University of Tehran Earth Observation and Geomatics Engineering 2588-4352 2 1 2018 06 01 On the use of two L1 norm minimization methods in geodetic networks 1 8 66945 10.22059/eoge.2018.256034.1021 EN Alireza Amiri-Simkooei Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran 0000-0002-2952-0160 Journal Article 2017 11 05 L1 norm adjustment is a powerful technique to detect gross errors in geodetic observations. This paper investigates the results of two formulations that provide the L1 norm adjustment of a linear functional model. The usual method for implementation of the L1 norm adjustment leads to solving a linear programming (LP) problem. The formulation of the L1 norm minimization is presented based on the LP problem for a rank deficient linear(ized) system of equations. Then, an alternative technique is explained based on the least squares residuals. The results are tested on both linear and non-linear functional models, which confirm the efficiency of both formulations. The results also indicate that the L1 norm minimization, compared to the weighted least squares method, is a robust technique for the detection of blunders in geodetic observations. Finally, this contribution presents a data snooping procedure to the residuals obtained by the L1 norm minimization method.

https://eoge.ut.ac.ir/article_66945_30a9f86bc9c7449f56c187dc57bb4494.pdf

University of Tehran Earth Observation and Geomatics Engineering 2588-4352 2 1 2018 06 01 Automatic change detection in remotely sensed hyperspectral imagery (Case study: wetlands and waterbodies) 9 25 66946 10.22059/eoge.2018.238510.1010 EN Mahdi Hasanlou School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 0000-0002-7254-4475 Seyd Teymoor Seydi School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 0000-0002-3678-4877 Journal Article 2017 07 24 Wetlands are one of the important types of ecosystems that play a fundamental role in the environment and provide significant benefits due to the resources that they contain. Therefore, it is necessary to monitor the changes in these ecosystems. The alterations in Earth’s ecosystems caused by the natural activities, such as drought, as well as human activities and population growth has been affecting the wetlands and waterbodies area. Therefore, for achieving a better detection of these changes over time, it is important to generate descriptive location maps based on the characteristics of wetlands. Hyperspectral images have shown potential use in many applications due to their high spectral resolution, and consequently, their high informative value. This study presents a hybrid procedure for automatic detection of changes in wetlands using a new approach which can provide more details about the changes with high accuracy. The hybrid proposed method is based on incorporating chronochrome, Z-score analysis, Otsu algorithm, simplex via split augmented lagrangian (SISAL), Harsanyi–Farrand–Chang (HFC), Pearson correlation coefficient (PCC), and support vector machine (SVM) to detect changes using hyperspectral imagery. The proposed method in the first step, produce a training data for tuning SVM and kernel parameters. The second step, predicted change areas based on a chronochrome algorithm and binary change map obtained using SVM classifier. The third step, the amplitude of changes is created by Z-Score analysis and binary change mask. Finally, the multiple change map is produced based on the estimation of number and extraction of endmembers and similarity measure. The proposed method evaluated and compared the performances with other common hyperspectral change detection methods using three real-world datasets of multi-temporal hyperspectral imagery. The empirical results reveal the superiority of the proposed hybrid method in extracting the change map with an overall accuracy of nearly 96% and a kappa coefficient of 0.89 while other hyperspectral change detection methods have the overall accuracy lower than 93% and kappa coefficient 0.80. In addition, this hybrid method can provide ‘multiple changes’ as well as the magnitude of extracted changes.

https://eoge.ut.ac.ir/article_66946_cdc8b0d6dc924ba4ede94704f8446ca6.pdf

University of Tehran Earth Observation and Geomatics Engineering 2588-4352 2 1 2018 06 01 Numerical determination of the geodesic curves: the solution of a two-point boundary value problem 26 35 66947 10.22059/eoge.2018.257564.1023 EN Mohammad Reza Seif Department of Surveying Engineering, Arak University of Technology, Arak, Iran Emad Ghalenoei Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada Journal Article 2017 10 20 In this paper, we suggest a simple iterative method to find the geodesic path on a surface parameterized by orthogonal curvilinear system between two given points based on solving Boundary Value Problem. In this supposed method, an iterative algorithm is used for finding the sufficient initial values as the destination point agree with the boundary conditions. Geodesic determination between two given points is formulated for a general surface, and specially tested for reference ellipsoid which has many applications in geosciences and geodesy. Accuracy of the method is independent on the distant between two points on the surface. Moreover, it can be used in aviation and sailings for finding the shortest path between start and destination points.

