice motion (around 8–10ma–1), and the 1 day short baseline provides the best choice for glacier digital elevation model reconstruction. It is remarkable that the scattering field of the JERS-1 pair remained coherent over the long time interval and the interferometric fringes are clear. The overall ice flow is from east to west, downslope and towards Lambert Glacier. The regional flow is obstructed by nunataks extending north–south, with two wide gaps. Two narrow glaciers flow past the nunataks and rejoin each other at the downstream end. Regional morphology, and the resolved flow in the Grove Mountains area, suggests that ice flow is channeled throughout this eastern flank of Lambert Glacier.

Recovering radar shadow to improve interferometric phase unwrapping and dem reconstruction

This letter presents a new approach to actively use radar shadow in order to unwrap and geocode objects that cannot be reconstructed by synthetic aperture radar interferometry alone. This new algorithm may significantly improve phase unwrapping and digital elevation model reconstruction in mountainous topography or in urban areas. The algorithm is explained and examples are given with data from the Shuttle Radar Topography Mission, where shadow is a serious problem in Alpine terrain.