scholarly journals 3D-information fusion from very high resolution satellite sensors

2015 ◽  
Vol XL-7/W3 ◽  
pp. 651-656 ◽  
Author(s):  
T. Krauss ◽  
P. d'Angelo ◽  
G. Kuschk ◽  
J. Tian ◽  
T. Partovi
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Information Fusion ◽  
Renewable Energy Sources ◽  
Open Pit ◽  
Open Pit Mining ◽  
Surface Model ◽  
Digital Terrain ◽  
3D Information ◽  
Very High

In this paper we show the pre-processing and potential for environmental applications of very high resolution (VHR) satellite stereo imagery like these from WorldView-2 or Pl´eiades with ground sampling distances (GSD) of half a metre to a metre. To process such data first a dense digital surface model (DSM) has to be generated. Afterwards from this a digital terrain model (DTM) representing the ground and a so called normalized digital elevation model (nDEM) representing off-ground objects are derived. Combining these elevation based data with a spectral classification allows detection and extraction of objects from the satellite scenes. Beside the object extraction also the DSM and DTM can directly be used for simulation and monitoring of environmental issues. Examples are the simulation of floodings, building-volume and people estimation, simulation of noise from roads, wave-propagation for cellphones, wind and light for estimating renewable energy sources, 3D change detection, earthquake preparedness and crisis relief, urban development and sprawl of informal settlements and much more. Also outside of urban areas volume information brings literally a new dimension to earth oberservation tasks like the volume estimations of forests and illegal logging, volume of (illegal) open pit mining activities, estimation of flooding or tsunami risks, dike planning, etc. In this paper we present the preprocessing from the original level-1 satellite data to digital surface models (DSMs), corresponding VHR ortho images and derived digital terrain models (DTMs). From these components we present how a monitoring and decision fusion based 3D change detection can be realized by using different acquisitions. The results are analyzed and assessed to derive quality parameters for the presented method. Finally the usability of 3D information fusion from VHR satellite imagery is discussed and evaluated.

Prospecting urban rooftop solar farm potential in Dublin, Ireland

2021 ◽  
Author(s):  
Ankit Verma ◽  
John Connolly ◽  
Noel O'Connor
Keyword(s):  
Renewable Energy ◽  
High Resolution ◽  
Solar Energy ◽  
Urban Areas ◽  
Renewable Energy Sources ◽  
Solar Irradiation ◽  
Industrial Building ◽  
Surface Model ◽  
Very High ◽  
Rooftop Solar

<p>The development of a sustainable and renewable energy system is a significant challenge for Ireland. In line with UN and EU policies, Ireland aims to transition to a competitive, low carbon, climate-resilient and environmentally sustainable economy by 2050 (Project Ireland 2040 National Planning Framework). Ireland is committed to an aggregate reduction in CO<sub>2</sub> emissions of at least 80% (compared to 1990 levels) by 2050 across the electricity generation, built environment and transport sectors. Renewable energy can help Ireland reduce GHG emissions and carbon footprint as energy demands grow. It also reduces dependencies on fossil fuels as well as increases energy supply security.</p><p>According to the Sustainable Energy Authority of Ireland’s “Energy in Ireland 2020” report, 36.5% of electricity demand was met by renewable energy sources in 2019. Wind energy contributes 32% while solar energy contributes to <1%. Significant investment has been made in Ireland’s wind sector; however, the solar energy sector is relatively new. Ireland has the second-lowest total installed and cumulated solar photovoltaic (PV) capacity in the EU with just 36 MW or 7.3 W per inhabitant. (EurObserv'ER 2019).</p><p>Solar prospecting is necessary to identify optimum locations where solar farms can be established. Commercial and industrial building rooftops in urban areas offer a suitable location for establishing rooftop solar farms due to good connectivity with the electricity grid and proximity to users. Here we present an urban solar prospecting study in Dublin, Ireland.</p><p>A very high-resolution geospatial dataset was acquired for 47 industrial areas covering 53.3 km<sup>2</sup>. The data comprises of very high-resolution aerial images (12.5 cm/pixel) and digital surface model (DSM) (25 cm/pixel).</p><p>The high-resolution DSMs were used to model solar irradiation on building rooftops in ArcGIS Pro using the area solar analyst tool. These models were optimised for Irish conditions using Met Éireann solar radiation data for Dublin. The maximum solar insolation received in Dublin is 1000-1050 kWh/m<sup>2</sup>. The results demonstrate that there is potentially a large amount of commercial and industrial rooftop surface area available for PV installation in Dublin. These rooftops can generate a significant amount of electricity and help to offset CO<sub>2</sub> emissions.</p><p> </p>

