LiDAR DSM Research Articles

Building extraction in urban and rural areas with aerial and LiDAR DSM

Building extraction in urban and rural areas with aerial and LiDAR DSM

Semantic object-based urban scene analysis for feature fusion of VHR imagery and Lidar DSM

Semantic object-based urban scene analysis for feature fusion of VHR imagery and Lidar DSM

Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM

On 8th August 2017, a catastrophic Ms. 7.0 earthquake with a focal depth of 20 km struck the Jiuzhaigou County in Sichuan Province, China. It exerted a strong influence on the slope stability within the surrounding areas and triggered numerous secondary geohazards including rockfalls and other co-seismic landslides, which incurred drastic surface changes, and thus can be easily identified from cloud-free high-resolution optical imagery. Most of such landslides became stabilized shortly after the earthquake while others moving very slowly for years. In contrast, some slopes were destabilized without significant surface change into slow-moving landslides, which may pose long-term potential threats to people's life and property. Therefore, it is crucial to accurately identify these slow-moving landslides and regularly monitor their post-seismic activity. In this study, we employed the synthetic aperture radar interferometry (InSAR) techniques to detect and monitor slow-moving landslides after the earthquake in the Jiuzhaigou area, and analyzed the impacts of the earthquake on these landslides through integration of multi-source data (InSAR, Lidar, optical image, and field survey). As a result, 16 slow-moving landslides were detected by InSAR in the Jiuzhaigou area, including several historical landslides. The results of time-series InSAR analyses enabled identification of three kinds of landslide evolution modes affected by the earthquake, i.e. acceleration of deformation of pre-existing landslides, reactivation of dormant landslide, and remobilization of earthquake-triggered landslide. Each mode is supported by detailed analyses of multi-source data. The results demonstrated that satellite InSAR combined with high-resolution Lidar and optical data can provide a cost-effective approach of post-earthquake geohazards detection and monitoring.

Applicability of the global TanDEM-X elevation data for terrain modelling of a forested karst area: a case study from Slovak Karst

New interferometric radar data of the TanDEM-X space mission have become recently available as a global digital elevation model providing 0.4 arc second spatial resolution (ca. 12 meters). The TanDEM-X dataset brings new options into geoscientific research across multiple scales. However, the accuracy and suitability of this data have not been evaluated in such an extensive manner as, for example, the widely used SRTM data which resolution is 1 arc second (ca. 30 m). We present a validation of the vertical accuracy of TanDEM-X DEM product and an evaluation of its suitability for landform classification in a forested karst area. The DEM segmentation using geomorphons was used for the automated object-based landform classification. We focused on the identification of dolines for which polygons of dolines mapped by an expert-driven approach were used for validation. Airborne lidar data in the form of DSM and DTM were used as the reference dataset for validation of the TanDEM-X DEM vertical accuracy. The results from the study area show that the vertical RMSE of the TanDEM-X data is 3.42 m with respect to the lidar DSM and 9.64 m in comparison with lidar DTM. The identification of dolines by the geomorphon approach achieved 73 % with TanDEM-X, lower than for the lidar DTM (85 %). The TanDEM-X elevation errors were strongly correlated with the canopy height derived from the lidar data suggesting limited suitability of the TanDEM-X data for mapping fine-scale geomorphological features under forests while there was a good match with the lidar DTM terrain in open areas. URL: https://www.gcass.science.upjs.sk/

Phase unmixing of TerraSAR-X staring spotlight interferograms in building scale for PS height and deformation

Staring spotlight images with a spatial resolution of up to tens of centimeters are good data sources for urban applications including displacement mapping. However, phase discontinuities, layover, and shadowing effect are also associated with staring spotlight interferograms, adding to the difficulties in height estimation and spatial phase unwrapping. The scattering mechanism of the staring spotlight images in the urban environment is complicated, thus it is difficult to simulate and remove the reference height of staring spotlight interferograms directly. In addition, global spatial phase unwrapping networks tend to smooth phase discontinuities. With the aim of implementing height estimation and phase unwrapping for TerraSAR-X Staring Spotlight interferograms, a workflow for phase unmixing of TerraSAR-X staring spotlight interferograms is proposed in this paper. The PS height is estimated in the baseline domain rather than the spatial domain. Taking into account the length and height change of each connection, the spatial phase unwrapping network is adjusted and segmented into isolated networks. The connected components of the adjusted spatial phase unwrapping network can be identified using graph theory. Spatial phase unwrapping is implemented in individual networks. The unfolded height phase is separated from the unwrapped phase, and the remaining phase is deformation dominated. Compared with the traditional global spatial phase unwrapping method, this study demonstrates the feasibility of the proposed least squares parameter search and graph partition based workflow in urban area, for phase unmixing of TerraSAR-X staring spotlight interferograms in building scale for PS height and deformation, as evidenced by external LiDAR DSM and temperature records.

