scholarly journals Building extraction from satellite imagery using a digital surface model

2018 ◽  
Author(s):  
A V Dunaeva ◽  
◽  
F A Kornilov ◽  
◽  
Keyword(s):  
Satellite Imagery ◽  
Surface Model ◽  
Digital Surface Model ◽  
Building Extraction

scholarly journals APPLICATION OF DSM IN OBSTACLE CLEARANCE SURVEYING OF AERODROME

2016 ◽  
Vol XLI-B2 ◽  
pp. 227-233 ◽  
Author(s):  
X. Qiao ◽  
S. H. Lv ◽  
L. L. Li ◽  
X. J. Zhou ◽  
H. Y. Wang ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Satellite Imagery ◽  
Primary Objective ◽  
Considerable Improvement ◽  
Surface Model ◽  
Digital Surface Model ◽  
Horizontal Position ◽  
Obstacle Clearance ◽  
Digital Elevation ◽  
Elevation Model

Compared to the wide use of digital elevation model (DEM), digital surface model (DSM) receives less attention because that it is composed by not only terrain surface, but also vegetations and man-made objects which are usually regarded as useless information. Nevertheless, these objects are useful for the identification of obstacles around an aerodrome. The primary objective of the study was to determine the applicability of DSM in obstacle clearance surveying of aerodrome. According to the requirements of obstacle clearance surveying at QT airport, aerial and satellite imagery were used to generate DSM, by means of photogrammetry, which was spatially analyzed with the hypothetical 3D obstacle limitation surfaces (OLS) to identify the potential obstacles. Field surveying was then carried out to retrieve the accurate horizontal position and height of the obstacles. The results proved that the application of DSM could make considerable improvement in the efficiency of obstacle clearance surveying of aerodrome.

scholarly journals APPLICATION OF DSM IN OBSTACLE CLEARANCE SURVEYING OF AERODROME

2016 ◽  
Vol XLI-B2 ◽  
pp. 227-233 ◽  
Author(s):  
X. Qiao ◽  
S. H. Lv ◽  
L. L. Li ◽  
X. J. Zhou ◽  
H. Y. Wang ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Satellite Imagery ◽  
Primary Objective ◽  
Considerable Improvement ◽  
Surface Model ◽  
Digital Surface Model ◽  
Horizontal Position ◽  
Obstacle Clearance ◽  
Digital Elevation ◽  
Elevation Model

Compared to the wide use of digital elevation model (DEM), digital surface model (DSM) receives less attention because that it is composed by not only terrain surface, but also vegetations and man-made objects which are usually regarded as useless information. Nevertheless, these objects are useful for the identification of obstacles around an aerodrome. The primary objective of the study was to determine the applicability of DSM in obstacle clearance surveying of aerodrome. According to the requirements of obstacle clearance surveying at QT airport, aerial and satellite imagery were used to generate DSM, by means of photogrammetry, which was spatially analyzed with the hypothetical 3D obstacle limitation surfaces (OLS) to identify the potential obstacles. Field surveying was then carried out to retrieve the accurate horizontal position and height of the obstacles. The results proved that the application of DSM could make considerable improvement in the efficiency of obstacle clearance surveying of aerodrome.

scholarly journals A NEW SATELLITE IMAGERY STEREO PIPELINE DESIGNED FOR SCALABILITY, ROBUSTNESS AND PERFORMANCE

2020 ◽  
Vol V-2-2020 ◽  
pp. 171-178
Author(s):  
J. Michel ◽  
E. Sarrazin ◽  
D. Youssefi ◽  
M. Cournet ◽  
F. Buffe ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
State Of The Art ◽  
The State ◽  
Use Cases ◽  
Surface Model ◽  
Digital Surface Model ◽  
Reference Dataset ◽  
Multiview Stereo ◽  
And Performance ◽  
Pipeline Implementation

