ID: 56242
Title: Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories
Author: Tapas R Martha, Norman Kerle, Cees J van Westen, Victor Jetten, K Vinod Kumar
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 67, January 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Feature extraction, OOA, GEOBIA, Cartosat-1, Landslide hazard and risk, disaster
Abstract: Object-oriented analysis (OOA) has been demonstrated to produce more accurate results than pixel-based image processing. Studies carried out by previous researchers have shown how landslide inventories can be prepared from multispectral satellite images using OOA. However, panchromatic images are frequently the only data available after a landslide event. Furthermore, preparation of historical inventories relies on the analysis of satellite images and aerial photograph acquired over past few decades that are also mostly only available in black and white. In such cases the methodology developed using multispectral data cannot be used directly due to limited spectral information, in particular in near-infrared bands. In this paper we present a new methodology that addresses some of these issues. Using high resolution panchromatic images from Cartosat-1 (2.5 m) and IRS -1D (5.8 m), and a 10 m gridded DTM extracted from Cartosat -1, we developed a new approach which uses change detection techniques and a global contextual criteria in an object-based environment to detect and classify landslides into five different types. Continues time series images from 1998 to 2006 were used to prepare annual landslide inventories in a highly rugged Himalayan terrain. The maximum and minimum detection percentages achieved for all landslides are 96.7% and 71.5%, respectively, with corresponding quality percentages of 88.1% and 55.3%, respectively. However, the lack of spectral information proved to be a hurdle resulting in a high branching factor that indicates that further work is required to eliminate false positives. Nevertheless, the method was able to create much needed historical landslide inventories, which are critical for landslide hazard and risk assessment studies.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56241
Title: An assessment of the effectiveness of a random forest classifier for land-cover classification
Author: V F Rodriguez-Galiano, B Ghimire, J Rogan, M Chica-Olmo, J P Rigol-Sanchez
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 67, January 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Remote sensing, Machine learning, classification, random forest, land-cover, Landsat Thematic Mapper
Abstract: Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluaated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parameteric nature; high classifiction accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning.
In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classfications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significant level.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56240
Title: Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions
Author: Ainong Li, Jingang Jiang, Jinhu Bian, Wei Deng
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 67, January 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Remote sensing, classification, matter element model, associated function, mountainous region
Abstract: That the multi-source remote sensing information integrates knowledge-based geospatial constraints to develop efficient and practical Land cover classification algorithm has become one of the most important developing directions in the field of remote sensing ground object classification. Remote sensing classification is a strictly incompatible problem, but the spectra distribution of remote sensing data has compatible attributes especially in mountainous regions, and such contradiction is one of the main reasons leading to uncertainties in remote sensing classification. In this paper, the remote sensing spectra feature compatible information is transformed into the probability of the association degree firstly, and then the matter-element theory is introduced to establish models to achieve the integrated classification of multisource data to fuse knowledge-based geographical constrained condition probability. Taking the grassland -wetland fragile ecosystem in Ruoergai plateau of China as a case study, this paper selected the multi-source data including images of Landsat TM and CBERS, ASTER-GDEM and MODIS-NDVI to construct a comprehensive classifier, in which the relationship between topography and land cover, and the prior knowledge on vegetation growth difference were taken as constraints to support the decision - making. The classification accuracy was evaluated by a field investigation and existing land cover map. The test result shows that, the overall accuracy (89.89%) and Kappa coefficient (0.8870) are better than those derived by the Maximum Likelihood method. It indicates that the proposed classification method is not subject to the dimensionality and form of data sources, and it can integrate the data source information to improve the classification accuracy, so that it is very useful to apply multi-source data adn prior knowledge to land cover classification in mountainous regions.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56239
Title: Combination of individual tree detection and area-based approach in imputation of forest variables using airborne laser data
Author: Mikko Vastaranta, Ville Kankare, Markus Holopainen, Xiaowei Yu, Juha Hyyppa, Hannu Hyyppa
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 67, January 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Laser scanning, forest inventory, field measurements
