Showing papers on "Aerial image published in 2014"

Electronic manifest of underground facility locate marks

Abstract: A method performed by a device may include receiving information regarding a particular geographic area; retrieving an aerial image of the particular geographic area; displaying the aerial image; determining an approximate geographic location of a mark denoting an underground facility; overlaying, on the displayed aerial image, information concerning the approximate geographic location of the mark denoting the underground facility; and storing the aerial image and the information concerning the approximate geographic location of the mark denoting the underground facility.

Floating aerial LED signage based on aerial imaging by retro-reflection (AIRR)

Abstract: We propose a floating aerial LED signage technique by utilizing retro-reflection. The proposed display is composed of LEDs, a half mirror, and retro-reflective sheeting. Directivity of the aerial image formation and size of the aerial image have been investigated. Furthermore, a floating aerial LED sign has been successfully formed in free space.

Accurate Geo-Registration by Ground-to-Aerial Image Matching

Abstract: We address the problem of geo-registering ground-based multi-view stereo models by ground-to-aerial image matching. The main contribution is a fully automated geo-registration pipeline with a novel viewpoint-dependent matching method that handles ground to aerial viewpoint variation. We conduct large-scale experiments which consist of many popular outdoor landmarks in Rome. The proposed approach demonstrates a high success rate for the task, and dramatically outperforms state-of-the-art techniques, yielding geo-registration at pixel-level accuracy.

Using aerial imaging to provide supplemental information about a location

Abstract: Aerial images, such as images from satellites or other aerial imaging devices, may be used to assist in responding to the occurrence of events (such as vehicle accidents or other emergency events) or conditions. In one implementation, an alert may be received indicating a condition or event associated with a user device. In response, an aerial image associated with the location of the user device may be requested. The alert may be responded to based on the received image. Aerial imaging may also provide views of the road ahead of a driver that terrain, topography, or darkness may otherwise impede. Image recognition may provide analysis of a hazard, condition, or occurrence at a scene that the aerial imaging system has captured and transmitted in response to a request.

Seamless Fusion of LiDAR and Aerial Imagery for Building Extraction

Abstract: Although many efforts have been made on the fusion of Light Detection and Ranging (LiDAR) and aerial imagery for the extraction of houses, little research on taking advantage of a building's geometric features, properties, and structures for assisting the further fusion of the two types of data has been made. For this reason, this paper develops a seamless fusion between LiDAR and aerial imagery on the basis of aspect graphs, which utilize the features of houses, such as geometry, structures, and shapes. First, 3-D primitives, standing for houses, are chosen, and their projections are represented by the aspects. A hierarchical aspect graph is then constructed using aerial image processing in combination with the results of LiDAR data processing. In the aspect graph, the note represents the face aspect and the arc is described by attributes obtained by the formulated coding regulations, and the coregistration between the aspect and LiDAR data is implemented. As a consequence, the aspects and/or the aspect graph are interpreted for the extraction of houses, and then the houses are fitted using a planar equation for creating a digital building model (DBM). The experimental field, which is located in Wytheville, VA, is used to evaluate the proposed method. The experimental results demonstrated that the proposed method is capable of effectively extracting houses at a successful rate of 93%, as compared with another method, which is 82% effective when LiDAR spacing is approximately 7.3 by 7.3 ft $^{2}$ . The accuracy of 3-D DBM is higher than the method using only single LiDAR data.

Benchmarking High Density Image Matching for Oblique Airborne Imagery

Abstract: . Both, improvements in camera technology and new pixel-wise matching approaches triggered the further development of software tools for image based 3D reconstruction. Meanwhile research groups as well as commercial vendors provide photogrammetric software to generate dense, reliable and accurate 3D point clouds and Digital Surface Models (DSM) from highly overlapping aerial images. In order to evaluate the potential of these algorithms in view of the ongoing software developments, a suitable test bed is provided by the ISPRS/EuroSDR initiative Benchmark on High Density Image Matching for DSM Computation. This paper discusses the proposed test scenario to investigate the potential of dense matching approaches for 3D data capture from oblique airborne imagery. For this purpose, an oblique aerial image block captured at a GSD of 6 cm in the west of Zurich by a Leica RCD30 Oblique Penta camera is used. Within this paper, the potential test scenario is demonstrated using matching results from two software packages, Agisoft PhotoScan and SURE from University of Stuttgart. As oblique images are frequently used for data capture at building facades, 3D point clouds are mainly investigated at such areas. Reference data from terrestrial laser scanning is used to evaluate data quality from dense image matching for several facade patches with respect to accuracy, density and reliability.

