ID: 58792
Title: Moving code - Sharing geoprocessing logic on the Web.
Author: Mathias Miller, Lars Bernard, Daniel Kadner.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 193-203 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Geoprocessing, Software reuse, Interoperability, Spatial data Infrastructures, Standards.
Abstract: Efficient data processing is a long-standing challenge in remote sensing. Effective and efficient algorithms are required for product generation in ground processing systems, event-based or on-demand analysis, environmental monitoring, and data mining. Furthermore, the increasing number of survey missions and the exponentially growing data volume in recent years have created demand for better software reuse as well as an efficient use of scalable processing infrastructures. Solutions that address both demands simultaneously have begun to slowly appear, but they seldom consider the possibility to coordinate development and maintenance efforts across different institutions, community projects, and software vendors.
This paper presents a new approach to share, reuse, and possibly standardise geoprocessing logic in the field of remote sensing. Drawing from the principles of service-oriented design and distributed processing, this paper introduces moving-code packages as self-describing software components that contain algorithmic code and machine readable description of the provided functionality, platform, and infrastructure, as well as basic information about exploitation rights. Furthermore, the paper presents a lean publishing mechanism by which to distribute these packages on the Web and to integrate them in different processing environments ranging from monolithic workstations to elastic computational environments ranging from monolithic workstations to elastic computational environments or "clouds". The paper concludes with an outlook toward community repositories for reusable geoprocessing logic and their possible impact on data-driven science in general.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58791
Title: Semantic orchestration of image processing services for environmental analysis.
Author: Elisabeth Ranisavljevic, Florent Devin, Dominique Laffly, Yannick Le Nir.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 184-192 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Environment, Image processing, Snow ice, Web service, Orchestration, Semantic.
Abstract: In order to analyze environmental dymamics, a major process is the classifcation of the different phenomena of the site (e.g. ice and snow for a glacier). When using the in situ pictures, this classification requires data pre-proccessing. Not all the pictures need the same sequence of processes depending on the disturbances. Until now, these sequences have been done manually, which restricts the processing of large amount of data.
In this paper, we present how to realize a semantic orchestration to automate the sequencing for the analysis. It combines two advantages: solving the problem of the amont of processing and diversifying the possibilities in the data processing. We define a BPEL description to express the sequences. This BPEL uses some web services to run the data processing. The dynamic modification of the BPEL is done using SPARQL queries on these annotated web services. The results obtained by a prototype implementing this method validate the construction of the different workflows that can be applied to a large number of pictures.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58790
Title: Adaptive geo-information processing service evolution: Reuse and local modification method.
Author: Haifeng Li, Bo Wu.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 165-183 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Geography information services, Services automated composition, Evolution, Reuse, Local modification.
Abstract: Geo-information (GI) service automated composition according to user demands is a crucial task in spatial data infrastructures. State-of-the-art GI service composition approaches face serious limitations in terms of effectiveness and stability as the general GI proccessing service chain (GIPSC) must be generated from individual user specifications from scratch. This paper presents a novel approach called an adaptive geo-information service evolution (AgiSE) method which overcomes these limitations by adaptively reusing and modifying previously generated GIPSC. In this method, an influence domain minimisation (IDM) criterion is employed to modify existing GIPSC to fit the new (changed) user demands through minimum revisions. The correction of local modification is ensured by process and integrity constraints. An innovative algorithm called influence domain pursuit is developed to find the optimised solution through a heuristic backward search based on the defined IDM. Experimental analysis shows the significant improvements of using AgiSE in GI services compared with existing traditional methods. The benefits of AgiSE are the improved efficiency of GI services compared with existing traditional methods. The benefits os AgiSE are the improved efficiency of GI service composition and the improved executing stability of GIPSC which were achieved by reducing the service provider load. The AgiSE presented in this paper is crucial in reusing a general unified framework for GI service composition.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58789
Title: Intelligent services for discovery of complex geospatial features from remote sensing imagery.
