Proceedings of SPIE - The International Society for Optical Engineering (1996756X)3957pp. 398-402
Virtual Reality (VR) is a possible which brings users to the reality by computer and Virtual Environment (VE) is a simulated world which takes users to any points and directions of the object. VR and VE can be very useful if accurate and precise data are used, and allows users to work with realistic model. Photogrammetry is a technique which is able to collect and provide accurate and precise data for building 3D model in a computer. Data can be collected from various sensors and cameras, and methods of data collector are vary based on the method of image acquiring. Indeed VR includes real-time graphics, three-dimensional model, and display and it has application in the entertainment industry, flight simulators, industrial design. Above definitions describe the relationship between VR and VE with photogrammetry. This paper describes a reliable and precis method of data acquiring based on close range photogrammetry for building a VR model. The purpose of this project is to make a real possibility for seismic designers to investigate all effects of shaking on a real building. Minar Gonban is an ancient building with two amazing minarets at Esfehan IRAN. While one of them was shaken the second one started to shake. The project is fulfilled on this building because building simply can be shaken and its effects can be investigated. The building was photographed by multiple movie cameras and photo cameras. Sequence images were restored in a computer for creating sequence models of building. A VR model is builded based on extracted data from photogrammetry images. The developed VR model is precise and reliable and provides real possibility for users to investigate the effects of shaking on the building. The developed VR model is based on real data. The results verify a reliable VR can be useful for human life because one of its application can help to investigate effects of earthquake on the building and duce its casualty.
CTIT workshop proceedings series (16821750)35
In the context of the analysis of remotely sensed data the question arises of how to analyse large volumes of data. In the specific case of agricultural fields in flat areas these fields can often be modelled in terms of geometric primitives such as triangles and rectangles. In this case the options are classical i.e. bottom-up, starting at the pixel level and resulting in a segmented, labelled image or top-down, starting with a model for image partitioning and resulting in a minimum cost estimation of shape hypotheses with corresponding parameters. Standard bottom-up classification methods usually concern the pixel as a main element and try to label the pixel individually. But various errors are involved in the image analysis with these methods. Mixed pixels, simplicity of the basic assumptions in the classification algorithms, sensor effects, atmospheric effects, and radiometric overlap of land cover objects lead to the wrong detection in image analysis. In this paper we propose a Model-Based Image Analysis (MBIA) approach to analyze the remotely sensed data. In this manner using the available knowledge about the remote sensing system we generate some hypothesis maps and then test them using the radiometric measurements (images). In order to test the method we used the boundaries of the agricultural fields stored in a GIS to model the objects in the scene. The results of the method have been compared with the result of a traditional Maximum-Likelihood classification and a standard Object-Based Classification using the boundaries. Using this approach we could reach to the 94% overall accuracy. © 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved.
CTIT workshop proceedings series (16821750)35
Traditionally accuracy assessment of the classification results uses some collected reference data (ground truth). Ground truth collection is a time-consuming and money-swallowing activity and usually can not be done completely. Uncertainty is an important subject in remote sensing that can appear and be increased sequentially in a chain of remote sensing from data acquisition, geometric and radiometric processing to the information extraction. Conceptually the relation between uncertainty and accuracy is an inverse relation. This relation can aid us to construct a relation between accuracy measures and uncertainty related measures. In this paper we investigate this relation using the generated synthetic images (for the sake of the reliability of the obtained results) and try to find an uncertainty related measure that has a strong relationship with the accuracy parameters like overall accuracy.We have found that among the uncertainty measures the mean quadratic score has the strong and reliable relationship with the commonly used accuracy measures. This relationship can be a good basis for the future investigations that lead to the classification based accuracy measures and avoiding some problematic data related issued of ground truth data collection. © 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved.