System and method for detecting features in aerial images using disparity mapping and segmentation techniques

Abstract: A system for aerial image detection and classification is provided herein. The system comprising_an aerial image database storing one or more aerial images electronically received from one or more image providers, and an object detection pre- processing engine in electronic communication with the aerial image database, the object detection pre-processing engine detecting and classifying objects using a disparity mapping generation sub-process to automatically process the one or more aerial images to generate a disparity map providing elevation information, a segmentation sub-process to automatically apply a pre-defined elevation threshold to the disparity map, the pre-defined elevation threshold adjustable by a user, and a classification sub-process to automatically detect and classify objects in the one or more stereoscopic pairs of aerial images by applying one or more automated detectors based on classification parameters and the pre¬ defined elevation threshold.

Automatic histogram-based fuzzy C-means clustering for remote sensing imagery

Abstract: Fuzzy C-means (FCM) clustering has been widely used in analyzing and understanding remote sensing images. However, the conventional FCM algorithm is sensitive to initialization, and it requires estimations from expert users to determine the number of clusters. To overcome the limitations of the FCM algorithm, an automatic histogram-based fuzzy C-means (AHFCM) algorithm is presented in this paper. Our proposed algorithm has two primary steps: 1 – clustering each band of a multispectral image by calculating the slope for each point of the histogram, in two directions, and executing the FCM clustering algorithm based on specific rules, and 2 – automatic fusion of labeled images is used to initialize and determine the number of clusters in the FCM algorithm for automatic multispectral image clustering. The performance of our proposed algorithm is first tested on clustering a very high resolution aerial image for various numbers of clusters and, next, on clustering two very high resolution aerial images, a high resolution Worldview2 satellite image, a Landsat8 satellite image and an EO-1 hyperspectral image, for a constant number of clusters. The superiority of the new method is demonstrated by comparing it with the well-known methods of FCM, K-means, fast global FCM (FGFCM) and kernelized fast global FCM (KFGFCM) clustering algorithms, both quantitatively by calculating the DB, XB and SC indices and qualitatively by visualizing the cluster results.

Tree species identification in aerial image data using directional reflectance signatures

Abstract: Tree species identification using optical remote sensing is challenging. Modern digital photogrammetric cameras enable radiometrically quantitative remote sensing and the estimation of reflectance images, in which the observations depend largely on the reflectance properties of targets. Previous research has shown that there are species-specific differences in how the brightness observed changes when the viewing direction in an aerial image is altered. We investigated if accounting for such directional signatures enhances species classification, using atmospherically corrected, real and simulated multispectral Leica ADS40 line-camera data. Canopy in direct and diffuse illumination were differentiated and species-specific variance-covariance structures were analyzed in real reflectance data, using mixed-effects modeling. Species classification simulations aimed at elucidating the level of accuracy that can be achieved by using images of different quality, number and view-illumination geometry. In real data, a substantial variance component was explained by tree effect, which demonstrates that observations from a tree correlate between observation geometries as well as spectrally. Near-infrared band showed the strongest tree effect, while the directionality was weak in that band. The gain from directional signatures was insignificant in real data, while simulations showed a potential gain of 1–3 percentage points in species classification accuracy. The quality of reflectance calibration was found to be important as well as the image acquisition geometry. We conclude that increasing the number of image observations cancels out random observation noise and reflectance calibration errors, but fails to eliminate the tree effect and systematic calibration inaccuracy. Directional reflectance constitutes a marginal improvement in tree species classification.

