Department of Geodesy and Geomatics

Hu man activit ies are embedded in the space. They frequently use their spatial cognition in familiar space, or navigation aids (e.g., cartographic maps and satellite navigation systems) in unfamiliar areas for positioning and way-finding purposes. Today, satellite navigation systems are widely used by even non-expert users because of their abilities in friendly interaction with the user. They find the optimu m path to a certain destination and navigate the user through it using automatically movement and rotation of the digital map regard ing the user's position as well as direction of movement. Dealing with cartographic maps, however, need prior knowledge and experience on map reading as well as enough spatial cognition. Users of cartographic maps must accomplish all of the way-finding, positioning and orientation tasks by themselves through connecting the elements of the environment and the map. Enhancing the cognitive readability of cartographic maps increases their usability fo r navigation mo re easily in unfamiliar environ ments. This paper represents the result of enriching cartographic maps with features used by pedestrians for navigation in urban areas.

Hu man activit ies are embedded in the space. They frequently use their spatial cognition in familiar space, or navigation aids (e.g., cartographic maps and satellite navigation systems) in unfamiliar areas for positioning and way-finding purposes. Today, satellite navigation systems are widely used by even non-expert users because of their abilities in friendly interaction with the user. They find the optimu m path to a certain destination and navigate the user through it using automatically movement and rotation of the digital map regard ing the user's position as well as direction of movement. Dealing with cartographic maps, however, need prior knowledge and experience on map reading as well as enough spatial cognition. Users of cartographic maps must accomplish all of the way-finding, positioning and orientation tasks by themselves through connecting the elements of the environment and the map. Enhancing the cognitive readability of cartographic maps increases their usabili...

Human activities are embedded in the space. People need to know where they are and how they can arrive to their destination. They may use their spatial cognition in familiar space, or navigation aids (e.g., cartographic maps and satellite navigation systems) in unfamiliar areas for positioning and wayfinding purposes. Today, satellite navigation systems are widely used by even non-expert users. Dealing with cartographic maps, however, need prior knowledge and experience on map reading as well as enough spatial cognition. Enriching cartographic maps with the elements of spatial cognition could help to navigate more easily in unfamiliar environments. This paper represents the result of a research to enrich cartographic maps with features used by pedestrians for navigation in urban areas.

Building change detection in high-resolution satellite images plays a special role in urban management and development. Recently, methods for building change detection have been greatly improved by developing deep learning. Although deep learning technologies, especially Siamese convolutional neural networks, have been successful and popular, they usually have problems in extracting features that are not discriminative enough and also cause the loss of shape and details at the edges. To address these problems, a dual-branch deep network and a parallel spatial-channel attention mechanism were suggested to extract spatial and spectral dependencies and more discriminative features. The spatial attention unit measured the rich context of local features, and the distinction between changed objects and backgrounds was increased using spatial attention in deep features. The channel attention module adjusted the weight of channels and acted as a channel selection process. Mixing two attentions to parallel mode made the features more practical, and useful information was learned more robustly. Moreover, a dual loss function was proposed in which the edge-based consistency constraints were used in the first part to converge the edges of the training and the predicted data. The weighted binary cross-entropy was added to the second part of the loss function. The proposed method was implemented on two remote sensing datasets, and the results were evaluated with state-of-the-art methods. With the proposed model, the F1-score was improved by 2.43% and 1.83% in the first and second datasets, respectively.

The Rational Function Model (RFM) has been widely used as an alternative to rigorous sensor models of high-resolution optical imagery in photogrammetry and remote sensing geometric processing. However, not much work has been done to evaluate the applicability of the RF model for Synthetic Aperture Radar (SAR) image processing. This paper investigates how to generate a Rational Polynomial Coefficient (RPC) for high-resolution TerraSAR-X imagery using an independent approach. The experimental results demonstrate that the RFM obtained using the independent approach fits the Range-Doppler physical sensor model with an accuracy of greater than 10 −3 pixel. Because independent RPCs indicate absolute errors in geolocation, two methods can be used to improve the geometric accuracy of the RFM. In the first method, Ground Control Points (GCPs) are used to update SAR sensor orientation parameters, and the RPCs are calculated using the updated parameters. Our experiment demonstrates that by using three control points in the corners of the image, an accuracy of 0.69 pixels in range and 0.88 pixels in the azimuth direction is achieved. For the second method, we tested the use of an affine model for refining RPCs. In this case, by applying four GCPs in the corners of the image, the accuracy reached 0.75 pixels in range and 0.82 pixels in the azimuth direction.