CTIT workshop proceedings series (16821750)35
Accuracy assessment is an important step in the process of analyzing remote sensing data. It determines the value of the resulting data to a particular user, i.e. the information value. Remote sensing products can serve as the basis for political as well as economical decisions. Users with a variety of applications should be able to evaluate whether the accuracy of the map suits their objectives or not. In the conventional accuracy assessment an error matrix and some accuracy measures derived from it are used. An error matrix is established using some known reference data and corresponding classified data. There are various factors that affect the performance of the accuracy assessment by influencing the error matrix through out the ground truth data collection. In practice, the techniques are of little value if these effective factors are not considered. In this paper the necessity considerations for accuracy assessment including the sampling schemas and the sample size for these sampling methods are studied. Also the factors that affect selecting and applying appropriate sampling schemas and sample size are investigated. For this study numbers of synthetic images and one real image and some reference data are used. Sensitivity of the various sampling schemas has been investigated using the synthetic images and using the real image the obtained results have been confirmed. The results represent that depend on specific conditions such as type and size of the study region and object characteristics, different sampling methods and sample sizes are preferred. © 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved.
Survey Review (17522706)38(296)pp. 165-173
It is possible to use single frequency GPS receivers to estimate the Total Electron Content (TEC). In this research, we improved an algorithm presented by Giffard [2], that is based on a least squares solution. We investigated the effect of the use of different weights (elevation of satellites, signal to noise ratio, combination of elevation and signal to noise ratio) and different block sizes on TEC estimates. We found that these parameters had a significant impact on TEC estimates based on this algorithm. Our research is based on observations at the GPS site of the Esfahan University made with single frequency 12-channel Leica System 500 receivers.
Remote Sensing of Environment (00344257)106(2)pp. 190-198
Surface emissivity estimation is a significant factor for the land surface temperature estimation from remotely sensed data. For fully vegetated surfaces, the emissivity estimation is performed in a simple manner since the emissivity is relatively uniform. However, for arid land with sparse vegetation, the estimation is more complicated since the emissivity of the exposed soil and rock is highly variable. In this study, mean and difference emissivity for bands 31 and 32 of MODIS sensor have been derived based on NDVI values. First, the NDVI thresholds have been determined to separate bare soil, partially vegetated soil and fully vegetated land. Then regression relations have been derived to estimate mean and difference emissivity of the bare soil samples and partially vegetated surfaces. A constant emissivity is also used for fully vegetated area. Along with the correlations, standard deviations of the regression relations have been examined for a set of representative soil types. Standard deviations smaller than 0.003 in mean emissivity and smaller than 0.004 in difference emissivity are resulted in regression linear relations. Evaluation of the NDVI derived regression relations has been performed using the results of MODIS Day/Night Land Surface Temperature (LST) algorithm on a pair of MODIS images. Using around 45,500 pixels with different soil and land cover types, emissivity of each pixel in bands 31 and 32 have been estimated. The calculated emissivities have been compared with emissivities calculated by MODIS Day/Night LST algorithm. Biases and standard deviations of NDVI-based relations show relatively high agreement for mean and difference emissivity relations with Day/Night method results. It may be concluded that the proposed algorithm can be used as a rather simple alternative to complex emissivity estimation algorithms. © 2006 Elsevier Inc. All rights reserved.
Photogrammetric Engineering and Remote Sensing (00991112)74(5)pp. 637-646
Estimation of land surface temperature and emissivity has taken on a great deal of importance in recent remote sensing studies. The estimation of temperature and emissivity from thermal radiation observations is involved with an under-determined equation set. In this study, an approach is proposed to overcome the problem based on statistical theory of observations and error propagation. First, the under-determined radiance equations have been completed using two NDVI-based equations for the mean and difference emissivities as constraint equations. The two added constraint equations provide the possibility of weighted least squares solution to estimate temperature and emissivity from the over-determined equation set simultaneously. The weights have been calculated based on the uncertainty of each of the equations. The weighting basis of the proposed approach allows statistical control on the uncertainties. The advantages of the weighted least squares solution which is contributed by this study are weighted observations used in the solution, the uncertainty considerations of the used observations, uncertainty propagation control, statistical standard deviation estimation for the unknowns, statistical quality control criteria, and the opportunity of systematic error detection. The numerical efficiency of the proposed approach is examined using a great number of simulated sample data. Then, the proposed approach is validated using the in situ measurements of land surface temperature. The validations accompanied by some statistical tests represent the acceptable performance and accuracy of the proposed approach (approximately 0.5°K for LST standard deviation and approximately 0.0075 for standard deviation of the bands 31 and 32 emissivities). In addition, the simplicity and robustness of the proposed approach may be regarded as a considerable achievement. © 2008 American Society for Photogrammetry and Remote Sensing.