A multi-band watershed segmentation method for individual tree crown delineation from high resolution multispectral aerial image

Abstract: A comprehensive forest resource inventory needs more detailed species information at individual tree level. Although conventional ground-based measurement fails to achieve this target in an efficient way, the emergence of high resolution remote sensing images has made it possible in the past decade. Individual tree crown delineation is one of the most critical steps for tree species classification from remote sensing images. However, it is still challenging to delineate individual tree crowns in deciduous forests because of the continuous canopy. In this study, a multi-band watershed segmentation method is proposed to delineate deciduous tree crowns by constructing a spectral angle space. The proposed algorithm is further examined by a high resolution multispectral aerial image of a deciduous forested area in Haliburton Forest, Ontario, Canada. Results demonstrate that, the proposed multi-band watershed segmentation method outperforms the existing valley-following based ITC map, in terms of visual interpretation and quantitative evaluation.

Moving object detection using dynamic motion modelling from UAV aerial images.

Abstract: Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology.

Digital surface model based on aerial image stereo pairs for 3D building

Abstract: There is an increasing demand for 3D city models for many applications and users worldwide. Fez is one of the most important tourism locations in Morocco and is a challenge for 3D city modeling due to its complex buildings and road structure. Due to its importance this Historic Medina of Fez was added to the UNESCO World Heritage List in 1981. It is located in the northern part of Morocco. In this work, we discuss the construction of a digital surface model based on aerial image stereo pairs using matching method and the use of this DSM for 3D city planning. We used aerial photographs with high accuracy (1/4000) covering the study area acquired in 2007 and additional cartographic data from Fez. 3D land use zoning allowed building volumes, usage, and density. They are the main tools defining the image of a city and bring into focus the model of best practice of the rehabilitation and conservation of the historic Medina.

A Multilayer Markovian Model for Change Detection in Aerial Image Pairs with Large Time Differences

Abstract: In this paper, we propose a Multilayer Markovian model for change detection in registered aerial image pairs with large time differences. A Three Layer Markov Random Field takes into account information from two different sets of features namely the Modified HOG (Histogram of Oriented Gradients) difference and the Gray-Level (GL) Difference. The third layer is the resultant combination of the two layers. Thus we integrate both the texture level as well as the pixel level information to generate the final result. The proposed model uses pair wise interaction retaining the sub-modularity condition for energy. Hence a global energy optimization can be achieved using a standard min-cut/ max flow algorithm ensuring homogeneity in the connected regions.

Automatic UAV Forced Landing Site Detection Using Machine Learning

Abstract: The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles) However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude

Systems and Methods for Creating an Aerial Image

Abstract: Systems/apparatuses and methods are provided for creating aerial images. A three-dimensional point cloud image is generated from an optical distancing system. Additionally, at least one two-dimensional street level image is generated from at least one camera. The three-dimensional point cloud image is colorized with the at least one two-dimensional street level image, thereby forming a colorized three-dimensional point cloud image. The colorized three-dimensional point cloud image is projected onto a two-dimensional plane, using a processor, thereby forming a synthetic aerial image.

Systems and Methods for Refining an Aerial Image

Abstract: Systems, apparatuses, and methods are provided for refining an aerial image. A plurality of aerial images is received using a processor. The plurality of aerial images is developed into a three-dimensional model. The three-dimensional model is synthesized into an improved orthophoto image. The improved orthophoto image may be stored on a personal computer or workstation as a reference platform.

Shoreline extraction from the fusion of LiDAR DEM data and aerial images using mutual information and genetic algrithms

Abstract: As sea level rises and coastal populations continue to grow, there is an increased demand for understanding the accurate position of the shorelines. The automatic extraction of shorelines utilizing the digital elevation models (DEMs) obtained from light detection and ranging (LiDAR), aerial images and multi-spectral images has become very promising. In this paper, we propose a new algorithm that can effectively extract shorelines from fused LiDAR DEMs with aerial images depending on the availability of training data. The LiDAR data and the aerial image are fused together by maximizing the mutual information using the genetic algorithm. The extraction of shoreline is obtained by segmenting the fused data into water and land by means of the support vector machines classifier. Compared with other relevant techniques in literature, the proposed method offers better accuracy in shoreline extraction.