Hyperspectral image containing high spectral information has a large number of narrow spectral bands over a continuous spectral range. This allows the identification and recognition of materials and objects based on the comparison of the spectral reflectance of each of them in different wavelengths. Hence, hyperspectral image in the generation of land cover maps can be very efficient. In the hyperspectral classification methods that use a dissimilarity measure for classification, the reference reflectance spectra of each class are usually estimated through averaging the image pixel's reflectance spectra of training data. This estimation method yields a reference reflectance spectrum in which minimize the total sum of squared Euclidean distances between the reference reflectance spectrum itself and the image pixel's reflectance spectra of training data. For this reason, the method is acceptable only for the Minimum Distance algorithm in which is used the squared Euclidean distance for classification. In this paper, we propose a method in which the reference reflectance spectrum is estimated by taking into account the dissimilarity measure that is used in the classification algorithm. Two SAM and JMD classification algorithms have been used to present and implement the proposed method. The evaluation of the accuracy and efficiency of the proposed method has been done by investigating and comparing the results of the classification of SAM and JMD algorithms by considering both averaging and proposed methods. The tests performed on four real hyperspectral images collected by AVIRIS, HYDICE, Hyperion and HyMap sensors show that the proposed method improves classification results, in a manner that the Kappa coefficient of the classification results of four hyperspectral imagery datasets increased by 13.18%, 1.06%, 0.75% and 2.18%, respectively, in the SAM algorithm and 10.79%, 2.17%, 0.34% and 2.4%, respectively, in the JMD algorithm.

Study of recent crustal movements is considered as an important task for geodesists, geologists and geophysicists. This is due to other part this phenomena is very dangerous on the human life in the world, so all constructed projects must be secured against the crustal deformation at local, regional and global scales. Study of recent crustal movements means the determination of the magnitude, direction and the rate of these movements. In addition, the analysis of horizontal crustal strains is important in a wide area of geophysical studies as well as in earthquake prediction. Nowadays, crustal deformation within Egypt relates to the regional and local tectonic forces. GPS data were collected and processed using Bernese 5.0 software. The displacement vectors for each epoch of observation were calculated from the coordinate changes. Considering the confidence limit, most of these displacement vectors could be mainly attributed to the movement within Aswan study area in those epochs of measurements. The relative movements between the epochs of measurements are estimated. The main objective of this work is to monitor the surface crustal deformation around the reservoir for understanding the geodynamics of this region. Also, an attempt has been made to investigate the recent crustal movements by two solutions , by making the point in the network once as a fixed point and once again by connecting point (IGS) out of the network and their relation to the induce seismicity in the areas vicinity of Aswan Lake

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.

Geometric documentation is one of the most important parts of a documentary report. Despite the advances made in the field of line drawing of ancient relief surfaces, in most cases, human operator interaction is unavoidable. In this paper, an algorithm for the semiautomatic line drawing of relief surfaces has been developed. In the proposed method, photometric stereo normals are used as a highresolution and low-noise data for the automatic extraction of surface edges. The normals are computed in 2D image space and also the fringe projection scanner is used for geometric correction of 2D image based drawings. Therefore, the drawings are converted to a metric map for geometric documentation reports. The results show that the efficiency of the proposed method, which has managed to correctly draw about more than 99% of the edge lines of an ancient relief. Also all of the drawn lines are completely coincided to the relief edges on its orthophoto image.

Rapid advances in remote sensing sensor technology have made it recently possible to collect new dense 3D data like Light Detection And Ranging (LIDAR). One of the challenging issues about LIDAR data is classification of these data for identification of different objects in urban area like building, road, and tree. Regarding to complexities of objects in urban area and disability of LIDAR data to collect the radiometric information of surface, traditional classifiers have low level of performance in classification of LIDAR data. Combining classifiers is an established concept that it used for improvement of classification results. In this paper we propose a classifier fusion system scheme based on Support Vector Machine (SVM) for classification of LIDAR data. Different SVMs are trained on the best different subset of features that are proper for object extraction in LIDAR data and chosen by RANSAC as feature selection method. In this article, two multiclass SVM methods known as one-against-one and one-against-all are investigated for classification of LIDAR data and then final decision is achieved by Majority Voting method. The results confirm that established method on LIDAR data has improved accuracy of classification. It is also demonstrated that one-against-all results better accuracy comparing to one-against-one although it is much more time consuming.