CTIT workshop proceedings series (16821750)37pp. 823-827
In this paper we present and develop a set of algorithms, mostly based on morphological operators, for automatic colonic polyp detection applied to computed tomography (CT) scans. Initially noisy images are enhanced using Morphological Image Cleaning (MIC) algorithm. Then the colon wall is segmented using region growing followed by a morphological grassfire operation. In order to detect polyp candidates we present a new Automatic Morphological Polyp Detection (AMPD) algorithm. Candidate features are classified as polyps and non-polyps performing a novel Template Matching Algorithm (TMA) which is based on Euclidean distance searching. The whole technique achieved 100% sensitivity for detection of polyps larger than 10 mm and 81.82% sensitivity for polyps between 5 to 10 mm and expressed relatively low sensitivity (66.67%) for polyps smaller than 5 mm. The experimental data indicates that our polyp detection technique shows 71.73% sensitivity which has about 10 percent improvement after adding the noise reduction algorithm.
CTIT workshop proceedings series (16821750)37pp. 523-528
One of the most important parameters in all surface-atmosphere interactions (e.g. energy fluxes between the ground and the atmosphere) is atmospheric water vapor. It is also an indicator among others to modeling the energy balance at the Earth's surface. Total atmospheric water vapor content is an important parameter in some remote sensing applications especially land surface temperature (LST) estimation. As such, total atmospheric water vapor content and LST are used as key parameters for a variety of environmental studies and agricultural ecological applications. Estimation of an accurate LST requires the atmospheric water vapor content estimation. This study is concerned with retrieving total atmospheric water vapor content (W) using Moderate Resolution Imaging Spectrometer (MODIS). We have used a ratio technique to estimate the column water vapor based on MODIS data. However Atmospheric Infrared Sounder (AIRS) column water vapor and AIRS MMR near surface water vapor have been taken into account to calculate coefficients of the equation in the ratio technique. Then the accuracy of the results was examined using independent data set. It is concluded in this study that MODIS data is appropriate in mapping water vapor content as a suitable alternative to meteorological stations measurement data. © 2008 International Society for Photogrammetry and Remote Sensing. All rights reserved.
Advanced Materials Research (discontinued) (16628985)301pp. 388-396
Cyclic axial loads in steel tubular might lead to local buckling, wrinkling and accumulation of plastic strains in the tube. During their life time steel tubes may also experience different types of material loss such as corrosion or thinning. This paper deals with the effects of corrosion defects on the strain ratcheting response of steel tubes. Small scale un-corroded and corroded tubular specimens have been tested under monotonic and cyclic axial loads. Optical system ATOS has been used for 3D surface acquisition and reconstruction of the tested specimen and to evaluate their strain ratcheting and wrinkling response. This is a camera-based triangulation system. A processing unit employs optical transform equations to automatically and with a great accuracy calculate 3D coordinates for every pixel of camera. Depending on camera resolution as an effect of such a scan a cloud of up to 4 million points has been obtained for every single measurement. From the results, it has been noticed that the possibility of ratcheting or progressive plastic failure substantially increases by the presence of the corrosion defects. With the corroded specimens, the strain ratcheting behaviour in the defected zone has been distinctively different from that in the perfect zones.