https://eoge.ut.ac.ir/article_66947_b329ce8007bd614cdd6f213f2e08792b.pdf

University of Tehran Earth Observation and Geomatics Engineering 2588-4352 2 1 2018 06 01 Extraction of ground points from LiDAR data based on slope and progressive window thresholding (SPWT) 36 44 66949 10.22059/eoge.2018.240284.1012 EN Pejman Rashidi School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Heidar Rastiveis School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran Journal Article 2017 08 22 Filtering of airborne LiDAR point clouds has broad applications, such as Digital Terrain Model (DTM) generation and three-dimensional urban modeling. Although several methods have been developed to separate the point clouds into ground and non-ground points, there are some challenges to identify the complex objects such as bridge and eccentric roofs. In this study, a new algorithm based on the Slope and Progressive Window Thresholding (SPWT) is proposed for ground filtering of LiDAR data. This algorithm is based on both multi-scale and slope methods that have strong effects on filtering the LiDAR data. The proposed algorithm utilizes the slope between adjacent points and the elevation information of points in a local window to detect non-ground objects. Therefore, not only it benefits from vertical information in each local window to detect the non-ground points, but it also uses the neighbor information in directional scanning, and it prevents the errors introduced by the sensitivity to direction. According to the physical characteristics of the ground surface and the size of objects, the best threshold values are considered. In order to evaluate the performance of the SPWT method, both low and high resolution datasets were applied that their average overall accuracy were reported to be 94.21% and 93.08%, respectively. These results proved that, irrespective of data resolution, the SPWT method could effectively remove the non-ground points from airborne LiDAR data.

https://eoge.ut.ac.ir/article_66949_a301201e2ef370952b865591469f1b37.pdf

University of Tehran Earth Observation and Geomatics Engineering 2588-4352 2 1 2018 06 01 Assessing the impact of cold and warm ENSO on drought over Iran 45 55 66950 10.22059/eoge.2018.257714.1022 EN Zahir Nikraftar Department of Geomatics Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran Ali Sam-Khaniani Babol Noshirvani University of Technology, Civil Engineering Department, P.O.Box 484, Shariati Ave, Babol,Mazandaran 47148-71167, Iran Journal Article 2017 12 01 The impacts of El Niño Southern Oscillation (ENSO) on climate change and in the global scale are well known, and have attracted the attention of researchers since the twentieth century. The study of ENSO impact on climate using precipitation and near surface temperature data from re-analysis products makes global and long-term analyses of this phenomenon possible. The common method to analyze the ENSO impact is to quantify the probability of extreme drought occurrences when the surface temperatures of central-east equatorial Pacific sea are abnormal. Although the results are always uncertain due to the complexity of atmospheric teleconnections, application of the recently available gridded datasets helps one to conduct more precise modeling and predictions. Spatiotemporal patterns of ENSO impact from 1980 to the end of 2013 for four ENSO indices (e.g. Nino 3.4, MEI, ONI, SOI) over Iran was investigated in this study. Spatial maps of the Pearson correlation coefficients and a composite analysis were obtained between the GPCC precipitation and temperature dataset with ENSO states. In addition, the frequency maps of extreme drought conditions during ENSO states were acquired. The results show that the western (along the range of Zagros Mountain) and northern (along the Alborz Mountain and the coastlines of the Caspian Sea to Khorasan Province) regions are more affected by ENSO events. The Pearson correlation coefficient for all four ENSO indices over the mentioned regions was determined to be about 0.70 for precipitation datasets and -0.70 for temperature datasets. The frequency analysis of extreme drought based and CZI (Chinese Z Index) and ENSO phases shows that the western and northeast parts of Iran are more affected by centraleast equatorial Pacific teleconnections. Composite analysis for all four ENSO indices shows the precipitation (over the rainy months)/temperature (over the summer months) anomalies, for the El Niño states about +25 (mm)/ -0.5˚ (C) and for the La Niña states about -25 (mm)/+0.6˚ to 1˚ (C).

https://eoge.ut.ac.ir/article_66950_1a455a59e5fb212b5d6ce937cdbc2b80.pdf

University of Tehran Earth Observation and Geomatics Engineering 2588-4352 2 1 2018 06 01 A comparison of four methods for extracting Land Surface Emissivity and Temperature in the Thermal Infrared Hyperspectral Data 56 63 66951 10.22059/eoge.2018.239666.1011 EN Faeze Soleimani Vosta Kolaei School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran Mehdi Akhoondzadeh School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran Journal Article 2017 08 12 Land Surface Temperature (LST) and Land Surface Emissivity (LSE) are two important physical properties of Earth's surface. LST retrieval plays a valuable role in environmental studies. Therefore, in order to estimate LST accurately, it is necessary to obtain LSEs. The HyTES (Hyperspectral Thermal Emission Spectrometer) instrument has 256 spectral bands covering the thermal infrared (TIR) spectral range. Due to a large number of narrow bandwidths of HyTES, this sensor can produce the accurate LST and LSEs. The main goal of this paper is to evaluate the accuracy of LSE and LST retrieval methods from HyTES data and to improve the accuracy of the Normalization (NOR) method. For this purpose, four different methods have been considered to retrieve LSE: (i) the Reference Channel method (REF), (ii) the Emissivity Normalization method, (iii) the Alpha emissivity method (AlPHA) and (iv) a method to improve the NOR algorithm. The first three methods have been widely used with thermal multispectral data in other researches. These methods were used with HyTES hyperspectral data in this paper; the fourth is a new method that improved the accuracy of the NOR method. The results of quality assessment show that the emissivity RMSEs of the REF, NOR, ALPHA methods and the new proposed method are 0.021, 0.815, 0.034 and 0.0201, respectively. Also, LST RMSEs of the REF and NOR methods are less than 1.5 K.

https://eoge.ut.ac.ir/article_66951_fe70e659d72db7250c46906baa6e8a0b.pdf