A Hough Voting Strategy for Registering Historical Aerial Images to Present-Day Satellite Imagery

2017 ◽  
pp. 595-605
Author(s):  
Sebastian Zambanini ◽  
Robert Sablatnig
Keyword(s):  
Satellite Imagery ◽  
Aerial Images ◽  
Hough Voting ◽  
Voting Strategy

scholarly journals LARGE SCALE CITY MAPPING USING SATELLITE IMAGERY / KOSMINIŲ NUOTRAUKŲ IŠ GOOGLE EARTH, TAIKOMŲ MIESTAMS KARTOGRAFUOTI STAMBIUOJU MASTELIU, REKTIFIKAVIMAS / РЕКТИФИКАЦИЯ КОСМИЧЕСКИХ СНИМКОВ ИЗ GOOGLE EARTH ДЛЯ КРУПНОМАСШТАБНОГО КАРТОГРАФИРОВАНИЯ ГОРОДОВ

2012 ◽  
Vol 37 (4) ◽  
pp. 168-171 ◽  
Author(s):  
Birutė Ruzgienė ◽  
Qian Yi Xiang ◽  
Silvija Gečytė
Keyword(s):  
Satellite Imagery ◽  
Large Scale ◽  
Modern Technology ◽  
Google Earth ◽  
Aerial Images ◽  
Control Points ◽  
Image Rectification ◽  
Image Deformation ◽  
Map Construction ◽  
Short Period

The rectification of high resolution digital aerial images or satellite imagery employed for large scale city mapping is modern technology that needs well distributed and accurately defined control points. Digital satellite imagery, obtained using widely known software Google Earth, can be applied for accurate city map construction. The method of five control points is suggested for imagery rectification introducing the algorithm offered by Prof. Ruan Wei (tong ji University, Shanghai). Image rectification software created on the basis of the above suggested algorithm can correct image deformation with required accuracy, is reliable and keeps advantages in flexibility. Experimental research on testing the applied technology has been executed using GeoEye imagery with Google Earth builder over the city of Vilnius. Orthophoto maps at the scales of 1:1000 and 1:500 are generated referring to the methodology of five control points. Reference data and rectification results are checked comparing with those received from processing digital aerial images using a digital photogrammetry approach. The image rectification process applying the investigated method takes a short period of time (about 4-5 minutes) and uses only five control points. The accuracy of the created models satisfies requirements for large scale mapping. Santrauka Didelės skiriamosios gebos skaitmeninių nuotraukų ir kosminių nuotraukų rektifikavimas miestams kartografuoti stambiuoju masteliu yra nauja technologija. Tai atliekant būtini tikslūs ir aiškiai matomi kontroliniai taškai. Skaitmeninės kosminės nuotraukos, gautos taikant plačiai žinomą programinį paketą Google Earth, gali būti naudojamos miestams kartografuoti dideliu tikslumu. Siūloma nuotraukas rektifikuoti Penkių kontrolinių taskų metodu pagal prof. Ruan Wei (Tong Ji universitetas, Šanchajus) algoritmą. Moksliniam eksperimentui pasirinkta Vilniaus GeoEye nuotrauka iš Google Earth. 1:1000 ir 1:500 mastelio ortofotografiniai žemėlapiai sudaromi Penkių kontrolinių taškų metodu. Rektifikavimo duomenys lyginami su skaitmeninių nuotraukų apdorojimo rezultatais, gautais skaitmeninės fotogrametrijos metodu. Nuotraukų rektifikavimas Penkių kontrolinių taskų metodu atitinka kartografavimo stambiuoju masteliu reikalavimus, sumažėja laiko sąnaudos. Резюме Ректификация цифровых и космических снимков высокой резолюции для крупномасштабного картографирования является новой технологией, требующей точных и четких контрольных точек. Цифровые космические снимки, полученные с использованием широкоизвестного программного пакета Google Earth, могут применяться для точного картографирования городов. Для ректификации снимков предложен метод пяти контрольных точек с применением алгоритма проф. Ruan Wei (Университет Tong Ji, Шанхай). Для научного эксперимента использован снимок города Вильнюса GeoEye из Google Earth. Ортофотографические карты в масштабе 1:1000 и 1:500 генерируются с применением метода пяти контрольных точек. Полученные результаты и данные ректификации сравниваются с результатами цифровых снимков, полученных с применением метода цифровой фотограмметрии. Ректификация снимков с применением метода пяти контрольных точек уменьшает временные расходы и удовлетворяет требования, предъявляемые к крупномасштабному картографированию.