scholarly journals USE OF VERY HIGH-RESOLUTION AIRBORNE IMAGES TO ANALYSE 3D CANOPY ARCHITECTURE OF A VINEYARD

2015 ◽  
Vol XL-3/W3 ◽  
pp. 399-403 ◽  
Author(s):  
S. Burgos ◽  
M. Mota ◽  
D. Noll ◽  
B. Cannelle
Keyword(s):  
High Resolution ◽  
Ground Level ◽  
Surface Model ◽  
Canopy Architecture ◽  
Average Value ◽  
Digital Terrain ◽  
Green Cover ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Very High

Differencing between green cover and grape canopy is a challenge for vigour status evaluation in viticulture. This paper presents the acquisition methodology of very high-resolution images (4 cm), using a Sensefly Swinglet CAM unmanned aerial vehicle (UAV) and their processing to construct a 3D digital surface model (DSM) for the creation of precise digital terrain models (DTM). The DTM was obtained using python processing libraries. The DTM was then subtracted to the DSM in order to obtain a differential digital model (DDM) of a vineyard. In the DDM, the vine pixels were then obtained by selecting all pixels with an elevation higher than 50 [cm] above the ground level. The results show that it was possible to separate pixels from the green cover and the vine rows. The DDM showed values between −0.1 and + 1.5 [m]. A manually delineation of polygons based on the RGB image belonging to the green cover and to the vine rows gave a highly significant differences with an average value of 1.23 [m] and 0.08 [m] for the vine and the ground respectively. The vine rows elevation is in good accordance with the topping height of the vines 1.35 [m] measured on the field. This mask could be used to analyse images of the same plot taken at different times. The extraction of only vine pixels will facilitate subsequent analyses, for example, a supervised classification of these pixels.

scholarly journals Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster–Shafer Theory

2020 ◽  
Vol 12 (6) ◽  
pp. 983 ◽  
Author(s):  
Youkyung Han ◽  
Aisha Javed ◽  
Sejung Jung ◽  
Sicong Liu
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Principal Component ◽  
Geographic Area ◽  
Majority Voting ◽  
Dempster Shafer Theory ◽  
Object Based ◽  
Intensity Images ◽  
Shafer Theory ◽  
Very High

Change detection (CD), one of the primary applications of multi-temporal satellite images, is the process of identifying changes in the Earth’s surface occurring over a period of time using images of the same geographic area on different dates. CD is divided into pixel-based change detection (PBCD) and object-based change detection (OBCD). Although PBCD is more popular due to its simple algorithms and relatively easy quantitative analysis, applying this method in very high resolution (VHR) images often results in misdetection or noise. Because of this, researchers have focused on extending the PBCD results to the OBCD map in VHR images. In this paper, we present a proposed weighted Dempster-Shafer theory (wDST) fusion method to generate the OBCD by combining multiple PBCD results. The proposed wDST approach automatically calculates and assigns a certainty weight for each object of the PBCD result while considering the stability of the object. Moreover, the proposed wDST method can minimize the tendency of the number of changed objects to decrease or increase based on the ratio of changed pixels to the total pixels in the image when the PBCD result is extended to the OBCD result. First, we performed co-registration between the VHR multitemporal images to minimize the geometric dissimilarity. Then, we conducted the image segmentation of the co-registered pair of multitemporal VHR imagery. Three change intensity images were generated using change vector analysis (CVA), iteratively reweighted-multivariate alteration detection (IRMAD), and principal component analysis (PCA). These three intensity images were exploited to generate different binary PBCD maps, after which the maps were fused with the segmented image using the wDST to generate the OBCD map. Finally, the accuracy of the proposed CD technique was assessed by using a manually digitized map. Two VHR multitemporal datasets were used to test the proposed approach. Experimental results confirmed the superiority of the proposed method by comparing the existing PBCD methods and the OBCD method using the majority voting technique.

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