Journal of the Earth and Space Physics (2538371X)37(1)pp. 11-24
The least squares harmonic estimation is applied to the hourly time-series of Total Electron Contents (TEC) derived from ionospheric models using seven years of GPS observations processed by Bernese software. The frequencies of dominant spectral components in the spectrum are estimated. We observe significant periodic patterns with periods of 24 h and its fractions 24h/n, n=2,.,11, which are the well-known Fourier series decomposition of the diurnal periodic pattern of the ionospheric variations. The principal component with daily signal is due to the day-night variation of TEC values. The semidiurnal and tri-diurnal components can be explained by the substorm signatures in both auroral electrojet (in layer E) and ring current variations (related to magnetosphere at low latitudes) and tidal effects. Also, the spectrum shows the well-known 27-day period of solar cycle variations. We observe annual, semi-annual and tri-annual signals in the series. The detected signals are then applied to perform an ionospheric prediction. The results indicate that a substantial part (in the absolute sense) of the TEC values can be predicted using this base function, and an undetectable part remains as disturbed noise which can exceed 20 TEC units for the disturbed ionosphere. In comparison with the standard Klobuchar model, the model presented in this contribution will significantly improve the single frequency GPS positioning accuracy.
Abzal, A.,
Varshosaz m., M.,
Saadatseresht, M. Photogrammetric Record (14779730)26(135)pp. 293-306
In this paper a new triangulation-based laser scanner is presented which has a simple, yet strong, flexible and low-cost structure. A digital camera and three laser line projectors are the main components of the system. One of the laser projectors is positioned vertically, while the other two are horizontal. The former scans the object, whereas the latter two establish an optical frame which is used, in part, to define the plane containing the vertical laser projector at each step of scanning. At each step, an image is taken which includes the object along with the projected laser lines. By intersecting the vertical and horizontal lines a couple of points are formed which, along with the calibration information of the system, enable the extraction of the object coordinates. Results of the tests carried out show that by using an optical frame of this nature, the process of scanning is greatly facilitated. That is, the scanner can easily be used to scan objects of different size and dimensions. Within the current configuration, the system enables measurements with an accuracy of 1/1600. Also, as the system has a rigorous basis, its accuracy can be increased if improved hardware is provided. © 2011 The Authors. The Photogrammetric Record © 2011 The Remote Sensing and Photogrammetry Society and Blackwell Publishing Ltd.
Sattari, M.,
Shahbazi, M.,
Homayouni, S.,
Saadatseresht, M.,
Shahbazi, M.,
Sattari, M.,
Homayouni, S.,
Saadatseresht, M. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 148-155
This paper describes a method for detecting and recognizing traffic signs by integrating the range and intensity images of a Time-of-flight camera, based on Photonic Mixer Device (PMD) technology, with images of a standard digital camera. The reflectivity of signs surfaces along with background suppression ability and active sensing of the PMD camera make the signs sharply visible in intensity images. Besides the image descriptors, utilizing the object-based information provides robust and reliable detection and recognition. The overall acquisition system and proposed technique overcome the conventional illumination, disorientation and scaling problems in detection and recognition process. The method of this paper is implemented and evaluated on data acquired by a multi-sensor mobile mapping system.
Shahbazi, M.,
Homayouni, S.,
Saadatseresht, M.,
Sattari, M. Sensors (14248220)11(9)pp. 8721-8740
Time-of-flight cameras, based on Photonic Mixer Device (PMD) technology, are capable of measuring distances to objects at high frame rates, however, the measured ranges and the intensity data contain systematic errors that need to be corrected. In this paper, a new integrated range camera self-calibration method via joint setup with a digital (RGB) camera is presented. This method can simultaneously estimate the systematic range error parameters as well as the interior and external orientation parameters of the camera. The calibration approach is based on photogrammetric bundle adjustment of observation equations originating from collinearity condition and a range errors model. Addition of a digital camera to the calibration process overcomes the limitations of small field of view and low pixel resolution of the range camera. The tests are performed on a dataset captured by a PMD[vision]-O3 camera from a multi-resolution test field of high contrast targets. An average improvement of 83% in RMS of range error and 72% in RMS of coordinate residual, over that achieved with basic calibration, was realized in an independent accuracy assessment. Our proposed calibration method also achieved 25% and 36% improvement on RMS of range error and coordinate residual, respectively, over that obtained by integrated calibration of the single PMD camera. © 2011 by the authors; licensee MDPI, Basel, Switzerland.