Author: Peng Yue, Liping Di, Yaxing Wei, Weiguo Han.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 151-164 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Image mining, Geospatial services, Workflow Semantic, Feature discovery, GIS, Complex geospatial features.
Abstract: Remote sensing imagery has been commonly used by intelligence analysts to discover geospatial features, including complex ones. The overwhelming volume of routine image acquisition requires automated methods or systems for features such as buildings and roads from remote sensing imagery have been studied extensively. The discovery of complex geospatial features, however, is still rather understudied. A complex feature, such as a Weapon of Mass Destruction (WMD) proliferation facility is spatially composed of elementary features (e.g., buildings for hosting fuel concentration machines, cooling towers, transportation roads, and fences). Such spatial semantics and services. The elementary features extracted from imagery are archived in distributed Web Feature Services (WFSs) and dicoverable from a catalogue service. Using spatial semantics among elementary features and thematic semantics among feature types, workflow-based service chains can be constructed to locate semantically-related complex features in imagery. The workflows are reusable and can provide on-demand discovery of complex features in a distributed environment.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58788
Title: Temporal logic and operation relations based knowledge representation for land cover change web services.
Author: Jun Chen, Hao Wu, Songian Li, Anping Liao, Chaoying He, Shu Peng
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 140-150 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Land cover, Change information, Temporal Logic, Spatial operation, Knowledge representation.
Abstract: Providing land cover spatio-temporal information and geo-computing through web service is a new challenge for supporting global change research, earth system simulation and many other societal benefit areas. This requires an integrated knowledge representation and web implementation of static land cover and change information, as well as the related operations for geo-computing. The temporal logic relations among land cover snapshots and increments were examined with a matrix-based three-step analysis. Twelve temporal logic relations were identified and five basic spatial operations were formalized with set of operators, which were all used to develop algorithms for deriving implicit change information. A knowledge representation for land cover change information was then developed based on these temporal logic and operation relations. A prototype web service system was further implemented can be facilitated with such a web service system.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58787
Title: Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery.
Author: Oleksandr Kit, Matthias Ludeke.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 130-137 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Urban, Developing countries, Identification, High resolution, Multitemporal.
Abstract: This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very highr resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny-and LSD-based imagery pre-proccessing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate in considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58786
Title: A protocol for improving mapping and assessing of seagrass abundance along the West Central Coast of Florida using Landsat TM and EO-1 ALI/Hyperion images.
Author: Ruiliang Pu, Susan Bell.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 116-129 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Image optimization, Submerged aquatic vegetation (SAV), Fuzzy synthetic evaluation, Leaf area index, Biomass, Remote sensing.
Abstract: Seagrass habitats are characteristic features of shallow waters worldwide and provide a variety of ecosystem functions. Remote sensing techniques can help connect spatial and temporal information about seagrass resources. In this study, we evaluate a protocol that utilizes image optimization algorithms followed by atmospheric and sunlight connections to the three satellite sensors [Landsat 5 Thematic Mapper (TM), Earth Observing-1 (EO-1) Advanced Land Imager (ALI) and Hyperion (HYP)] and a fuzzy synthetic evaluation technique to map and assess seagrass abundance in Pinellas County, FL, USA. After image preprocessed with image optimization algorithms and atmosperic and sunlight correction approaches, the three sensors data were used to classify the submerged aquatic vegetation cover (%SAV cover) into 5 classes with a maximum likelihood classifier. Based on three biological metrics[%SAV, leaf area index(LAI), and Biomass] measured from the field, nine multiple regression models were developed for estimating the three biometrics with spectral variables derived from the three sensors data. Then, five membership maps were created with the three biometrics along with two environmental factors (water depth and distance-to-shortline). Finally, seagrass abundance maps were produced by using a fuzzy synthetic evaluation technique and five membership maps. The experimental results indicate that the HYP sensor produced the best results of the 5-class classification of %SAV cover (overall accuracy= 87% and Kappa=0.83 vs. 82% and 0.77 by ALI and 79% and 0.73 by TM) and better multiple regression models for estimating the three biometrics (R?=0.66, 0.62 and 0.61 for % SAV, LAI and Biomass vs 0.62, 0.61 and 0.55 by ALI and 0.58, 0.56 and 0.52 by TM) for creating seagrass abundance maps along with two evironmental factors. Combined our results demonstrate that the image optimization algorithms and the fuzzy synthetic evaluation technique were effective in mapping detailed seagrass habitats and assessing seagrass abundance with the 30-m resolution data collected by the three sensors.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58785
Title: Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables.
