Development of Line-of-Sight Digital Surface Model for Co-Registering Off-Nadir VHR Satellite Imagery With Elevation Data

2015 ◽  
Vol 8 (5) ◽  
pp. 1913-1923 ◽  
Author(s):  
Alaeldin Suliman ◽  
Yun Zhang
Keyword(s):  
Satellite Imagery ◽  
Line Of Sight ◽  
Surface Model ◽  
Digital Surface Model ◽  
Elevation Data

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 BUILDING OUTLINE EXTRACTION FROM AERIAL IMAGERY AND DIGITAL SURFACE MODEL WITH A FRAME FIELD LEARNING FRAMEWORK

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 487-493
Author(s):  
X. Sun ◽  
W. Zhao ◽  
R. V. Maretto ◽  
C. Persello
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Surface Model ◽  
Digital Surface Model ◽  
Frame Field ◽  
Learning Framework ◽  
Elevation Data ◽  
3D Information ◽  
Direction Information

Abstract. Deep learning-based semantic segmentation models for building delineation face the challenge of producing precise and regular building outlines. Recently, a building delineation method based on frame field learning was proposed by Girard et al. (2020) to extract regular building footprints as vector polygons directly from aerial RGB images. A fully convolution network (FCN) is trained to learn simultaneously the building mask, contours, and frame field followed by a polygonization method. With the direction information of the building contours stored in the frame field, the polygonization algorithm produces regular outlines accurately detecting edges and corners. This paper investigated the contribution of elevation data from the normalized digital surface model (nDSM) to extract accurate and regular building polygons. The 3D information provided by the nDSM overcomes the aerial images’ limitations and contributes to distinguishing the buildings from the background more accurately. Experiments conducted in Enschede, the Netherlands, demonstrate that the nDSM improves building outlines’ accuracy, resulting in better-aligned building polygons and prevents false positives. The investigated deep learning approach (fusing RGB + nDSM) results in a mean intersection over union (IOU) of 0.70 in the urban area. The baseline method (using RGB only) results in an IOU of 0.58 in the same area. A qualitative analysis of the results shows that the investigated model predicts more precise and regular polygons for large and complex structures.

scholarly journals TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 161-169 ◽  
Author(s):  
K. Bakuła ◽  
P. Kupidura ◽  
Ł. Jełowicki
Keyword(s):  
Land Cover ◽  
Laser Scanning ◽  
Near Infrared ◽  
Airborne Laser Scanning ◽  
Surface Model ◽  
Digital Surface Model ◽  
Airborne Laser ◽  
Laser Reflectance ◽  
Elevation Data ◽  
Intensity Images

Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.

scholarly journals TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 161-169 ◽  
Author(s):  
K. Bakuła ◽  
P. Kupidura ◽  
Ł. Jełowicki
Keyword(s):  
Land Cover ◽  
Laser Scanning ◽  
Near Infrared ◽  
Airborne Laser Scanning ◽  
Surface Model ◽  
Digital Surface Model ◽  
Airborne Laser ◽  
Laser Reflectance ◽  
Elevation Data ◽  
Intensity Images

Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.

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 Digital surface models and the landscape: interaction between bedrock and glacial geology in the Ullapool area

2009 ◽  
Vol 45 (2) ◽  
pp. 99-105 ◽  
Author(s):  
K. M. Goodenough ◽  
M. Krabbendam ◽  
T. Bradwell ◽  
A. Finlayson ◽  
A. G. Leslie
Keyword(s):  
Surface Model ◽  
Digital Surface Model ◽  
Glacial Geology ◽  
Glacial Geomorphology ◽  
Landscape Elements ◽  
Bedrock Geology ◽  
Geological Features ◽  
Surface Models ◽  
Elevation Data ◽  
Thrust Zone

SynopsisThe front cover image for this volume is a hill-shaded digital surface model (DSM) of the Ullapool area, created using NEXTMap Britain elevation data from Intermap Technologies. This is a classic area for bedrock geology, transected by the Moine Thrust Zone, and in recent years it has also been studied in detail for its glacial history. Perhaps equally important, this is one of Scotland's most iconic landscapes. The geology of the area comprises a number of distinct sequences, each of which has a characteristic landscape expression as illustrated by the DSM. This paper considers the influence of the bedrock geology on the glacial geomorphology, and shows that the interplay of the two has led to the development of the different landscape elements of this spectacular area. Surprisingly, it is not always the major geological features – such as the Moine Thrust – that have the strongest topographic expression.

