Digital Terrain Model Extraction Research Articles

Exploitation of the Number of Return Echoes for DTM Extraction from Point Clouds Acquired by LiDAR UAS DJI Zenmuse L1

Abstract. Following the enormous technological developments of LiDAR (Light Detection And Ranging) sensors, it is currently easier to find them commercially in the UA (Uncrewed Aerial Systems) sector. In particular, with the Zenmuse L1 by DJI (Dà-Jiāng Innovations) the market has grown globally, mainly due to the compactness of the product that is easily compatible with UAS. The L1 sensor can record up to three returns of the emanating signal, so it can acquire a larger amount of points, such as those below the vegetation. Therefore, in addition to the geometric information of the points, the Zenmuse L1 point clouds also provide other information, such as the number of echo returns from 1 to 3. This data could be exploited to improve the automatic extraction of the digital terrain model (DTM) from the point clouds, hopefully leading to the avoidance of manual correction. This research aims to focus on evaluating whether the addition of the return number feature can affect the identification of the ground points through different computational methods and can improve the time efficiency of state-of-the-art algorithms.

Forest Height Estimation Approach Combining P-Band and X-Band Interferometric SAR Data

Forest height is an essential parameter used to derive important information about forest ecosystems, such as forest above-ground biomass. In this article, a forest height estimation approach combining P-band and X-band interferometric synthetic aperture radar (InSAR) was introduced. The forest height was estimated using the difference in the penetration of long- and short-wavelength radars to the forest. That is, the P-band and X-band InSAR data were used to extract the digital terrain model (DTM) and digital surface model (DSM), respectively. For the DTM, an improved time-frequency (TF) analysis method was used to reduce the effect of forest scatterers on the extraction of a pure understory terrain phase based on P-band InSAR. For the DSM, a novel compensation algorithm based on a multi-layer model (MLM) was proposed to remove the penetration bias of the X-band. Compared to the existing method based on the infinitely deep uniform volumes (IDUV) model, the MLM-based method is more in line with the characteristics of forest structure and the scattering mechanism for X-band InSAR. The airborne P-band repeat-pass InSAR and spaceborne X-band (TanDEM-X) single-pass InSAR data were used to verify the proposed method over the study area in the Saihanba Forest Farm in Hebei, China. The results demonstrated that the improved TF method can achieve high-precision DTM extraction based on P-band InSAR data, and the root mean square error (RMSE) was 0.94 m. The proposed MLM-based compensation method of the DSM achieved a smaller error (RMSE: 1.67 m) compared to the IDUV-based method (RMSE: 3.01 m). Under the same DTM extracted by P-band InSAR, the estimation accuracy of forest height based on the MLM method was 86.58% (RMSE: 1.81 m), which was 8.49% higher than that of the IDUV-based method (RMSE: 2.98 m).

DTM extraction from DSM using a multi-scale DTM fusion strategy based on deep learning

Extraction of digital terrain model (DTM) from Digital Surface Model (DSM) still faces many problems in a complex scene with geometric ambiguities such as steep slope forested environments and contiguous non-ground regions. In this paper, an approach based on deep learning is proposed to generate DTM directly from DSM without applying filtering methods for eliminating non-ground pixels. In this regard, first, in the preprocessing step, the data is prepared for entering into the proposed deep network. Then, a hybrid deep convolutional neural network (HDCNN) is proposed which is a combination of the U-net architecture and residual networks. In this network, effective features are generate4d in different scales during the downsampling process from the input DSM and the DTM is extracted during the upsampling process. To rectify the results, a multi-scale fusion strategy is proposed to produce the final DTM by fusing the generated DTMs at different scales and with different spatial shifts. The performance of the proposed approach is analyzed by implementing four different evaluation scenarios in five different datasets. The evaluation results demonstrated significant performance and high generalizability of the proposed approach. The proposed network also outperforms the deep learning-based filtering methods and two reference DTM extraction algorithms especially in challenging regions.

High-Resolution Terrain Modeling Using Airborne LiDAR Data with Transfer Learning

This study presents a novel workflow for automated Digital Terrain Model (DTM) extraction from Airborne LiDAR point clouds based on a convolutional neural network (CNN), considering a transfer learning approach. The workflow consists of three parts: feature image generation, transfer learning using ResNet, and interpolation. First, each point is transformed into a featured image based on its elevation differences with neighboring points. Then, the feature images are classified into ground and non-ground using ImageNet pretrained ResNet models. The ground points are extracted by remapping each feature image to its corresponding points. Last, the extracted ground points are interpolated to generate a continuous elevation surface. We compared the proposed workflow with two traditional filters, namely the Progressive Morphological Filter (PMF) and the Progressive Triangulated Irregular Network Densification (PTD). Our results show that the proposed workflow establishes an advantageous DTM extraction accuracy with yields of only 0.52%, 4.84%, and 2.43% for Type I, Type II, and the total error, respectively. In comparison, Type I, Type II, and the total error for PMF are 7.82%, 11.60%, and 9.48% and for PTD 1.55%, 5.37%, and 3.22%, respectively. The root means square error (RMSE) for the 1 m resolution interpolated DTM is only 7.3 cm. Moreover, we conducted a qualitative analysis to investigate the reliability and limitations of the proposed workflow.