Photogrammetric Record (14779730)27(139)pp. 330-359
In this paper, a multi-resolution hybrid approach is proposed for the reconstruction of building models from point clouds of lidar data. The detection of the main roof planes is obtained through a polyhedral approach, whereas the models of appended parts, in this case the dormers, are reconstructed by adopting a model-driven approach. Clustering of the roof points in a multi-resolution space is based on the fuzzy c-mean in the polyhedral section of this hybrid approach. A weighted plane algorithm is developed in order to determine the planes of each cluster. The verification of planes between multi-resolution spaces adopts a method based on a least squares support vector machine that, in the model-driven section, is applied for detecting types of projecting structures. A method is then developed to determine the dormer models' parameters. Finally, the detection of boundary roof lines is obtained through a customised fuzzy Hough transform. The paper outlines the concept of the algorithms and the processing chain, and illustrates the results obtained by applying the technique to buildings of different complexities. © 2012 The Authors. The Photogrammetric Record © 2012 The Remote Sensing and Photogrammetry Society and Blackwell Publishing Ltd.
Amiri-simkooei, A.,
Asgari, J.,
Zangeneh-nejad f., F.,
Zaminpardaz s., Journal of Surveying Engineering (07339453)138(4)pp. 172-183
This contribution reviews a few basic concepts of optimization and design of a geodetic network. Proper assessment and analysis of networks is an important task in many geodetic-surveying projects. Appropriate quality-control measures should be defined, and an optimal design should be sought. The quality of a geodetic network is characterized by precision, reliability, and cost. The aim is to present a few case studies that have been designed to meet optimal precision and reliability criteria. Though the case studies may be of interest to the geodetic community in their own right, the aim is to gain insight into the general optimization problem of a geodetic network. This is also potentially of interest for educational purposes. The case studies include a zeroth-order design to improve the precision of the network points in a traverse network and a first-order design to meet the high reliability and maximum precision criteria in a geodetic network. It is shown that not only the configuration of the network but also the type of the observations used can affect the design criteria. For example, the case studies presented show that the optimal shape of the trilateration network (intersection with distances) can result in a weak network in the sense of reliability and precision if the observations are replaced by angles rather than distances (triangulation network). In close relation to the optimization problem of a geodetic network, the global positioning system satellite configuration is also optimized for a particular case that provides the minimum value of the geometric dilution of precision. © 2012 American Society of Civil Engineers.
GPS Solutions (15211886)16(1)pp. 77-88
In an attempt to model regular variations of the ionosphere, the least-squares harmonic estimation is applied to the time series of the total electron contents (TEC) provided by the JPL analysis center. Multivariate and modulated harmonic estimation spectra are introduced and estimated for the series to detect the regular and modulated dominant frequencies of the periodic patterns. Two significant periodic patterns are the diurnal and annual signals with periods of 24/n hours and 365. 25/n days (n = 1, 2,...), which are the Fourier series decomposition of the regular daily and yearly periodic variations of the ionosphere. The spectrum shows a cluster of periods near 27 days, thereby indicating irregularities at this solar cycle period. A series of peaks, with periods close to the diurnal signal and its harmonics, are evident in the spectrum. In fact, the daily signal harmonics of ω i = 2πi are modulated with the annual signal harmonics of ω j = 2πj/365. 25 as ω ijM = 2πi(1 ± j/365. 25i). Among them, at low and midlatitudes, the largest variations belong to the diurnal signal modulated to the semiannual signal. Some preliminary results on the modulated part are presented. The maximum ranges of the modulated daily signal are ±15 TECU and ±6 TECU at high and low solar periods, respectively. A model consisting of purely harmonic functions plus modulated ones is capable of studying known regular anomalies of the ionosphere, which is currently in progress. © 2011 The Author(s).