scholarly journals Ekstraksi Digital Surface Model (DSM) dari Data Unmanned Aerial Vehicle (UAV) Berbasis Point Cloud

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Indra Laksana ◽  
R Suharyadi ◽  
M. Pramono Hadi
Keyword(s):  
Data Acquisition ◽  
Point Cloud ◽  
Unmanned Aircraft ◽  
Surface Model ◽  
Digital Surface Model ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Key Points ◽  
Elevation Data ◽  
Data Point

<div class="WordSection1"><p><strong>Abstr</strong><strong>ak. </strong>Akuisisi data dengan menggunakan pesawat tanpa awak semakin sering dilakukan. Penelitian ini memodelkan data elevasi dari pengukuran lapangan dengan menggunakan pesawat tanpa awak. Tujuan dari penelitian ini :(1) untuk menguji kemampuan pesawat tanpa awak dalam mengakuisisi data elevasi, dan (2) untuk membandingkan data elevasi jika ditambahkan data point cloud dan data pengukuran batimetri. Metode pengolahan dengan menggunakan data point cloud dilakukan dengan pertama-tama mencocokkan titik kunci. Pencocokan titik kunci mengkaitkan seluruh hasil foto udara hingga membentuk satu kesatuan area yang telah difoto. Selanjutnya dilakukan penampalan titik ikat pada area yang telah terbentuk dari pencocokan titik kunci. Titik ikat berfungsi sebagai koreksi data pada saat pesawat tanpa awak melakukan pengambilan data. Foto udara yang telah dikoreksi kemudian diolah untuk mendapatkan data <em>point cloud</em>. <em>Point cloud</em> berguna sebagai data penyusun ortofoto dan data <em>Digital Surface Model</em> (DSM). Pengolahan data point cloud hingga menghasilkan DSM dilakukan dengan menggunakan software Pix4D dan Agisoft photoscan. Hasil yang diperoleh menunjukkan bahwa terjadi peningkatan kemampuan DSM ketika data pointcloud ditambahkan data titik ikat dan data pengukuran batimetri. Sehingga dapat disimpulkan bahwa akuisisi data menggunakan pesawat tanpa awak mampu menghasilkan data yang dapat dipercaya. Selain dapat dipercaya akuisisi data dengan pesawat tanpa awak lebih murah jika dibandingkan dengan akuisisi data dengan foto udara.</p><p>Keywords:  digital surface model, pesawat tanpa awak, titik ikat</p><p><strong> </strong></p><p><strong>Abstract. </strong><em>Data acquisition using unmanned aircraft is increasingly being done. This study models elevation data from field measurements using unmanned aircraft. The purpose of this study: (1) to test the ability of unmanned aircraft to acquire elevation data, and (2) to compare elevation data if added point cloud data and bathymetry measurement data. The processing method using point cloud data is done by first matching key points. Matching key points links all aerial photography results to forming a single unit area that has been photographed.</em><em> </em><em>Next, a tie point is carried out in the area formed from matching key points. Tie points function as data correction when unmanned aircraft take data. Corrected aerial photos are then processed to obtain point cloud data.</em><em> </em><em>Point cloud is useful as orthophoto compiler data and Digital Surface Model (DSM) data.</em><em> </em><em>Point cloud data processing to produce DSM is done using Pix4D and Agisoft photoscan software.</em><em>The results obtained showed that there was an increase in DSM capabilities when point cloud data was added to the tie point data and bathymetry measurement data. So, it can be concluded that data acquisition using unmanned aircraft is able to produce reliable data. Besides being reliable, data acquisition with unmanned aircraft is cheaper compared to data acquisition with aerial photography.</em></p></div><strong><em>Keywords:</em> </strong>u<em>nmanned aerial vehicle, ground c point, Digital surface model</em><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph;"> </p>

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