Abstract: The two main approaches to deriving forest variables from laser-scanning data are the statistical area based approach (ABA) and individual tree detection (ITD). With ITD it is feasible to acquire single tree information, as in field measurements. Here, ITD was used for measuring training data for the ABA. In addition to automatic ITD (ITDauto), we tested a combination of ITDauto and visual interpretation (ITDvisual). ITDvisual had two stages: in the first, ITDauto was carried out and in the second, the results of the ITDauto were visually corrected by interpreting three-dimensional laser point clouds. The field data comprised 509 circular plots (r = 10m) that were divided equally for testing and training. ITD-derived forest variables were used for training the ABA and the accuracies of the k-most similar neighbor (k-MSN) imputations were evaluated and compared with the ABA trained with traditional measurements. The root-mean-squared error (RMSE) in the mean volume was 24.8%, 25.9%, and 27.2% with the ABA trained with field measurements, ITDauto and ITDvisual, respectively. When ITD methods were applied in acquiring training data, the mean volume, basal area, and basal area-weighted mean diameter were underestimated in the ABA by 2.7-9.2%. This project constituted a pilot study for using ITD measurements as training data for the ABA. Further studies are needed to reduce the bias and to determine the accuracy obtained in imputation of species-specific variables. The method can be applied in areas with sparse road networks or when the costs of fieldwork must be minimized.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56238
Title: Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use
Author: Yuyu Zhou, Qihao Weng, Kevin R Gurney, Yanmin Shuai, Xuefei Hu
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 67, January 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Anthropogenic heat discharge, building energy use, multi-scale, urban heat island, Urban remote sensing
Abstract: This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor ' s parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use in scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56237
Title: 3D building reconstruction based on given ground plan information and surface models extracted from spaceborne imagery
Author: Frederik Tack, Gurcan Buyuksalih, Rudi Goossens
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 67, January 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: 3D city modeling, DSM, photogrammetry, High resolution satellite imager, 2D ground plans
Abstract: 3D surface models have gained field as an important tool for urban planning and mapping. However, urban environments have a complex nature to model and they provide a challenge to investigate the current limits of automatic digital surface modeling from high resolution satellite imagery. An approach is introduced to improve a 3D surface model, extracted photogrammetrically from satellite imagery, based on the geometric building information embodied in existing 2D ground plans. First buildings are clipped from the extracted DSM based on the 2D polygonal building ground plans. To generate prismatic shaped structures with vertical walls and flat roofs, building shape is retrieved from the cadastre database while elevation information is extracted from the DSM. Within each 2D building boundary, a constant roof height is extracted based on statistical calculations of the height values. After buildings are extracted from the initial surface model, the remaining DSM is further processed to simplify to a smooth DTM that reflects bare ground, without artifacts, local relief, vegetation, cars and city furniture. In a next phase, both models are merged to yield an integrated city model or generalized DSM. The accuracy of the generalized surface model is assessed according to a quantitative-statistical analysis by comparison with two different types of reference data.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56236
Title: Determining fast orientation changes of multi-spectral line cameras from the primary images
Author: Jurgen Wohlfeil
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 67, January 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Line camera, exterior orientation, feature matching, optical information, sparse bundle adjustment
Abstract: Fast orientation changes of airborne and spaceborne line cameras cannot always be avoided. In such cases it is essential to measure them with high accuracy to ensure a good quality of the resulting imagery products. Several approaches exist to support the orientation measurement by using optical information received through the main objective/telescope. In this article an approach is proposed that allows the determination of non-systematic orientation changes between every captured line. It does not require any additional camera hardware or onboard processing capabilities but the payload images and a rough estimate of the camera ' s trajectory. The approach takes advantages of the typical geometry of multi-spectral line cameras with a set of linear sensor arrays for different spectral bands on teh focal plance. First, homologous points are detected within the heavily distorted images of different spectral bands. With their help a connected network of geometrical correspondences can be built up. The network is used to calculate the orientation changes of the camera with the temporal and angular resolution of the camera. The approach was tested with an extensive set of aerial surveys covering a wide range of different conditions and achieved precise and reliable results.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56235
Title: Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction
Author: Wai Yeung Yan, Ahmed Shaker, Ayman Habib, Ana Paula Kersting
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 67, January 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: LiDAR, Geometric calibration, Radiometric correction, intensity data, classification
