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.