Automatic UAV forced landing site detection using machine learning

Abstract: The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Support system for surveying moving wild animals in the snow using aerial remote-sensing images

Abstract: Japan is one of the most diverse zoogeographic regions in the world. It includes subtropical to cool temperate zones, high land and low land, and a high ratio of forest and mountain or hill. Owing to their low densities, large geographical ranges, and the large size of forested and mountainous areas, population size information on large mammals in Japan is generally insufficient. To address this problem, we developed an automated support system for detecting wild animals moving over snow using two overlapping aerial images. The system reduces the number of man-hours required to survey moving wild animals from a large amount of aerial image data. The system consists of three newly developed algorithms which perform the following tasks: 1 feature point extraction for registering two images, 2 corresponding point identification by determining the correspondence between feature points, and 3 automatic detection of moving wild animals by comparing two images using a computer-aided detection of moving wild animals DWA algorithm. We applied the proposed algorithms to several types of aerial images to automatically extract cattle, deer, and a walking human. Furthermore, we conducted a survey of wild animals using the system and used it to detect a walking human. The number of man-hours required to conduct the survey was reduced by 90%. A further advantage of the system was that, since relief displacement effects do not cause false detection, the system can be employed in forested areas during the leaf-off period.

An algorithm for the detection of vessels in aerial images

Abstract: In this paper we present a sea vessel detection algorithm in aerial image sequences acquired by an unmanned aerial vehicle. The proposed method is robust to variable background lighting, highlights due to sun reflections, vehicle self motion and scale changes. By relying in simple blob analysis rules, based on both spatial and temporal constraints, the algorithm is capable of real-time operation onboard the vehicle, even with non optimized code. We evaluate our method on three sequences labeled with ground truth vessel position, with more that 2900 frames. Overall we are able to achieve very low false positive rates even in heavy sun reflection conditions.

Hierarchical Semantic Hashing: Visual Localization from Buildings on Maps

Abstract: In this paper we present a vision-based method for instant global localization from a given aerial image. The approach mimics how humans localize themselves on maps using spatial layout of semantic elements on the map. Unlike other matching and localization methods that use visual appearance or feature matching, our method relies on robust and consistently detectable semantic elements that are invariant to illumination, temporal variations and occlusions. We use the buildings on the map and on the given aerial query image as our semantic elements. Spatial relations between these elements are efficiently stored and queried under a hierarchical semantic version of the Geometric Hashing algorithm that is inherently rotation and scale invariant. We also present a method to obtain building locations from a given query image using image classification and processing techniques. Overall this approach provides fast and robust localization over large areas. We show our experimental results for localizing satellite image tiles from a 16.5 km sq dense city map with over 7,000 buildings.

Fast Lithography Simulation under Focus Variations for OPC

Abstract: In 90nm technology and beyond, process variations should be considered such that the design will be robust with respect to process variations. Focus error and exposure dose variations are the two most important lithography process variations. In a simple approximation, the critical dimension (CD) is about linearly related to the exposure dose variation, while it is quadratically related to the focus variation. Other kinds of variations can be reduced to these variations effectively as long as they are small. As a metric to measure the effects of exposure dose variations, normalized image log-slope (NILS) is pretty fast to compute once we have the aerial images. OPC software has used it as an optimization objective. But focus variation has not been commonly considered in current OPC software. One way is to compute several aerial images at different defocus conditions, but this approach is very time consuming. In this paper, we derive an analytical formula to compute the aerial image under any defocus condition. This method works for any illumination scheme and is applicable to both binary and phase shift masks (PSM). A model calibration method is also provided. It is demonstrated that there is only about 2-3x runtime increase using our fast focus-variational lithography simulation compared to the current single-focus lithography simulation. To confirm the accuracy, our model is compared with PROLITH TM . This ultra-fast simulator can enable better and faster process-variation aware OPC to make layouts more robust under process variations, and directly guide litho-aware layout optimizations.