Correction: Yan, Y.; et al. Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images. Sensors 2017, 17, 1957.

The authors wish to make the following corrections to this paper [1]:[...].

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

Abstract. 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.

Forest Canopy Height Estimation Using Multiplatform Remote Sensing Dataset

Recently, numerous studies have attempted to determine forest height using remote sensing techniques that not only have the benefits of fast data acquisition, processing, and analysis but are also cost-effective. However, if there was insufficient data to apply the latest remote sensing techniques, we need to consider many kinds of datasets as possible. In this study, we tried to determine forest height using discrete-return LiDAR data, SRTM, satellite L-band SAR data, and Optical data. We experimented with the differences between LiDAR DSM and DTM, as well as SRTM DSM and LiDAR DTM. In addition, we applied an SBAS algorithm and linear regression to the dataset. From the quantitative evaluation, the RMSE and R2 of the LiDAR-derived forest height (3.22 m and 0.43, resp.) and the SRTM-derived forest height (2.90 m and 0.50, resp.) were both reasonably good, especially when we consider data acquisition time differences and measurement errors in mountainous areas. Moreover, we slightly improved the RMSE and R2 from 2.90 m and 0.50, respectively, to 2.75 m and 0.54, respectively, by correcting the SRTM using the SBAS algorithm. Furthermore, we merged the datasets using linear regression and obtained improved forest heights with RMSE and R2 values of 2.68 m and 0.56, respectively. To generate a forest height map, we used NDVI from Optical imagery and masked heights below 2 m from each sensor. Thus, we excluded urban areas, “bare earth surfaces,” and mountain streams from each sensor’s imagery. Finally, we generated a forest height map by overlapping the datasets. The results of this study indicate that each sensor has the potential for not only determining forest height but also extracting complementary forest area information. Furthermore, this study demonstrates the potential for improvement using the SBAS algorithm and linear regression.

ALOS-1 PALSAR 영상을 이용한 대전지역 변위 관측 - PSInSAR와 SqueeSAR 분석 결과 비교 -

SqueeSAR 분석기법은 SAR 영상내에 있는 고정산란체(PS)와 분산산란체(DS)를 모두 이용하는 새로운 기법이다. 비록 도심지역에는 많은 PS가 존재하지만, SqueeSAR 기법은 관측밀도를 높이는데 기여할 수 있다. 차분간섭도 제작에 필요한 DEM 정확도에 의한 변위분석 결과의 영향을 분석하기 위해 SRTM 1-arc (~30 m)와 1 m LIDAR DEM을 사용한 분석을 수행하였다. 두개의 고도자료를 이용하여 PSInSAR와 SqueeSAR 분석을 수행한 결과 인공구조물과 같은 PS에서는 자료처리에 사용된 DEM 오차를 거의 정확하게 보정할 수 있기 때문에, 사용된 DEM의 정확도와 상관없이 최종 시계열 분석 결과는 동일하였다. 반면, 고정산란체가 아닌 Distributed Scatterer (DS)일 경우 사용된 DEM의 정확도에 따라 영향을 받게 되며, SqueeSAR의 경우 사용된 DEM이 정확할수록 분석 결과가 좋아짐을 확인하였다. 도심지역에서의 변위 관측에서도 SqueeSAR 기법이 PSInSAR 기법에 비해 약 5배 이상의 관측점을 추출하는데 기여했으며, 관측점의 오차도 PSInSAR 결과에 비해 현저하게 개선되었다. SqueeSAR is a new technique to combine Persistent Scatterer (PS) and Distributed Scatterer (DS) for deformation monitoring. Although many PSs are available in urban areas, SqueeSAR analysis can be beneficial to increase the PS density in not only natural targets but also smooth surfaces in urban environment. The height of each targets is generally required to remove topographic phase in interferometric SAR processing. The result of PSInSAR analysis to use PS only is not affected by DEM resolution because the height error of initial input DEM at each PSs is precisely compensated in PS processing chain. On the contrary, SqueeSAR can be affected by DEM resolution and precision since it includes spatial average filtering for DS targets to increase a signal-to-noise ratio (SNR). In this study we observe the effect of DEM resolution on deformation measurement by PSInSAR and SqueeSAR. With ALOS-1 PALSAR L-band data, acquired over Daejeon city, Korea, two different DEM data are used in InSAR processing for comparison: 1 m LIDAR DEM and SRTM 1-arc (~30 m) DEM. As expected the results of PSInSAR analysis show almost same results independently of the kind of DEM, while the results of SqueeSAR analysis show the improvement in quality of the time-series in case of 1-m LIDAR DSM. The density of InSAR measurement points was also improved about five times more than the PSInSAR analysis.

An intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs)

AbstractSpatial modeling methods usually use pixels and image objects as fundamental processing units to address real‐world objects, geo‐objects, in image space. To do this, both pixel‐based and object‐based approaches typically employ a linear two‐staged workflow of segmentation and classification. Pixel‐based methods segment a classified image to address geo‐objects in image space. In contrast, object‐based approaches classify a segmented image to identify geo‐objects from raster datasets. These methods lack the ability to simultaneously integrate the geometry and theme of geo‐objects in image space. This article explores Geographical Vector Agents (GVAs) as an automated and intelligent processing unit to directly address real‐world objects in the process of remote sensing image classification. The GVA is a distinct type of geographic automata characterized by elastic geometry, dynamic internal structure, neighborhoods and their respective rules. We test this concept by modeling a set of objects on a subset IKONOS image and LiDAR DSM datasets without the setting parameters (e.g. scale, shape information), usually applied in conventional Geographic Object‐Based Image Analysis (GEOBIA) approaches. The results show that the GVA approach achieves more than 3.5% improvement for correctness, 2% improvement for quality, although no significant improvement for completeness to GEOBIA, thus demonstrating the competitive performance of GVAs classification.

Calculation of Enclosure Index for Assessing Urban Landscapes Using Digital Surface Models

Local governments in Japan need to assess landscape quality from the perspective of aesthetics before approving new construction. However, ground surveys are time-consuming and expensive to perform over wide areas. This paper proposes a methodology using a digital surface model (DSM) to estimate a local landscape referred to as the enclosure index, in urban areas. The inde xi s defined as the ratio of occluded area to the entire field of view. The index can be applied to local landscape assessment from the viewpoint of a human, and it is expected to allow for estimating the landscape quality over a wide area at low cost. The index is defined in two different projections, and two types of DSMs for calculating the index were investigated: DSM from airborne LiDAR (LiDAR-DSM) and DSM generated from stereoscopic aerial images (Image-DSM). Enclosure index (occlusion) maps were generated, and the indices were calculated in the study area of Higashiyama, Kyoto, Japan. As a result of validation based on ground truth data, it was found that the index from LiDAR-DSM was more accurate than that from Image-DSM because the height accuracy of LiDAR-DSM was higher than that of Image-DSM, especially for narrow streets, where three-dimensional coordinates are difficult to restore by using aerial images. Even though last-pulse mode LiDAR data may yield underestimated DSM, it was found that the proposed methodology is effective for estimating the index over a wide area at lo wc ost.

Impact of Orthorectification on Simulated Compact Polarimetric RCM Data With Accurate Lidar DSM

Orthorectification using digital terrain models is a key issue for polarimetric complex synthetic aperture radar (SAR) data because resampling the complex data can corrupt the polarimetric phase, mainly in terrain with relief. Orthorectification will be also a sensitive issue with the compact polarimetric data of the future Canadian Radar Constellation Mission (RCM) sensor to be launched in 2018. Two orthorectification methods for the complex SAR data are thus proposed and compared: performing polarimetric processing in the image space before the geometric processing or in the ground space after the geometric processing. RCM compact polarimetric data using the requirements of the very high resolution (VHR) mode were simulated at the Canada Centre for Mapping and Earth Observation from fine-quad Radarsat-2 data acquired with different look angles over a hilly relief study site. Quantitative evaluations between the two methods, using a basis-invariant parameter (the entropy), were thus performed to evaluate the impact of orthorectification on the simulated VHR RCM data. To avoid the propagation of elevation errors into the final error budget, an accurate lidar digital surface model was used in the orthorectification. The results demonstrated that the oversampling and the noise floor that is used to generate the simulated VHR RCM data are the main factors, which corrupted the simulated VHR RCM data during its orthorectification with the ground-space method.