Author: Kimmo Nurminen, Mika Karjalainen, Xiaowei Yu, Juha Hyyppa, Eija Honkavaara.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 104-115 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Image matching, Dense point cloud, Surface model, Plot-level forest variables, Airborne laser scanning.
Abstract: Recent research results have shown that the performance of digital surface model extraction using novel high quality photogrammetric images and image matching is a highly competitive alternative to laser scanning. In this article, we proceed to compare the performance of these two methods in the estimation of plot-level forest variables. Dense point clouds extracted from aerial frame images were used to estimate the plot-level forest variables needed in a forest inventory covering 89 plots. We analyzed images with 60% and 80% forward overlaps and used test plots with off-nadir angles between 0? and 20?. When compared to reference ground measurements, the airborne laser scanning (ALS) data proved to be the most accurate: it yielded root mean square error (RMSE) values of 6.55% for mean height, 11.42% for mean diameter, and 20.72% for volume. when we applied a forward overlap of 80% , the corresponding results from aerial images were 6.77% for mean height, 12.00% for mean diameter, and 22.62% for volume. A forward overlap of 60% resulted in slightly deteriorated RMSE values of 7.55% for mean height, 12.20% for mean diameter, and 22.77% for volume. According to our results, the use of higher forward overlap produced only slightlly better results in the estimation of these forest variables. Additionally, we found that the estimation accuracy was not significantly impacted by the increase in the off-nadir angle. Our results confirmed that digital aerial photographs were about as accurate as ALS in forest resources estimation as long as terrain model was available.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58784
Title: Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies.
Author: Parinaz Rahimzadeh-Bajgiran, Aaron A Berg, Catherine Champagne, Kenji Omasa.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 94-103 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Soil moisture, Evaportive fraction, Land surface temperature, Air temperature, MODIS
Abstract: A new approach to estimate soil moisture (SM) based on evaporative fraction (EF) retrieved from optical thermal infrared MODIS data is presented for Canadian Prairies in parts of Saskatchewan and Alberta. An EF model using the remotely sensed land surface temperature (Ts)/vegetation index concept was modified by incorporating North American Regional Reanalysis (NAAR) Ta data and used for SM estimation. Two different combinations of temperature and vegetation fraction using the difference between Ts from MODIS Aqua and Terra images and Ta from NARR data (Ts-Ta Aqua-day and Ts-Ta Terra day, respectively) were proposed and the results were compared with those obtained from a previously improved model (?Ts Aqua-DayNight) as a reference. For the estimation of SM from EF, two empirical models were tested and discussed to find the most appropriate model for converting MODIS derived EF data to SM values. Estimated SM values were then correlated with in situ SM values (R?=0.42-0.77, p values < 0.04) exhibiting the possibility to estimate SM from remotely sensed EF models. The proposed Ts-Ta MODIS Aqua-day and Terra-day approaches resulted in better estimations of SM (on average higher R? values and similar RMSEs) as compared with the ?Ts reference approach indicating that the concept of incorporating NARR Ta data into Ts/Vegetation index model improved soil moisture estimation accuracy based on evaporative fraction. The accuracies of the predictions were found to be considerably better for intermediate SM values (from 12 to 22 vol/vol%) with square errors averaging below 11(vol/vol%)? . This indicates that the model needs further improvements to account for extreme soil moisture conditions. The findings of this research can be potentailly used to downscale SM estimations obtained from passive microwave remote sensing techniques.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58783
Title: Backscattering of individual LiDAR pulses from forest canopies explained by photogrammetrically derived vegetation structure.