The ultimate goal of pattern recognition systems in remote sensing is to achieve the best possible classification performance for recognition of different objects such as buildings, roads and trees .Extracting of road from newer Lidar data is one of the main challenge in photogrammetry ...

LIDAR is a powerful remote sensing technology for acquisition of 3D information from terrain surface. Algorithms used for LIDAR data in urban area are used mostly to deal with the 3D points cloud and to identify objects, such as buildings, trees and roads. In contrast to the well studied problem of building and tree detection from LIDAR data, the detection of roads from LIDAR is in its formative years. Road detection from remotely sensed data is a difficult problem that requires more research due to the many unsolved questions related to scene interpretation. Existing road extraction techniques are characterised by poor detection rates and the need for existing data and / or user interaction. To improve the potential of road extraction process, other information in addition to the height of cloud points is required, such as laser intensity. However, few algorithms for classification using intensity data have been deeply investigated. The laser intensity differs from material to material. The intensity of reflection for most instances of the same material is similar; while pulse intensity from different materials differs. This article deals with using as much of the recorded laser information as possible thus both height and intensity are used. To extract roads from an LIDAR point cloud, a multiple classifier system is used to classify the LIDAR points into road and other non-road objects. We experiment classifier selection and combination in classification of LIDAR data over an urban area, wherein we aim to select an optimal or sub-optimal subset of classifiers from available combination candidates through an evolutionary strategy. The performance of the selected classifiers is measured by the combination accuracy using plurality of votes. The obtained results show that optimum subset classifier selection, improves the combination performance compared to the combination of all classifiers.

LIght Detection And Ranging (LIDAR) is a powerful remote sensing technology in the acquisition of the terrain surface information for object classification and extraction. Major benefits of thistechnique are its high level ofautomation during data capturing and its spatial resolution. Because of high complexities and difficulties in urban areas, many researchers focus on the using of LIDAR data in such area. Consequently, one of the challenging issues about LIDAR data is classification of these data in urban area for identification of different objects such as building, road and tree. Several urban classification methods have been proposed for classification of LIDAR data. Support Vector Machines (SVM), oneof the new techniques for pattern classification; have been widely used in many application areas such as remote sensing. SVM is a binary classification method but in some researches like remote sensing or pattern recognition, we need more than two classes. One solution for this d...

Interest in the joint use of different data from multiple sensors has been increased for classification applications. This is because the fusion of different information can produce a better understanding of the observed site. In this field of study, the fusion of light detection and ranging (LIDAR) and passive optical remote sensing data for classification of land cover has attracted much attention. This paper addressed the use of a combination of hyperspectral (HS) and LIDAR data for land cover classification. HS images provide a detailed description of the spectral signatures of classes, whereas LIDAR data give detailed information about the height but no information for the spectral signatures. This paper presents a multiple fuzzy classifier system for fusion of HS and LIDAR data. The system is based on the fuzzy K-nearest neighbor (KNN) classification of two data sets after application of feature grouping on them. Then a fuzzy decision fusion method is applied to fuse the results of fuzzy KNN classifiers. An experiment was carried out on the classification of HS and LIDAR data from Houston, USA. The proposed fuzzy classifier ensemble system for HS and LIDAR data provide interesting conclusions on the effectiveness and potentials of the joint use of these two data. Fuzzy classifier fusion on these two data sets improves the classification results when compared with independent single fuzzy classifiers on each data set. The fuzzy proposed method represented the best accuracy with a gain in overall accuracy of 93%.