Sattari, M.,
Shahbazi, M.,
Homayouni, S.,
Saadatseresht, M.,
Shahbazi, M.,
Sattari, M.,
Homayouni, S.,
Saadatseresht, M. CTIT workshop proceedings series (16821750)39pp. 51-56
Recent advances in positioning techniques have made it possible to develop Mobile Mapping Systems (MMS) for detection and 3D localization of various objects from a moving platform. On the other hand, automatic traffic sign recognition from an equipped mobile platform has recently been a challenging issue for both intelligent transportation and municipal database collection. However, there are several inevitable problems coherent to all the recognition methods completely relying on passive chromatic or grayscale images. This paper presents the implementation and evaluation of an operational MMS. Being distinct from the others, the developed MMS comprises one range camera based on Photonic Mixer Device (PMD) technology and one standard 2D digital camera. The system benefits from certain algorithms to detect, recognize and localize the traffic signs by fusing the shape, color and object information from both range and intensity images. As the calibrating stage, a self-calibration method based on integrated bundle adjustment via joint setup with the digital camera is applied in this study for PMD camera calibration. As the result, an improvement of 83% in RMS of range error and 72% in RMS of coordinates residuals for PMD camera, over that achieved with basic calibration is realized in independent accuracy assessments. Furthermore, conventional photogrammetric techniques based on controlled network adjustment are utilized for platform calibration. Likewise, the well-known Extended Kalman Filtering (EKF) is applied to integrate the navigation sensors, namely GPS and INS. The overall acquisition system along with the proposed techniques leads to 90% true positive recognition and the average of 12 centimetres 3D positioning accuracy.
CTIT workshop proceedings series (16821750)39pp. 57-60
Automatic building extraction from high resolution satellite imagery is considered as an important field of research in remote sensing and machine vision. Many algorithms for extraction of buildings from satellite images have been presented so far. These algorithms mainly have considered radiometric, geometric, edge detection and shadow criteria approaches to perform the building extraction. In this paper, we propose a novel object based approach for automatic and robust detection and extraction of building in high spatial resolution images. To achieve this goal, we use stable and variable features together. Stable features are derived from inherent characteristics of building phenomenon and variable features are extracted using SEparability and THresholds analysis tool. The proposed method has been applied on a QuickBird imagery of an urban area in Isfahan city and visual validation demonstrates that the proposed method provides promising results. © 2012 ISPRS.
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 6005-6008
In this paper, a method is presented to recognition and modeling of three types of dormers from lidar data. The input data of this proposed algorithm involves raw roof lidar data, a regular grid of lidar data and an initial building model without superstructures. This proposed method is modular. The first stage provides a recognition type of dormers via a support vector machine. The second stage reconstructs the dormer models. Experiments show the efficiency of the proposed method. © 2012 IEEE.
Amiri-simkooei, A.,
Zangeneh-nejad f., F.,
Asgari, J. Journal of Surveying Engineering (07339453)139(4)pp. 176-187
To achieve the best linear unbiased estimation of unknown parameters in geodetic data processing a realistic stochastic model for observables is required. This work is a follow-up to work carried out recently in which the geometry-free observation model (GFOM) was used. Here, least-squares variance component estimation is applied to global positioning system (GPS) observables using the geometry-based observation model (GBOM). The benefit of using GBOM, rather than GFOM, is highlighted in the present contribution. An appropriate stochastic model for GPS observables should include different variances for each observation type, the correlation between different observables, the satellite elevation dependence of the observables' precision, and the temporal correlation of the GPS observables. Unlike the GFOM, in theGBOMtwo separate variances along with their corresponding covariances are simultaneously estimated for the phase observations of the L1 and L2 frequencies. The numerical results for two receivers-namely, Trimble 4000 SSi (Trimble Navigation, Sunnyvale, California) and Leica SR530 (Leica Geosystems, Aarau, Switzerland)-indicate a significant correlation between the observation types. The results show positive correlations of 0.55 and 0.51 between the CA and P2 code observations for Trimble 4000 SSi and Leica SR530, respectively. In addition, the satellites' elevation dependence of the GPS observables' precision is remarkable. Also, a temporal correlation of about 10 s exists in the L2 GPS observables for the Trimble 4000 SSi receiver. © 2013 American Society of Civil Engineers.