Photogrammetric surveying forests and woodlands with UAVs: techniques for automatic removal of vegetation and digital terrain model production for hydrological applications

The purpose of this study is the photogrammetric survey of a forested area using unmanned aerial vehicles (UAV), and the estimation of the digital terrain model (DTM) of the area, based on the photogrammetrically produced digital surface model (DSM). Furthermore, through the classification of the height difference between a DSM and a DTM, a vegetation height model is estimated, and a vegetation type map is produced. Finally, the generated DTM was used in a hydrological analysis study to determine its suitability compared to the usage of the DSM. The selected study area was the forest of Seih-Sou (Thessaloniki). The DTM extraction methodology applies classification and filtering of point clouds, and aims to produce a surface model including only terrain points (DTM). The method yielded a DTM that functioned satisfactorily as a basis for the hydrological analysis. Also, by classifying the DSM–DTM difference, a vegetation height model was generated. For the photogrammetric survey, 495 aerial images were used, taken by a UAV from a height of ∼200 m. A total of 44 ground control points were measured with an accuracy of 5 cm. The accuracy of the aerial triangulation was approximately 13 cm. The produced dense point cloud, counted 146 593 725 points.

DTM extraction under forest canopy using LiDAR data and a modified invasive weed optimization algorithm

The penetration ability of Light Detection and Ranging (LiDAR) pulses into vegetation cover makes it a valuable tool in forest inventory. Extraction of a Digital Terrain Model (DTM) using LiDAR data is a challenging topic, especially in steep and complex terrains with forest canopy. This paper presents some approaches for ground filtering and interpolating the point cloud to generate DTM in forested terrains. Interpolating the points in dense forests that have high attitude variation is very difficult and make the popular interpolation methods unsatisfactory. This paper proposed a modified Invasive Weed Optimization (IWO) method for finding the optimized coefficients of the polynomial interpolation method. This method had a good performance with 0.210 mm RMSE value in the forested terrains. The interpolated point cloud was used as the input of the proposed ground filtering method for detecting the non-ground pixels. The proposed ground filtering method was structured with two main sections including, iterative geodesic morphology and scan labeling. In the iterative geodesic morphology, some geometric and structural parameters were introduced to investigate the quality of extracted points in each iteration. The scan labeling searched the data pixel by pixel in four directions and labeled the pixels with the high slope value in all directions. The non-ground pixels were obtained by integrating the result of iterative geodesic and scan labeling. Assessment of the ground filtering results using the International Society for Photogrammetry and Remote Sensing (ISPRS) showed 3.92% total error compared to the other reported algorithms. This demonstrated the ability of the proposed approach in recognition of the non-ground pixels. The extracted pixels were removed and the DTM was generated by filling the gaps using the proposed IWO and polynomial interpolation. Some forested regions with various characteristics such as sparse and dense trees on the hills and steep slopes were utilized to evaluate the accuracy of the generated DTM. The computed RMSE in the test areas was 0.463 m, on average, which was acceptable for the complex and forested terrains.

A deep learning approach to DTM extraction from imagery using rule-based training labels

Existing algorithms for Digital Terrain Model (DTM) extraction still face difficulties due to data outliers and geometric ambiguities in the scene such as contiguous off-ground areas or sloped environments. We postulate that in such challenging cases, the radiometric information contained in aerial imagery may be leveraged to distinguish between ground and off-ground objects. We propose a method for DTM extraction from imagery which first applies morphological filters to the Digital Surface Model to obtain candidate ground and off-ground training samples. These samples are used to train a Fully Convolutional Network (FCN) in the second step, which can then be used to identify ground samples for the entire dataset. The proposed method harnesses the power of state-of-the-art deep learning methods, while showing how they can be adapted to the application of DTM extraction by (i) automatically selecting and labelling dataset-specific samples which can be used to train the network, and (ii) adapting the network architecture to consider a larger surface area without unnecessarily increasing the computational burden. The method is successfully tested on four datasets, indicating that the automatic labelling strategy can achieve an accuracy which is comparable to the use of manually labelled training samples. Furthermore, we demonstrate that the proposed method outperforms two reference DTM extraction algorithms in challenging areas.

NEW DTM EXTRACTION APPROACH FROM AIRBORNE IMAGES DERIVED DSM

Abstract. In this work, a new filtering approach is proposed for a fully automatic Digital Terrain Model (DTM) extraction from very high resolution airborne images derived Digital Surface Models (DSMs). Our approach represents an enhancement of the existing DTM extraction algorithm Multi-directional and Slope Dependent (MSD) by proposing parameters that are more reliable for the selection of ground pixels and the pixelwise classification. To achieve this, four main steps are implemented: Firstly, 8 well-distributed scanlines are used to search for minima as a ground point within a pre-defined filtering window size. These selected ground points are stored with their positions on a 2D surface to create a network of ground points. Then, an initial DTM is created using an interpolation method to fill the gaps in the 2D surface. Afterwards, a pixel to pixel comparison between the initial DTM and the original DSM is performed utilising pixelwise classification of ground and non-ground pixels by applying a vertical height threshold. Finally, the pixels classified as non-ground are removed and the remaining holes are filled. The approach is evaluated using the Vaihingen benchmark dataset provided by the ISPRS working group III/4. The evaluation includes the comparison of our approach, denoted as Network of Ground Points (NGPs) algorithm, with the DTM created based on MSD as well as a reference DTM generated from LiDAR data. The results show that our proposed approach over performs the MSD approach.