Abstract. This paper presents a new Multiview Stereo Pipeline (MVS), called CARS, dedicated to satellite imagery. This pipeline is intended for massive Digital Surface Model (DSM) production and has therefore been designed to maximize scalability robustness and performance. Those two properties have driven the design of the workflow as well as the choice of algorithms and parameter trends, making our pipeline unique with respect to existing solutions in literature. This paper intends to serve as a reference paper for the pipeline implementation, and therefore provides a detailed description of algorithms and workflow. It also demonstrates the pipeline robustness and stability in several use cases, and compares its accuracy with the state-of-the-art pipelines on a reference dataset.

scholarly journals Modified Superpixel Segmentation for Digital Surface Model Refinement and Building Extraction from Satellite Stereo Imagery

2018 ◽  
Vol 10 (11) ◽  
pp. 1824 ◽  
Author(s):  
Zeinab Gharibbafghi ◽  
Jiaojiao Tian ◽  
Peter Reinartz
Keyword(s):  
Satellite Images ◽  
Surface Model ◽  
Digital Surface Model ◽  
Building Extraction ◽  
Edge Preserving ◽  
Urban Scenes ◽  
Superpixel Segmentation ◽  
Multi Scale ◽  
Stereo Imagery ◽  
Edge Based

Superpixels, as a state-of-the-art segmentation paradigm, have recently been widely used in computer vision and pattern recognition. Despite the effectiveness of these algorithms, there are still many limitations and challenges dealing with Very High-Resolution (VHR) satellite images especially in complex urban scenes. In this paper, we develop a superpixel algorithm as a modified edge-based version of Simple Linear Iterative Clustering (SLIC), which is here called ESLIC, compatible with VHR satellite images. Then, based on the modified properties of generated superpixels, a heuristic multi-scale approach for building extraction is proposed, based on the stereo satellite imagery along with the corresponding Digital Surface Model (DSM). First, to generate the modified superpixels, an edge-preserving term is applied to retain the main building boundaries and edges. The resulting superpixels are then used to initially refine the stereo-extracted DSM. After shadow and vegetation removal, a rough building mask is obtained from the normalized DSM, which highlights the appropriate regions in the image, to be used as the input of a multi-scale superpixel segmentation of the proper areas to determine the superpixels inside the building. Finally, these building superpixels with different scales are integrated and the output is a unified building mask. We have tested our methods on building samples from a WorldView-2 dataset. The results are promising, and the experiments show that superpixels generated with the proposed ESLIC algorithm are more adherent to the building boundaries, and the resulting building mask retains urban object shape better than those generated with the original SLIC algorithm.

scholarly journals Comparing Three Machine Learning Techniques for Building Extraction from a Digital Surface Model

2021 ◽  
Vol 11 (13) ◽  
pp. 6072
Author(s):  
Nicla Maria Notarangelo ◽  
Arianna Mazzariello ◽  
Raffaele Albano ◽  
Aurelia Sole
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Urban Morphology ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Surface Model ◽  
Feature Descriptor ◽  
Digital Surface Model ◽  
Building Extraction ◽  
Area Of Interest

Automatic building extraction from high-resolution remotely sensed data is a major area of interest for an extensive range of fields (e.g., urban planning, environmental risk management) but challenging due to urban morphology complexity. Among the different methods proposed, the approaches based on supervised machine learning (ML) achieve the best results. This paper aims to investigate building footprint extraction using only high-resolution raster digital surface model (DSM) data by comparing the performance of three different popular supervised ML models on a benchmark dataset. The first two methods rely on a histogram of oriented gradients (HOG) feature descriptor and a classical ML (support vector machine (SVM)) or a shallow neural network (extreme learning machine (ELM)) classifier, and the third model is a fully convolutional network (FCN) based on deep learning with transfer learning. Used data were obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) and cover the urban areas of Vaihingen an der Enz, Potsdam, and Toronto. The results indicated that performances of models based on shallow ML (feature extraction and classifier training) are affected by the urban context investigated (F1 scores from 0.49 to 0.81), whereas the FCN-based model proved to be the most robust and best-performing method for building extraction from a high-resolution raster DSM (F1 scores from 0.80 to 0.86).

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

2018 ◽  
Vol XLII-2 ◽  
pp. 363-370 ◽  
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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