Abstract: Airborne light detection and ranging (LiDAR) systems are used to measure the range (distance from the sesor to the target) and the intensity data (the backscattered energy from the target). LiDAR has been used extensively to model the topography of the Earth surface. Nowadays, LiDAR systems operating in the near-infrared spectral range are also gaining high interest for land cover classification and object recognition, LiDAR system requires geometric calibration (GC) and radiometric correction (RC) in order to maximize the benefit from the collected LiDAR data. This paper evluates the impact of the GC and the RC of the LiDAR data on land cover classification. The procedure includes the use of a quasi-rigorous method for the GC and the radar (range ) equation for the RC of the LiDAR data. The geometric calibration procedure is used to adjust the coordinates of the point cloud by removing the impact of biases in the system parameters as well as deriving corrected ranges and scan angles (in the absence of the system ' s raw measurements) for the RC process. The geometrically calibrated ranges and scan angles are then used to correct the intensity data from the atmospheric attenuation and background backscattering based on the radar (range) equation. The atmospheric attenuation, which has not been fully addressed in the previous literature, is modeled by considering the parameters of absorption as well as scattering derived from existing empirical models and public (free) molecular absorption database. A LiDAR dataset covering an urban area is used to evaluate the effect of the GC and RC of the LiDAR data on land cover classification. The results are evaluated using a true ortho-rectified aerial image acquired during the same flight mission. The classification results show an accuracy improvement of about 9.4 -12.8% for the LiDAR data used after the GC and RC. The study provides a practical approach for the LiDAR system GC and RC which can be implemented by most of the data end users. The outcome from this research work is a data processing tool that maximizes the benefits of using the intensity data for object recognition and land cover classification, which will be quite important for LiDAR data users.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56234
Title: A new shape from shading technique with application to Mars express HRSC images
Author: R O ' Hara, D Barnes
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 67, January 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Shape from shading, SFS, HRSC, photoclinometry, Mars
Abstract: We have developed a new optimisation based shape from shading algorithm which is able to make use of sophisticated camera and reflectance models and does not require a good initialising surface. Surface shape consistent with ground truth is obtained when the technique is applied to both synthetic rendered surfaces and real images captured by the Mars Express orbiter and HRSC instrument. The obtained surfaces provide improved fine surface detail over that found using stereo techniques and demonstrate the applicability of the technique to real images.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56233
Title: Infuence of solar elevation in radiometric and geometric performance of multispectral photogrammetry
Author: Eija Honkavaara, Lauri Markelin, Tomi Rosnell, Kimmo Nurminen
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 67, January 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Point cloud, radiometry, matching, geometry photogrammetry
Abstract: Solar elevation is an important factor in passive, airborne data collection. The minimum solar elevation allowed in missions for topographic mapping is typically 300 from the horizon. A general hypothesis is that the new, high dynamic range, digital large-format photogrammetric sensors allow for high data quality, even with lower solar elevations, which would improve the feasibility and cost-efficiency of photogrammetric technology in various applications. Objectives of this study were to investigate theoretically and empirically the impacts of solar elevation in modern photogrammetric processes. Two cutting-edge aspects of novel photogrammetric technology were considered: point cloud creation by automatic image matching and reflectance calibration of image data. For the empirical study, we used image data collected by a large-format photogrammetric camera, Intergraph DMC, with low (25-280C) and medium (44-480) solar elevations form 2, 3 and 4 km heights. We did not detect negative influences of decreasing solar elevation during our general evaluations: an analysis of image histograms showed that the ranges of digital numbers could be tuned to similar levels with exposure settings, and evaluations of density and the accuracy of point clouds did not show any reduction of quality. We carried out detailed evaluations in forests, roads and fields. Our results did not indicate deterioration of the quality in sun-illuminated areas with decreasing solar elevation. In shadowed areas, we observed that the variation of image signal was reduced in comparison to sun-illuminated areas and emphasized the issue of complication of reflectance calibration. Artefacts appeared in automatically generated point clouds in areas shadowed by trees, which we observed in flat objects as up to 3 times increased random height variation and decreased success in measuring the terrain surface. Our results also showed that the overall performance of point cloud generation was high. Typically, point clouds could be derived even from a single stereo model with the point density corresponding to the GSD, but some expected and unexpected failures also appeared. The height accuracy was dependent on the object properties and the intersection geometry; the height accuracy was 0.5-2 times GSD at well defined objects. Our conclusions were that in the future it is of increasing importance to quantify the sensitivity of different methods on the radiometric properties of the image data. It is also important to develop interpretation methods that are not sensitive to shadows, in order to enable optimal use of photogrammetric technology in normal to rapid response applications.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56232
Title: Parameter-free ground filtering of LiDAR data for automatic DTM generation
Author: Domen Mongus, Borut Zalik
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 67, January 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: LiDAR, Digital terrain model, Classification
Abstract: This paper considers a new method for the automatic generation of digital terrain models from LiDAR data. The method iterates a thin plate spline interpolated surface towards the ground, while points ' residuals from the surface are inspected at each iteration, with a gradually decreasing window size. Top-hat transformation is used to enhance discontinuities caused by surface objects. Finally, parameter - free ground point filtering is achieved by automatic thresholding based on standard deviation. The experiments show that his method correctly determines DTM even in those cases of more difficult terrain features. The expected accuracy of ground point determination on those datasets commonly used in practice today is over 96%, while the average total error produced on the ISPRS benchmark dataset is under 6%>
