Rami Al-Ruzouq

Photogrammetric and Lidar Data Registration Using Linear Features

The enormous increase in the volume of datasets acquired by lidar systems is leading towards their extensive exploitation in a variety of applications, such as, surface reconstruction, city modeling, and generation of perspective views. Though being a fairly new technology, lidar has been influenced by and had a significant impact on photogrammetry. Such an influence or impact can be attributed to the complementary nature of the information provided by the two systems. For example, photogrammetric processing of imagery produces accurate information regarding object space break lines (discontinuities). On the other hand, lidar provides accurate information describing homogeneous physical surfaces. Hence, it proves logical to combine data from the two sensors to arrive at a more robust and complete reconstruction of 3D objects. This paper introduces alternative approaches for the registration of data captured by photogrammetric and lidar systems to a common reference frame. The first approach incorporates lidar features as control for establishing the datum in the photogrammetric bundle adjustment. The second approach starts by manipulating the photogrammetric imagery to produce a 3D model, including a set of linear features along object space discontinuities, relative to an arbitrarily chosen coordinate system. Afterwards, conjugate photogrammetric and lidar straight-line features are used to establish the transformation between the arbitrarily chosen photogrammetric coordinate system and the lidar reference frame. The second approach (bundle adjustment, followed by similarity transformation) is general enough to be applied for the co-registration of multiple three-dimensional datasets regardless of their origin (e.g., adjacent lidar strips, surfaces in GIS databases, and temporal elevation data). The registration procedure would allow for the identification of inconsistencies between the surfaces in question. Such inconsistencies might arise from changes taking place within the object space or inaccurate calibration of the internal characteristics of the lidar and the photogrammetric systems. Therefore, the proposed methodology is useful for change detection and system calibration applications. Experimental results from aerial and terrestrial datasets proved the feasibility of the suggested methodologies.

Linear features for automatic registration and reliable change detection of multi-source imagery

Change detection analysis usually involves multi-spectral, multi-source and/or multi-resolution imagery that has been captured at different times. Accurate co-registration of these datasets is a prerequisite step for a reliable change detection procedure. This paper introduces a new approach for automatic image registration using the Modified Iterated Hough Transform (MIHT) and linear features, which have been chosen since they can be reliably extracted from imagery with varying geometric and radiometric properties. Finally, edge detection and subtraction of the registered images are used as the basis for the change-detection procedure. Experimental results from real data proved the feasibility of the suggested approach.

Data fusion of multi-source imagery based on linear features registration

Image fusion is the process of combining images captured by different sensors under different conditions. These images usually have different geometric and radiometric properties. The enormous increase in the volume of remotely sensed data has created the need for robust data processing techniques that can fuse data observed by different acquisition systems. This need is motivated by the fact that collected data by these sensors are complementary in nature. Therefore, simultaneous utilization of the collected data would guarantee a full understanding of the object/phenomenon under consideration. In this regard, a data-fusion procedure can be defined as being concerned with the problem of how to combine data and/or information from multiple sensors in order to achieve improved accuracies and better inference about the environment than could be attained through the use of a single sensor. Fusion of multi-source imagery captured under different conditions is a challenging problem. The difficulty is attributed to the varying radiometric and geometric resolutions of the acquired imagery. Image registration is considered as one of the most critical requirement for accurate data fusion. The most appropriate primitives, transformation function and similarity measure have been incorporated in a matching strategy to solve the registration problem. Experimental results using real data proved the feasibility and the robustness of the suggested method.