New evidence of glacier surges in the Central Andes of Argentina and Chile

2018 ◽  
Vol 42 (6) ◽  
pp. 792-825 ◽  
Author(s):  
Daniel Falaschi ◽  
Tobias Bolch ◽  
Maria Gabriela Lenzano ◽  
Takeo Tadono ◽  
Andrés Lo Vecchio ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Central Andes ◽  
Aerial Images ◽  
Mountain Ranges ◽  
The Andes ◽  
Digital Elevation ◽  
Heterogeneous Behavior ◽  
Elevation Changes ◽  
Glacier Surges

In contrast to the large surge-type glacier clusters widely known for several mountain ranges around the world, the presence of surging glaciers in the Andes has been historically seen as marginal. The improved availability of satellite imagery during the last years facilitates investigating of glaciers in more detail even in remote areas. The purpose of the study was therefore to revisit existing information about surge-type glaciers for the Central Andes of Argentina and Chile (32° 40′–34° 20′ S), to identify and characterize possible further surge-type glaciers, providing new insights into the mass balance and evolution of the velocity of selected glaciers during the surge phase. Based on the analysis of 1962–2015 satellite imagery, historical aerial images, differencing of digital elevation models and a literature survey, we identified 21 surge-type glaciers in the study area. Eleven surge events and six possible surge-type glaciers were identified and described for the first time. The estimation of annual elevation changes of these glaciers for the 2000–2011 period, which encompasses the latest surge events in the region, showed heterogeneous behavior with strongly negative to positive surface elevation change patterns (−1.1 to +1.0 m yr−1). Additionally, we calculated maximum surface velocities of 3±1.9 m d−1 and 3.1±1.1 m d−1 for two of the glaciers during the latest identifiable surge events of 1985–1987 and 2003–2007. Within this glacier cluster, highly variable advance rates (0.01–1 km yr−1) and dissimilar surface velocities at the surge peak (3–35 m d−1) were observed. In comparison with other clusters worldwide, surge-type glaciers in the Central Andes are on average smaller and show minor absolute advances. Generally low velocities and the heterogeneous duration of the surge cycles are common between them and glaciers in the Karakorum, a region with similar climatic characteristics and many known surge-type glaciers. As a definitive assertion concerning the underlying surge mechanism of surges in the Central Andes could not be drawn based on the remote sensing data, this opens more detailed research avenues for surge-type glaciers in the region.

scholarly journals Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

Drones ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 60 ◽  
Author(s):  
Bo Yang ◽  
Timothy L. Hawthorne ◽  
Hannah Torres ◽  
Michael Feinman
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Coastal Management ◽  
Rapid Development ◽  
Object Oriented ◽  
Mapping Method ◽  
Aerial Images ◽  
Quality Data ◽  
Mapping Technique ◽  
Spectral Mapping

High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have quantitatively compared how much improvement there is of UAV multispectral mapping methods compared to more conventional remote sensing data such as satellite imagery. The objective of this paper is to quantitatively demonstrate the improvements of a multispectral UAV mapping technique for higher resolution images used for advanced mapping and assessing coastal land cover. We performed multispectral UAV mapping fieldwork trials over Indian River Lagoon along the central Atlantic coast of Florida. Ground Control Points (GCPs) were collected to generate a rigorous geo-referenced dataset of UAV imagery and support comparison to geo-referenced satellite and aerial imagery. Multi-spectral satellite imagery (Sentinel-2) was also acquired to map land cover for the same region. NDVI and object-oriented classification methods were used for comparison between UAV and satellite mapping capabilities. Compared with aerial images acquired from Florida Department of Environmental Protection, the UAV multi-spectral mapping method used in this study provided advanced information of the physical conditions of the study area, an improved land feature delineation, and a significantly better mapping product than satellite imagery with coarser resolution. The study demonstrates a replicable UAV multi-spectral mapping method useful for study sites that lack high quality data.