CTIT workshop proceedings series (16821750)40(1W3)pp. 453-458
Determination of the maximum ability for feature extraction from satellite imageries based on ontology procedure using cartographic feature determination is the main objective of this research. Therefore, a special ontology has been developed to extract maximum volume of information available in different high resolution satellite imageries and compare them to the map information layers required in each specific scale due to unified specification for surveying and mapping. ontology seeks to provide an explicit and comprehensive classification of entities in all sphere of being. This study proposes a new method for automatic maximum map feature extraction and reconstruction of high resolution satellite images. For example, in order to extract building blocks to produce 1:5000 scale and smaller maps, the road networks located around the building blocks should be determined. Thus, a new building index has been developed based on concepts obtained from ontology. Building blocks have been extracted with completeness about 83%. Then, road networks have been extracted and reconstructed to create a uniform network with less discontinuity on it. In this case, building blocks have been extracted with proper performance and the false positive value from confusion matrix was reduced by about 7%. Results showed that vegetation cover and water features have been extracted completely (100%) and about 71% of limits have been extracted. Also, the proposed method in this article had the ability to produce a map with largest scale possible from any multi spectral high resolution satellite imagery equal to or smaller than 1:5000.
International Journal of Remote Sensing (13665901)35(13)pp. 5094-5119
In studies of high-resolution satellite (HRS) imagery, the extraction of man-made features such as roads and buildings has become quite attractive to the photogrammetric and remote-sensing communities. The extraction of 2D images from buildings in a dense urban area is an intricate problem, due to the variety of shapes, sizes, colours, and textures. To overcome the problem, many case studies have been conducted; however, they have frequently contained isolated buildings with low variations of shapes and colours and/or high contrast with respect to adjacent features. As an alternative, this study uses continuous building blocks along with high variation in shape, colour, radiance, size, and height. In addition, some non-building features include either the same or similar materials to that of building rooftops. Thus, there is low contrast between building and non-building features. The core components of the algorithm are: (1) multispectral binary filtering, (2) sub-clustering and single binary filtering, (3) multi-conditional region growing, and (4) post-processing. This approach was applied to a dense urban area in Tehran, Iran, and a semi-urban area in Hongshan district, Wuhan city, central China. A quantitative comparison was carried out between the proposed and three other algorithms for the Wuhan case study. GeoEye multispectral imagery was used in both case studies. The results show that the proposed algorithm correctly extracted the majority of building and non-building features in both case studies. The short running time of this algorithm along with precise manual editing can generate accurate building maps for practical applications. © 2014 Taylor & Francis.
Arabian Journal of Geosciences (discontinued) (18667538)7(5)pp. 1891-1897
Atmospheric water vapor validation needs simultaneous, well-defined, and independent information which are not easily available causing limitations in the development of remote sensing water vapor retrieval algorithms. This study is concerned with the retrieval of total atmospheric water vapor content and its validation. A band ratio method has been used to estimate the water vapor content based on Moderate Resolution Imaging Spectroradiometer (MODIS) Near InfraRed (NIR) data. The method uses MODIS bands 17, 18, and 19 as NIR bands and band 2 to remove the land cover reflectance. Furthermore, the Atmospheric Infrared Sounder (AIRS) has been used for both algorithm development and analysis of the results. The method has been modified to take into account the dry condition of the central parts of Iran. Using some various datasets, the method is implemented and evaluated quantitatively. The validation of the water vapor estimates has been undertaken by an analysis of AIRS data. The validation results shows error as low as 9 % for the estimated water vapor using the MODIS NIR band ratio method. © 2013 Saudi Society for Geosciences.