A Comparison of 4 Shadow Compensation Techniques for Land Cover Classification of Shaded Areas from High Radiometric Resolution Aerial Images

Abstract: Very high resolution imagery offers great possibilities for land cover/use mapping. Unfortunately, very high resolution imagery leads to significant shadowy pixels. In recent years, aerial image devices have produced high radiometric resolution data (12-bit or higher), providing more radiometric detail of potential use in classification or interpretation of land cover of shadow areas. This study evaluated ADS-40 high radiometric resolution aerial images to determine the feasibility of classifying shadow areas. We analyzed the spectral properties of land cover in shadow areas and conducted shadow-image classification comparing 4 shadow compensation methods: Method 1, no treatment, used 13-bit spectral information in shaded areas for classification; Method 2 used linear correlation correction (LCC) before the classification; Method 3 used histogram matching (HM) before the classification; and Method 4 used multisource data fusion (MSDF) to aid in classification of shadows. Subsequently, we developed...

Panoramic image unmanned aerial vehicle acquisition system

Abstract: The utility model provides a panoramic image unmanned aerial vehicle acquisition system which comprises an unmanned aerial vehicle and a panorama camera. The panorama camera is installed in a central load cabin of the vehicle body of the unmanned aerial vehicle. The panorama camera comprises a camera control system, a GPS positioning device, a storage unit and a plurality of sets of lenses. The lenses are installed on the outer side of the vehicle body of the unmanned aerial vehicle and face to different directions. The lenses, the GPS positioning device and the storage unit are respectively connected with the camera control system through a CAN bus. The panoramic image unmanned aerial vehicle acquisition system has the advantages that the panorama camera is mounted on the unmanned aerial vehicle to carry out aerial image acquisition, the acquisition range is wide and acquisition efficiency is high; the lenses of the panorama camera face to different directions, the image of an object can be collected from different angles, and the acquisition accuracy is high.

Object-based Multi-Image Semi-Global Matching - Concept and first results

Abstract: Semi-Global Matching (SGM) is a widespread algorithm for image matching which is used for very different applications, reaching from real-time applications (e.g. for generating 3D-data for driver assistance systems) to aerial image matching. Originally developed for stereo-image matching, several extensions have been proposed to use more than two images within the matching process (multibaseline matching, multi-view stereo). Most of these extensions still perform the image matching in (rectified) stereo images and combine the pairwise results afterwards to create the final solution. This paper proposes an alternative approach which is suitable for the introduction of an arbitrary number of images into the matching process and utilizes image matching by using non-rectified images within a closed solution. The proposed approach differs from the original SGM method in two major aspects: Firstly, the cost calculation is formulated in object space within a dense voxel raster by using the grey- (or colour-) values of all images instead of pairwise cost calculation in image space. Secondly, the semi-global (path-wise) minimization process is transferred into object space as well, so that the result of semi-global optimization leads to index-maps (instead of disparity maps) which directly indicate the 3D positions of the best matches. The paper provides a detailed description of the approach and it discusses its advantages and disadvantages. Further on, first results and accuracy analysis are presented.

Automatic aerial image segmentation using a modified Chan-Vese algorithm

Abstract: Automatic segmentation of aerial images has been a challenging area of research in recent years. Among numerous image segmentation methods, the level set method has received a great deal of attention which could represent contours or surfaces with complex topology and change their topology in a natural way. The solution of classic level set model, however, can be easily trapped into a local minimum. To overcome this problem, a novel modified dual Chan-Vese model is proposed in this paper. This proposed model is composed of two contours, which evolve towards the edges of objects from inside of the objects and outside of the objects. By reducing the differences between the interior contour and the external contour, the proposed model can partly prevent the solution of the level set method from a local minimum. Experiments show that the proposed model can obtain exact aerial image segmentation.

Control of image triggering for aerial image capturing in nadir alignment for an unmanned aircraft

Abstract: Method for aerial image capturing by means of an unmanned and controllable aircraft comprising a camera, more particularly a drone, during a flight manoeuvre of said aircraft, comprising continual determining of a camera position and alignment of an optical camera axis and acquiring of a series of aerial images. For each aerial image (21a-b) of said aerial image series, the capturing of the respective aerial image (21a-b) is triggered by flying through a respective image trigger region (33) with said aircraft, wherein the location of said respective image trigger region (33) is determined at least in each case by one trigger position assigned to said respective image trigger region (33) and triggered subject to the alignment of the camera axis when flying through said respective image trigger region (33), with respect to fulfilling a defined, maximum angle deviation relative to a predetermined spatial alignment.