scholarly journals Automatic Generation of Aerial Orthoimages Using Sentinel-2 Satellite Imagery with a Context-Based Deep Learning Approach

2021 ◽  
Vol 11 (3) ◽  
pp. 1089
Author(s):  
Suhong Yoo ◽  
Jisang Lee ◽  
Junsu Bae ◽  
Hyoseon Jang ◽  
Hong-Gyoo Sohn
Keyword(s):  
Deep Learning ◽  
Satellite Imagery ◽  
Mean Squared Error ◽  
Similarity Index ◽  
Super Resolution ◽  
Structural Similarity ◽  
Automatic Generation ◽  
Fine Tuning ◽  
Aerial Images ◽  
Sentinel 2

Aerial images are an outstanding option for observing terrain with their high-resolution (HR) capability. The high operational cost of aerial images makes it difficult to acquire periodic observation of the region of interest. Satellite imagery is an alternative for the problem, but low-resolution is an obstacle. In this study, we proposed a context-based approach to simulate the 10 m resolution of Sentinel-2 imagery to produce 2.5 and 5.0 m prediction images using the aerial orthoimage acquired over the same period. The proposed model was compared with an enhanced deep super-resolution network (EDSR), which has excellent performance among the existing super-resolution (SR) deep learning algorithms, using the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root-mean-squared error (RMSE). Our context-based ResU-Net outperformed the EDSR in all three metrics. The inclusion of the 60 m resolution of Sentinel-2 imagery performs better through fine-tuning. When 60 m images were included, RMSE decreased, and PSNR and SSIM increased. The result also validated that the denser the neural network, the higher the quality. Moreover, the accuracy is much higher when both denser feature dimensions and the 60 m images were used.

scholarly journals GEOMETRIC OBJECT BASED BUILDING RECONSTRUCTION FROM SATELLITE IMAGERY DERIVED POINT CLOUDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 73-78
Author(s):  
Z. Li ◽  
B. Xu ◽  
J. Shan
Keyword(s):  
Satellite Imagery ◽  
Urban Areas ◽  
Satellite Images ◽  
Point Clouds ◽  
Geometric Object ◽  
Aerial Images ◽  
Reconstruction Method ◽  
Object Based ◽  
High Resolution Satellite Images ◽  
Building Reconstruction

<p><strong>Abstract.</strong> 3D building models are needed for urban planning and smart city. These models can be generated from stereo aerial images, satellite images or LiDAR point clouds. In this paper, we propose a geometric object-based building reconstruction method from satellite imagery derived point clouds. The goal is to achieve a geometrically correct, topologically consistent, and non-redundant 3D representation for buildings in urban areas. The paper first introduces our motivation, followed by a comprehensive review on related works. We then introduce the methodology and process developed in this paper. Primary results from the point clouds generated from WorldView high resolution satellite images are used to demonstrate the performance of the approach.</p>

scholarly journals Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment

2019 ◽  
Vol 11 (4) ◽  
pp. 436 ◽  
Author(s):  
Aleem Khaliq ◽  
Lorenzo Comba ◽  
Alessandro Biglia ◽  
Davide Ricauda Aimonino ◽  
Marcello Chiaberge ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Aerial Images ◽  
Spatial And Temporal Variability ◽  
Multispectral Imagery ◽  
Remotely Sensed Data ◽  
Spectral Contribution ◽  
Normalised Difference Vegetation Index ◽  
Soil Status