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56231
Title: Sediment yield estimation for watershed prioritization: a remote sensing study
Author: Rabindra Kumar Das
Editor: Prof Natarajan Gajendran
Year: 2012
Publisher: Indian Society for Education and Environment (iSee), Vol 5, Issue 3, March 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Indian Journal of Science and Technology
Keywords: Sediment yield, soil erosion, watershed, remote sensing, run-off, Geographic Information Systems (GIS)
Abstract: Watershed prioritization on the basis of soil erosion has become inevitable component of watershed management in order to conserve this precious natural resource. The major factors contributing to the soil erosion include the land use, the soil, the slope, the climate and the land management practices. These factors exhibit spatial variability due to watershed heterogeneity. Therefore, the watershed is usually discretized into sub-areas exhibiting some sorts of homogeneity. In this study the watershed is subdivided into a number of micro-watersheds and the emerging technology of remote sensing as data source was used for obtaining reliable and real time information on land use and soil. The information on slope was obtained from topographic maps. The land-use, the soil and the slope are used in a simple mathematical model for estimating sediment yeild of each micro-watershed and thereafter the micro-watersheds were statistically classified into four priority classes. The advantages of using satellite images are discussed. It is recommended to use images with resolution of 2-5 m (e.g. SPOT 5) as a cost-effective data source for sediment yield estimation and other watershed management activities.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 56230
Title: Fuzzy and anti fuzzy ternary hypergroups
Author: Bijan Davvaz
Editor: Prof Natarajan Gajendran
Year: 2012
Publisher: Indian Society for Education and Environment (iSee), Vol 5, Issue 3, March 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Indian Journal of Science and Technology
Keywords: Fuzzy subset, subhypergroup, Ternary hypergroup
Abstract: The notion of ternary hypergroup is a generalization of the notion of hypergroup in the sense of Marty. In this paper, we study the concepts of fuzzy and anti fuzzy ternary subhypergroups of a ternary hypergroup and we discuss some properties of them.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 56229
Title: Optimal placement and estimation of DG capacity in distribution network ' s using genetic algorithm-based method
Author: Ali Aref, Mohsen Davoudi and Majid Davoudi
Editor: Prof Natarajan Gajendran
Year: 2012
Publisher: Indian Society for Education and Environment (iSee), Vol 5, Issue 3, March 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Indian Journal of Science and Technology
Keywords: Distributed generation (DG), distribution network, opimization, genetic algorithm
Abstract: Distributed Generation (DG) unlike centralized electrical generation aims to generate electrical energy on small scale as near as possible to the load centers, interchanging electric power with the network. Moreover, DGs influence distribution system parameters such as reliability, loss reduction and efficiency while they are highly dependent on their situation in the distribution network. This paper focuses on optimal placement and estimation of DG capacity for installation and takes more number of significant parameters into account compare to the previous studies which consider just a few parameters for their optimizatin algorithms. Some of the so-called cost parameters are loss reduction, voltage profile improvement, environemental effects, installation and exploitation and maintenance expenses and costs of load prediction of each bus. Using an optimal Genetic Algorithm, proposed a destination function has been optimized which inclues all of the cost parameters. This method is also capable of changing the weights of each cost parameter in the destination function of the Genetic Algorithm and the matrix of coefficients in the DIGSILENT environment. The cost parameters are variables dependetn on the status and position of each bus in the network, putting forth an optimal DG placement. The proposed method has been applied and simulated on a sample IEEE 13-bus network. The obtained results show that nay change in the weight of each parameter in the destination function of the Genetic Algorithm and in the matrix of coefficients leads to a meaningful change in the location and capacity of the prospective DG in the distribution network.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 56228
Title: Using nodal marginal loss coefficients for transmission loss allocation
Author: Rahmat Azami, Mohammad Reza Behnamfar, Nosratallah Mohammadbaigi
Editor: Prof Natarajan Gajendran
Year: 2012
Publisher: Indian Society for Education and Environment (iSee), Vol 5, Issue 3, March 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Indian Journal of Science and Technology
Keywords: Transmission allocation, pool based electricity market, Admittance equivalent circuit, loss matrix
Abstract: In this paper, a method is proposed to assign transmission losses costs in pool-based electricity markets. This method is based on using the impedance matrix of the network and partial derivatives of the active power losses respect to bus currents coefficients. After performing load flow equations, the losses of each bus are calculated using the impedance matrix of the network and the injected currents from each bus. These losses are properly and faily shared between network buses for fair loss allocation, in proportion to partial derivatives of the active power losses respect to bus currents coefficients. Finally, this method has been tested on a benchmark IEEE 14-bus network and the results are compared with the other existing methods.
Location: 241
Literature cited 1: None
Literature cited 2: None