Amiri-simkooei, A.,
Zangeneh-nejad f., F.,
Asgari, J.,
Jazaeri, S. Measurement: Journal of the International Measurement Confederation (02632241)48(1)pp. 378-386
Linear regression problem is a widely used problem in many metrological and measurement systems. This contribution presents a simple and reliable formulation for the linear regression fit using the weighted total least squares (WTLS) problem, when both variables are subjected to different and possibly correlated noise. The formulation is a follow up to four recent research papers in which the method was successfully applied to errors-in-variables models. It is a simple modification of the standard least squares method whose principal result is that the so-called perpendicular offsets are minimized when the full structure of correlated noise among all elements of variable x, y or both variables is supposed to be used. The formulation is rigorous, thus without approximation, and can directly provide the uncertainty of the estimated parameters. In a special case, the general formulation simplifies to the well-known standard linear regression model available in the literature. The effectiveness of the algorithm, which was implemented in MATLAB and is available in Appendix A, is demonstrated using three simulated and experimental data sets. The results indicate that accurate and reliable estimates of line parameters along with their covariance matrix can be provided using the proposed formulation in a relatively small amount of time. © 2013 Elsevier B.V. All rights reserved.
Journal of the Indian Society of Remote Sensing (09743006)42(2)pp. 423-428
One of the most widely used outputs of remote sensing technology is Hyperspectral image. This large amount of information can increase classification accuracy. But at the same time, conventional classification techniques are facing the problem of statistical estimation in high-dimensional space. Recently in remote sensing, support vector machines (SVMs) have shown very suitable performance in classifying high dimensionality problem. Another strategy that has recently been used in remote sensing is multiple classifier system (MCS). It can also improve classification accuracy by combining different classifier methods or by a diversity of the same classifier. This paper aims to classify a Hyperspectral data using the most common methods of multiple classifier systems i.e. adaboost and bagging and a MCS based on SVM. The data used in the paper is an AVIRIS data with 224 spectral bands. The final results show the high capability of SVMs and MCSs in classifying high dimensionality data. © 2013 Indian Society of Remote Sensing.
Journal of Geodetic Science (20819943)4(1)
Precision, reliability and cost are the major criteriaapplied in optimization and design of geodetic networks.The terrestrial networks are being replaced quicklyby permanent and campaign Global Positioning System(GPS) networks. These networks must be optimized usingthe same three criteria. In this article the optimization ofthe observational plan of local GPS networks (Second OrderDesign (SOD)) is considered using the precision criterion.This study is limited to the selection of optimal numbersand the best distribution of the non-trivial baselinesthroughout the network. This objective is accomplishedbased on the SOD solution through the analytical methodin operational research by the means of quadratic programmingalgorithm. This presented method is tested ona real GPS network and appears to be a useful techniquein terms of cost reduction in the field work by the providedobservational plan and optimal distribution of thebaselines throughout the network. Results indicate thatweights of almost 36% of the baselines are negligiblewhencompared to the weights of the rest of the baselines; therefore,they could be eliminated fromthe observational plan,resulting in a 36% saving in the fieldwork cost. © 2014 H. Mehrabi, B. Voosoghi.
Photogrammetric Engineering and Remote Sensing (00991112)80(6)pp. 519-528
This paper reviews and evaluates four building extraction algorithms including two pixel-based and two object-based methods using a diverse set of very high spatial resolution imagery. The applied images are chosen from different places (the cities of Isfahan, Tehran, and Ankara) and different sensors (QuickBird and GeoEye-1), which are diverse in terms of building shape, size, color, height, alignment, brightness, and density. The results indicate that the performance and the reliability of two object-based algorithms are better than pixel-based algorithms; about 10 percent to 15 percent better for the building detection rate and 6 percent to 10 percent better for the reliability rate. However, in some cases, the detection rate of pixel-based algorithms has been greater than 80 percent, which is a satisfactory result. On the other hand, segmentation errors can cause limitations and errors in the object-based algorithms, so that the commission error of object-based algorithms has been higher than pixel-based algorithms in some cases. © 2014 American Society for Photogrammetry and Remote Sensing.