In agriculture, remotely sensed data play a crucial role in providing valuable information on crop and soil status to perform effective management. Several spectral indices have proven to be valuable tools in describing crop spatial and temporal variability. In this paper, a detailed analysis and comparison of vineyard multispectral imagery, provided by decametric resolution satellite and low altitude Unmanned Aerial Vehicle (UAV) platforms, is presented. The effectiveness of Sentinel-2 imagery and of high-resolution UAV aerial images was evaluated by considering the well-known relation between the Normalised Difference Vegetation Index (NDVI) and crop vigour. After being pre-processed, the data from UAV was compared with the satellite imagery by computing three different NDVI indices to properly analyse the unbundled spectral contribution of the different elements in the vineyard environment considering: (i) the whole cropland surface; (ii) only the vine canopies; and (iii) only the inter-row terrain. The results show that the raw s resolution satellite imagery could not be directly used to reliably describe vineyard variability. Indeed, the contribution of inter-row surfaces to the remotely sensed dataset may affect the NDVI computation, leading to biased crop descriptors. On the contrary, vigour maps computed from the UAV imagery, considering only the pixels representing crop canopies, resulted to be more related to the in-field assessment compared to the satellite imagery. The proposed method may be extended to other crop typologies grown in rows or without intensive layout, where crop canopies do not extend to the whole surface or where the presence of weeds is significant.

scholarly journals DETECTION OF CITIES VEHICLE FLEET USING YOLO V2 AND AERIAL IMAGES

2019 ◽  
Vol XLII-4/W12 ◽  
pp. 121-126 ◽  
Author(s):  
H. Lechgar ◽  
H. Bekkar ◽  
H. Rhinane
Keyword(s):  
Object Detection ◽  
Satellite Imagery ◽  
High Speed ◽  
Aerial Images ◽  
Multiple Objects ◽  
Essential Information ◽  
New Approach ◽  
Vehicle Fleet ◽  
Bounding Boxes ◽  
Key Steps

<p><strong>Abstract.</strong> Recent progress in deep learning methods has shown that key steps in object detection and recognition can be performed with convolutional neural networks (CNN). In this article, we adapt YOLO (You Only Look Once) to a new approach to perform object detection on satellite imagery. This system uses a single convolutional neural network (CNN) to predict classes and bounding boxes. The network looks at the entire image at the time of the training and testing, which greatly enhances the differentiation of the background since the network encodes the essential information for each object. The high speed of this system combined with its ability to detect and classify multiple objects in the same image makes it a compelling argument for use with satellite imagery.</p>

scholarly journals Automatic geolocation of targets tracked by aerial imaging platforms using satellite imagery

2014 ◽  
Vol XL-8 ◽  
pp. 1213-1220
Author(s):  
P. K. Shukla ◽  
S. Goel ◽  
P. Singh ◽  
B. Lohani
Keyword(s):  
Target Tracking ◽  
Satellite Imagery ◽  
Satellite Image ◽  
Search And Rescue ◽  
Aerial Images ◽  
Aerial Imaging ◽  
Feature Based ◽  
Aerial Platforms ◽  
Important Activity ◽  
Aerial Platform

Tracking of targets from aerial platforms is an important activity in several applications, especially surveillance. Knowled ge of geolocation of these targets adds additional significant and useful information to the application. This paper determines the geolocation of a target being tracked from an aerial platform using the technique of image registration. Current approaches utilize a POS to determine the location of the aerial platform and then use the same for geolocation of the targets using the principle of photogrammetry. The constraints of cost and low-payload restrict the applicability of this approach using UAV platforms. This paper proposes a methodology for determining the geolocation of a target tracked from an aerial platform in a partially GPS devoid environment. The method utilises automatic feature based registration technique of a georeferenced satellite image with an ae rial image which is already stored in UAV's database to retrieve the geolocation of the target. Since it is easier to register subsequent aerial images due to similar viewing parameters, the subsequent overlapping images are registered together sequentially thus resulting in the registration of each of the images with georeferenced satellite image thus leading to geolocation of the target under interest. Using the proposed approach, the target can be tracked in all the frames in which it is visible. The proposed concept is verified experimentally and the results are found satisfactory. Using the proposed method, a user can obtain location of target of interest as well features on ground without requiring any POS on-board the aerial platform. The proposed approach has applications in surveillance for target tracking, target geolocation as well as in disaster management projects like search and rescue operations.

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