Regarding to the limitations and benefits of remote sensing sensors, fusion of remote sensing data from multiple sensors such as hyperspectral and LIDAR (light detection and ranging) is effective at land cover classification. Hyperspectral images (HSI) provide a detailed description of the spectral signatures of classes, whereas LIDAR data give height detailed information. However, because of the more complexities and mixed information in LIDAR and HSI, traditional crisp classification methods could not be more efficient. In this situation, fuzzy classifiers could deliver more satisfactory results than crisp classification approaches. Also, referring to the limitation of single classifiers, multiple classifier system (MCS) may exhibit better performance in the field of multi-sensor fusion. This paper presents a fuzzy multiple classifier system for fusions of HSI and LIDAR data based on decision template (DT). After feature extraction and feature selection on each data, all selected features of both data are applied on a cube. Then classifications were performed by fuzzy k-nearest neighbour (FKNN) and fuzzy maximum likelihood (FML) on cube of features. Finally, a fuzzy decision fusion method is utilized to fuse the results of fuzzy classifiers. In order to assess fuzzy MCS proposed method, a crisp MCS based on support vector machine (SVM), KNN and maximum likelihood (ML) as crisp classifiers and naive Bayes (NB) as crisp classifier fusion method is applied on selected cube feature. A co-registered HSI and LIDAR data set from Houston of USA was available to examine the effect of proposed MCSs. Fuzzy MCS on HSI and LIDAR data provide interesting conclusions on the effectiveness and potentialities of the joint use of these two data.

The accurate measurement of platform orientation plays a critical role in a range of applications including marine, aerospace, robotics, navigation, human motion analysis, and machine interaction. We used Mahoney filter, Complementary filter and Xsens Kalman filter for achieving Euler angle of a dynamic platform by integration of gyroscope, accelerometer, and magnetometer measurements. The field test has been performed in Kish Island using an IMU sensor (Xsens MTi-G-700) that installed onboard a buoy so as to provide raw data of gyroscopes, accelerometers, magnetometer measurements about 25 minutes. These raw data were used to calculate the Euler angles by Mahoney filter and Complementary filter, while the Euler angles collected by XSense IMU sensor become the reference of the Euler angle estimations. We then compared Euler angles which calculated by Mahoney Filter and Complementary Filter with reference to the Euler angles recorded by the XSense IMU sensor. The standard deviations of the differences between the Mahoney Filter, Complementary Filter Euler angles and XSense IMU sensor Euler angles were about 0.5644, 0.3872, 0.4990 degrees and 0.6349, 0.2621, 2.3778 degrees for roll, pitch, and heading, respectively, so the numerical result assert that Mahoney filter is precise for roll and heading angles determination and Complementary filter is precise only for pitch determination, it should be noted that heading angle determination by Complementary filter has more error than Mahoney filter.

The collapse of buildings during the earthquake is a major cause of human casualties. Furthermore, the threat of earthquakes will increase with growing urbanization and millions of people will be vulnerable to earthquakes. Therefore, building damage detection has gained increasing attention from the scientific community. The advent of Light Detection And Ranging (LiDAR) technique makes it possible to detect and assess building damage in the aftermath of earthquake disasters using this data. The purpose of this paper is to propose and implement an object-based approach for mapping destructed buildings after an earthquake using LiDAR data. For this purpose, first, multi-resolution segmentation of post-event LiDAR data is done after building extraction from pre-event building vector map. Then obtained image objects from post-event LiDAR data is located on the pre-event satellite image. After that, appropriate features, which make a better difference between damage and undamaged buildings, are calculated for all the image objects on both data. Finally, appropriate training samples are selected and imported into the object-based support vector machine (SVM) classification technique for detecting the building damage areas. The proposed method was tested on the data set after the 2010 earthquake of Port-au-Prince, Haiti. Quantitative evaluation of results shows the overall accuracy of 92% by this method.

Earthquakes are known as one of the deadliest natural disasters that have caused many fatalities and homelessness through history. Due to the unpredictability of earthquakes, quick provision of buildings damage maps for reducing the number of losses after an earthquake has become an essential topic in Photogrammetry and Remote Sensing. Low-accuracy building damage maps waste the time that is required to rescue the people in destructed areas by wrongly deploying the rescue teams toward undamaged areas. In this research, an object-based algorithm based on combining LiDAR raster data and high-resolution satellite imagery (HRSI) was developed for buildings damage detection to improve the relief operation. This algorithm combines classification results of both LiDAR raster data and high-resolution satellite imagery (HRSI) for categorizing the area into three classes of “Undamaged,” “Probably Damaged,” and “Surely Damaged” based on the object-level analysis. The proposed method was tested...