Author: Ilkka Korpela, Aarne Hovi, Lauri Korhonen.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 81-93 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Close-range photogrammetry, Canopy imaging, Silhouette, Footprint, Echo Triggering, Waveforn LiDAR.
Abstract: In recent years, airborne LiDAR sensors have shown remarkable performance in the mapping of forest vegetation. This experimental study looks at LiDAR data at the scale of individual pulses to elucidate the sources behind interpulse variation in backscattering. Close-range Photogrammetry was used for obtaining the canopy reference measurements at the ratio scale. The experiments illustrated different orientation techniques in the field, LiDAR acquisitions and photogrammetry in both leaf-on and leaf-off conditions, and two-waveform recording LiDAR sensors. The intrafootprint branch silhouettes in zenith looking images, in which camera, footprint, and LiDAR sensor were collinear, were extracted and contrasted with LiDAR backscattering. An enhanced planimetric match (refinement of strip matching) was achieved by shifting pulses in a strip and searching for maximum correlation between the silhouette and LiDAR intensity. The relative silhouette explained upto 80-90% of the interpulse variation. We tested whether accounting for the Gaussian spread of intrafootprint irradiance would improve the correlations, but the effect was blurred by small-scale geometric noise. Accounting for reciever gain variations in the Leica ALS60 sensor data strengthened the dependences. The size of the vegetation objects required for triggering an echo constitute the complement to the actual canopy. We conclude that field photogrammetry is a useful tool for mapping forest canopies from below and that quantitative analysis is feasible even at the scale of single pulses for echanced understanding of LiDAR observations from vegetation.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58782
Title: Clustering based on eigenspace transformation-CBEST for efficient classification.
Author: Yaneli Chen, Peng Gong.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 64-80 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Land cover/use mapping, Large dataset, Landsat Thematic Mapper image, K-means, Remote sensing, Unsupervised classification.
Abstract: Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transforamtion to the dataset followed by compressing the data in the eigenspace transformation to the dataset followed by compresing the data in the eigenspace and storing them in coded matrices and vectors. The clustering process takes the advantage of the reduced size of the compressed data and thus reduces computational complexity. We name this approach Clustering Based on Eigen-space Transformation (CBEST). In our experiment a subscene of Landsat Thematic Mapper (TM) imagery, CBEST was found to be able to improve speed considerably over conventional K-means as the volume of data to be clustered increases. We assessed information loss and several other factors. In addition, we elvaluated the effectiveness of CBEST in mapping land cover/use with the same image that was acquired over Guangzhou City, South China and an AVIRIS hyperspectral image over Cappocanoe County, Indiana. Using reference data we assessed the accuracies for both CBEST and conventional K-means and we found that the CBEST was not negatively affected by information loss during compression in practice. We discussed potential applications of the fast clustering algorithm in dealing with large datasets in remote sensing studies.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58781
Title: Hyperspectral image noise reduction based on rank-1 tensor decomposition.
Author: Xian Guo, Xin Huang, Liangpei Zhang, Lefei Zhang.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 50-63 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Tensor decomposition, Rank-1 tensor, Hyperspectral image, Noise reduction, Rank estimation.
Abstract: In this study, a novel noise reduction algorithm for hyperspectral imagery (HSI) is proposed based on high-order rank-1 tensor decomposition. The hyperspectral data cube is considered as three-order tensor that is able to jointly treat both the spatial and spectral modes. Subsequently, the rank-1 tensor decomposition (R1TD) algorithm is applied to the sensor data, which takes into account both the spatial and spectral information of the hyperspectral data cube. A noise-reduced hyperspectral image is then obtained by combining the rank-1 tensors using an eigenvalue intensity sorting and reconstruction technique. Compared with the existing noise reduction methods such as the conventional channel-by-channel approaches and the recently developed multidimensional filter, the spatial-spectral adaptive total variation filter, experiments with both synthetic noisy data and real HSI data reveal that the proposed R1TD algorithm significantly improves the HSI data quality in terms of both visual inspection and image quality indices. The subsequent image classification results further validate the effectiveness of the posed HSI noise reduction algorithm.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58780
Title: Classifying a high resolution image of an urban area using super-object information.