Extraction of digital terrain model based on regular mesh generation in mountainous areas

Airborne LiDAR technology is a popular technique to quickly acquire high-precision information of the ground and the objects above it. Moreover, filtering the data is one of the necessary core processing steps. In recent years, many algorithms have been derived from traditional filtering algorithms. However, when applied to LiDAR data obtained from mountainous regions, most algorithms generate numerous omissions and errors when attempting to retain steep terrain features and filter vegetation information. This paper aims to quickly and accurately extract the digital terrain model (DTM) in mountainous regions. To accomplish this goal, a filtering algorithm based on a regular mesh generation strategy is proposed. The proposed algorithm initially divides the huge amount of point cloud data into strips and selects the appropriate spacing to subdivide each strip into equidistant grid data. The grid data is used as the input to an iterative polynomial fitting process, after which the point cloud is classified based on a controlled threshold. The experimental results show that the proposed method can quickly and efficiently classify data of mountains with different characteristics while retaining terrain feature information better than other algorithms. The average accuracy of recognition is greater than 92%. In addition, this algorithm also applies to mountains with lush vegetation.

Deep-Learning-Based Classification for DTM Extraction from ALS Point Cloud

Airborne laser scanning (ALS) point cloud data are suitable for digital terrain model (DTM) extraction given its high accuracy in elevation. Existing filtering algorithms that eliminate non-ground points mostly depend on terrain feature assumptions or representations; these assumptions result in errors when the scene is complex. This paper proposes a new method for ground point extraction based on deep learning using deep convolutional neural networks (CNN). For every point with spatial context, the neighboring points within a window are extracted and transformed into an image. Then, the classification of a point can be treated as the classification of an image; the point-to-image transformation is carefully crafted by considering the height information in the neighborhood area. After being trained on approximately 17 million labeled ALS points, the deep CNN model can learn how a human operator recognizes a point as a ground point or not. The model performs better than typical existing algorithms in terms of error rate, indicating the significant potential of deep-learning-based methods in feature extraction from a point cloud.

DTM generation in forest regions from satellite stereo imagery

Abstract. Satellite stereo imagery is becoming a popular data source for derivation of height information. Many new Digital Surface Model (DSM) generation and evaluation methods have been proposed based on these data. A novel Digital Terrain Model (DTM) extraction method based on the DSM from satellite stereo imagery is proposed in this paper. Instead of directly filtering the DSM, firstly a single channel based classification method is proposed. In this step, no multi-spectral information is used, because for some stereo sensors, like Cartosat-1, only panchromatic channels are available. The proposed classification method adopts the random forests method to get initial probability maps of the four main classes in forest regions (high-forest, low-forest, ground, and buildings). To cover the pepper and salt effect of this pixel based classification method, the probability maps are further filtered based on the adaptive Wiener filtering. Then a cube-based greedy strategy is applied in generating the final classification map from these refined probability maps. Secondly, the height distances between neighboring regions are calculated along the boundary regions. These height distances can be used to estimate the relative region heights. Thirdly, the DTM is extracted by subtracting these relative region heights from the DSM in the order of: buildings – low forest – high forest. In the end, the extracted DTM is further smoothed using median filter. The proposed DTM extraction method is finally tested on satellite stereo imagery captured by Cartosat-1. Quality evaluation is performed by comparing the extracted DTMs to a reference DTM, which is generated from the last return airborne laser scanning point cloud.

PHOTOGRAMMETRIC PERFORMANCE OF AN ULTRA LIGHT WEIGHT SWINGLET "UAV"

Abstract. Low cost mapping using UAV technology is becoming a trendy topic. Many systems exist where a simple camera can be deployed to take images, generally georeferenced with a GPS chip and MEMS attitude sensors. The step from using those images as information picture to photogrammetric products with geo-reference, such as digital terrain model (DTM) or orthophotos is not so big. New development in the field of image correlation allow matching rapidly and accurately images together, build a relative orientation of an image block, extract a DTM and produce orthoimage through a web server.The following paper focuses on the photogrammetric performance of an ultra light UAV equipped with a compact 12Mpix camera combined with online data processes provided by Pix4D. First, the step of image orientation is studied with the camera calibration step, thus the DTM extraction will be compared with conventional results from conventional photogrammetric software, new generation technique of pixel correlation and with reference data issued from high density laser scanning. The quality of orthoimage is presented in terms of quality and geometric accuracy.