Orthorectification of Full-Polarimetric Radarsat-2 Data Using Accurate LIDAR DSM

Orthorectification using digital terrain models is a key issue for full-polarimetric complex SAR data because resampling the complex data can corrupt the polarimetric phase, mainly in terrain with relief. This research thus compared two methods for the orthorectification of the complex SAR data: The polarimetric processing is performed before (image-space method) or after (ground-space method) the geometric processing. Radarsat-2 fine-quad data acquired with different look angles over a hilly relief study site were orthorectified using accurate light detection and ranging digital surface model. Quantitative evaluations between the two methods as a function of different geometric and radiometric parameters were thus performed to evaluate the impact during orthorectification. The results demonstrated that the look angles and the terrain slopes can potentially corrupt the polarimetric complex SAR data during its orthorectification with the ground-space method. In addition, general advice is provided to reduce these impacts to an acceptable level for the users and their polarimetric applications.

Automatic registration of optical imagery with 3d lidar data using local combined mutual information

Abstract. Automatic registration of multi-sensor data is a basic step in data fusion for photogrammetric and remote sensing applications. The effectiveness of intensity-based methods such as Mutual Information (MI) for automated registration of multi-sensor image has been previously reported for medical and remote sensing applications. In this paper, a new multivariable MI approach that exploits complementary information of inherently registered LiDAR DSM and intensity data to improve the robustness of registering optical imagery and LiDAR point cloud, is presented. LiDAR DSM and intensity information has been utilised in measuring the similarity of LiDAR and optical imagery via the Combined MI. An effective histogramming technique is adopted to facilitate estimation of a 3D probability density function (pdf). In addition, a local similarity measure is introduced to decrease the complexity of optimisation at higher dimensions and computation cost. Therefore, the reliability of registration is improved due to the use of redundant observations of similarity. The performance of the proposed method for registration of satellite and aerial images with LiDAR data in urban and rural areas is experimentally evaluated and the results obtained are discussed.

고해상도 SAR 영상을 이용한 도심지 건물 재구성

The monitoring of urban area, target detection and building reconstruction have been actively studied and investigated since high resolution X-band SAR images could be acquired by airborne and/or satellite SAR systems. This paper describes an efficient approach to reconstruct artificial structures (e.g. apartment, building and house) in urban area using high resolution X-band SAR images. Building footprint was first extracted from 1:25,000 digital topographic map and then a corner line of building was detected by an automatic detecting algorithm. With SAR amplitude images, an initial building height was calculated by the length of layover estimated using KS-test (Kolmogorov-Smirnov test) from the corner line. The interferometric SAR phases were simulated depending on SAR geometry and changable building heights ranging from -10 m to +10 m of the initial building height. With an interferogram from real SAR data set, the simulation results were compared using the method of the phase consistency. One of results can be finally defined as the reconstructed building height. The developed algorithm was applied to repeatpass TerraSAR-X spotlight mode data set over an apartment complex in Daejeon city, Korea. The final building heights were validated against reference heights extracted from LiDAR DSM, with an RMSE (Root Mean Square Error) of about 1~2m.

航空レーザ測量のDSMで抽出された地すべり危険斜面の現地検証

航空レーザ測量のDSMで抽出された地すべり危険斜面の現地検証

SEMI-AUTOMATIC CO-REGISTRATION OF PHOTOGRAMMETRIC AND LIDAR DATA USING BUILDINGS

Abstract. In this work, the co-registration steps between LiDAR and photogrammetric DSM 3Ddata are analyzed and a solution based on automated plane matching is proposed and implemented. For a robust 3D geometric transformation both planes and points are used. Initially planes are chosen as the co-registration primitives. To confine the search space for the plane matching a sequential automatic building matching is performed first. For matching buildings from the LiDAR and the photogrammetric data, a similarity objective function is formed based on the roof height difference (RHD), the 3D histogram of the building attributes, and the building boundary area of a building. A region growing algorithm based on a Triangulated Irregular Network (TIN) is implemented to extract planes from both datasets. Next, an automatic successive process for identifying and matching corresponding planes from the two datasets has been developed and implemented. It is based on the building boundary region and determines plane pairs through a robust matching process thus eliminating outlier pairs. The selected correct plane pairs are the input data for the geometric transformation process. The 3D conformal transformation method in conjunction with the attitude quaternion is applied to obtain the transformation parameters using the normal vectors of the corresponding plane pairs. Following the mapping of one dataset onto the coordinate system of the other, the Iterative Closest Point (ICP) algorithm is then applied, using the corresponding building point clouds to further refine the transformation solution. The results indicate that the combination of planes and points improve the co-registration outcomes.