Author: Brain Johnson, Zhixiao Xie.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 40-49 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Segmentation, Classification, Urban, High resolution, Land cover, Scale, Contextual.
Abstract: In this study, a multi-scale approach was used for classifying land cover in a high resolution image of an urban area. Pixels and image segments were assigned the spectral, texture, size, and shape information of their super-objects (i.e. the segments that are located within) from coarser segmentations of the same scene, and this set of super-object information was used as aditional input data for image classification. The accuracies of classifications that included super-object variables were compared with the classification accuracies of image segmentations that did not include super-object informtion was 78.11% and 0.727%, respectively. When single pixels or fine-scale image segments were assigned the statistics of the super-objects prior to classification, overall accuracy increased to 84.42% and the kappa coefficient increased to 0.804.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58779
Title: Amodified stochastic neighbor embedding for multi-feature dimension reduction of remote sensing images.
Author: Lefei Zhang, Liangpei Zhang, Dacheng Tao, Xin Huang.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 30-39 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Hyperspectral image, Multiple Features, Stochastic neighbor embedding, Dimension reduction, Classification.
Abstract: In autoamted remote sensing based on image analysis, it is important to consider the multiple features of certain pixel, such as the spectral signature, morphological property, and shape feature, in both the spatial and spectral domains, to improve the classification accuracy. Therfore, it is essential to consider the complementary properties of the different features and combine them in order to obtain an accurate classification rate. In this paper, we introduce a modified stochastic neighbor embedding (MSNE) algorithm for multiple features dimension reduction (DR) under probability preserving projection framework. For each feature, a probability distribution is constructed based on t-distributed stochastic neighbor embedding (t-SNE), and we then alternately solve t-SNE and learn the optimal combination co-efficients for different features in the proposed multiple features DR strategies, the suggested algorithm utilizes both the spatial and spectral features of a pixel to achieve a physically meaningful low-dimensional feature representation for the subsequent classification, by automatically learning a combination coefficient for each feature. The classification results using hyperspectral remote sensing images (HSI) show that MSNE can effectively improve RS image classification performance.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58778
Title: Error analysis of satellite attitude determination using a vision-based approach.
Author: Ludovico Carozza, Alessandro Bevilacqua.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 19-29 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Vision, Image registration, Error analysis, Accuracy analysis, Satellite, Feature tracking.
Abstract: Improvements in communication and processing technologies have opened the doors to exploit on-board cameras to compute objects spatial attitude using only the visual information from sequences of remote sensed images. The strategies and the algorithmic approach used to extract such information affect the estimation accuracy of the three-axis orientation of the object.
This work presents a method for analyzing the most relevant error sources, including numerical ones, possible drift effects and their influence on the overall accuracy, referring to vision-based approaches. The method in particular focuses on the analysis of the image registration algorithm, carried out through onpurpose simulations. The overall accuracy has been assessed on a challenging case study, for which accuracy represents the fundamental requirement. In particular, attitude determinatiion has been analysed for small satellites, by comparing theoretical findings to metric results from simulations on realistic groundtruth data. Significant laboratory experiments, using a numerical control unit, have further confirmed the outcome.
We believe that our analysis approach, as well as our findings in terms of errors characterization, can be useful at proof-of-concept design and planning levels, since they emphasize the main sources of error for visual based approaches employed for satellite attitude estimation. Nevertheless, the approach we present is also of general interest for all the affine applicative domains which require an accurate estimation of three-dimensional orientation parameters (i.e., robotics, airborne stabilization).
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None