Dual-scale validation of a medium-resolution coastal DEM with terrestrial LiDAR DSM and GPS

The use of medium-resolution photogrammetric-derived Digital Elevation Models (DEMs) to model coastal inundation risk is commonplace in the geosciences. However, these datasets are often characterised by relatively large and loosely defined elevation errors, which can seriously limit their reliability. Post-processed and static RTK dual-frequency GPS data and very high-resolution Terrestrial Laser Scanning DSM data are used here to quantify the magnitude and spatial distribution of elevation error on a 10 km coastal section of a medium-resolution photogrammetric DEM. The validation data are captured at two scales and spatial-resolutions to minimise the risk of spatial bias in the validation results. The strengths and shortcomings of each validation dataset are assessed, and the complimentary value of GPS and Terrestrial Laser Scanning for external validation is demonstrated. Elevation errors highlighted in the photogrammetric DEM are found to be significantly larger than suggested by the data suppliers, with a tendency for the larger errors to occur with increasing proximity to the coastline. The results confirm the unsuitability of the DEM tested for the local spatial modelling of coastal inundation risk, highlighting difficulties that may be prone to occur when similar DEM datasets are used in coastal studies elsewhere.

Semi-Automatic Objects Recognition in Urban Areas Based on Fuzzy Logic

Three dimensional object extraction and recognition (OER) from geographic data has been definitely one of more important topic in photogrammetry for quite a long time. Today, the capability of rapid generating high-density DSM increases the supply of geographic information but the discrete nature of the measuring makes more difficult to recognize correctly and to extract 3D objects from these surface. The proposed methodology wants to semi-automate some geographic objects clustering operations, in order to perform the recognition process. The clustering is a subjective process; the same set of data items often needs to be partitioned differently based on the application. Fuzzy logic gives the possibility to use in a mathematical process the uncertain information typical of human reasoning. The concept at the base of our proposal is to use the information contained in Image Matching or LiDAR DSM, and typically understood by the human operator, in a fuzzy recognition process able to combine the different input in order to perform the classification. So the object recognition approach proposed in our workflow integrates 3D structural descriptive components of objects, extracted from DSM, into a fuzzy reasoning process in order to exploit more fully all available information, which can contribute to the extraction and recognition process and, to handling the object’s vagueness. The recognition algorithm has been tested with to different data set and different objectives. An important issue is to apply the typical human process which allows to recognize objects in a range image in a fuzzy reasoning process. The investigations presented here have given a first demonstration of the capability of this approach.

Spectral filtering as a method of visualising and removing striped artefacts in digital elevation data

AbstractSpectral filtering was compared with traditional mean spatial filters to assess their ability to identify and remove striped artefacts in digital elevation data. The techniques were applied to two datasets: a 100 m contour derived digital elevation model (DEM) of southern Norway and a 2 m LiDAR DSM of the Lake District, UK. Both datasets contained diagonal data artefacts that were found to propagate into subsequent terrain analysis. Spectral filtering used fast Fourier transformation (FFT) frequency data to identify these data artefacts in both datasets. These were removed from the data by applying a cut filter, prior to the inverse transform. Spectral filtering showed considerable advantages over mean spatial filters, when both the absolute and spatial distribution of elevation changes made were examined. Elevation changes from the spectral filtering were restricted to frequencies removed by the cut filter, were small in magnitude and consequently avoided any global smoothing. Spectral filtering was found to avoid the smoothing of kernel based data editing, and provided a more informative measure of data artefacts present in the FFT frequency domain. Artefacts were found to be heterogeneous through the surfaces, a result of their strong correlations with spatially autocorrelated variables: landcover and landsurface geometry. Spectral filtering performed better on the 100 m DEM, where signal and artefact were clearly distinguishable in the frequency data. Spectrally filtered digital elevation datasets were found to provide a superior and more precise representation of the landsurface and be a more appropriate dataset for any subsequent geomorphological applications. Copyright © 2007 John Wiley & Sons, Ltd.