Infrastructure growth assessment of urban areas based on multi-temporal satellite images and linear features

ABSTRACT Urbanization is typically demonstrated by expansion of a city’s infrastructure, mainly development of its roads and buildings. In particular, transportation infrastructure is a key indicator of growth in the city since transportation is the backbone of economic development and prosperity. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, enable efficient identification of change patterns and prediction of built-up areas. In this study, two approaches were adopted to quantify and assess the pattern of urbanization. The first approach relied on extracting linear features (buildings and roads) from multi-temporal satellite images, where image-to-image registration was utilized based on linear features with Modified Iterated Hough Transform as matching criteria. The second approach relied on extracting linear features from vector (digitized) data. The latter approach complemented the first one by distinguishing between roads and buildings. The proposed methodology was then applied to Sharjah City, United Arab Emirates, as a case study. Results show that the urbanized area of the city almost quadrupled during 1976–2016. Growth in buildings and roads was generally consistent until 2005, after that the spatial growth witnessed a steep increase due to vertical expansion. To assess the accuracy of the utilized edge images and change detection, error matrices were prepared for the case study. An overall accuracy of more than 84% was achieved. The proposed methodology was successful in quantifying urban growth in the study area.

Archaeological Site Information Modelling and Management Based on Close-Range Photogrammetry and GIS

Abstract New challenges in management and restoration of archaeological sites require the utilization of advanced technologies and systems to assist in preserving the important legacy of human heritage. These challenges include monitoring and preserving of site landscapes, maintenance and rehabilitation of archaeological structures, and quality and safety management. In this research, an integrated methodology based on photogrammetry and Three-Dimensional (3D) Geographic Information System (GIS) is developed to capture and model essential details needed for the proper management of archaeological sites. Recent advances in digital imagery and modelling utilize photogrammetry as an efficient technique for 3D modelling of archaeological sites to derive accurate measurements of size, shape, position, and texture of objects using two-dimensional digital photographs. Spatial data that include aerial stereo photographs, satellite images, close-range photos, terrestrial surveying and Global Positioning Systems (GPS) are integrated within the design of the proposed model. The approach is validated with a case study of Ajloun Castle, one of the ancient and important preserved structures located in Jordan. Various sequential levels of details have been developed for the castle: Orthophoto, Digital Elevation Model (DEM), structures geometry and site layout and 3D textured model. The suggested methodology is expected to be efficient in modelling and documenting archaeological structures. The developed model is utilized to assess the management of archaeological structures through feasibility analysis, rehabilitation decisions, and safety planning.

Multi-Temporal Satellite Imagery for Urban Expansion Assessment at Sharjah City /UAE

Change detection is the process of identifying differences in land cover over time. As human and natural forces continue to alter the landscape, it is important to develop monitoring methods to assess and quantify these changes. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, are allowing for efficient identification of change patterns and the prediction of areas of growth. Sharjah is the third largest and most populous city in the United Arab Emirates (UAE). It is located along the northern coast of the Persian Gulf on the Arabian Peninsula. After the discovery of oil and its export in the last four decades at UAE, it has experienced very rapid growth in industry, economy and population. The main purpose of this study is to detect urban development in Sharjah city by detecting and registering linear features in multi-temporal Landsat images. This paper used linear features for image registration that were chosen since they can be reliably extracted from imagery with significantly different geometric and radiometric properties. Derived edges from the registered images are used as the basis for change detection. Image registration and pixel-pixel subtraction has been implement using multi- temporal Landsat images for Sharjah City. Straight-line segments have been used for accurate co-registration as well as main element for a reliable change detection procedure. Results illustrate that highest range of growth that represented by linear features (building and roads) have been accrued during 1976 – 1987 and stand for 36.24% of the total urban features inside Sharjah city. Moreover, result shows that since 1976 to 2010, the cumulative urban expansion inside Sharjah city is 71.9%.

Multiple source imagery and linear features for detection of urban expansion in Aqaba City, Jordan

Aqaba is one of the most strategic cities in Jordan and the entire region, as it is the only seaport for Jordan and has a special economic zone as the only window to global markets. The main purpose of this study is to detect urban development in Aqaba region by detecting and registering linear features in images with various geometric and radiometric properties taken at different times. This article used linear features for image registration that were chosen since they can be reliably extracted from imagery with significantly different geometric and radiometric properties. The modified iterated Hough transform (MIHT) is used as the matching strategy for automatically deriving an estimate of the parameters involved in the transformation function relating the images to be registered as well as the correspondence between conjugate lines. Derived edges from the registered images are used as the basis for change detection. The utilization of edges is motivated by the fact that they are invariant with respect to possible radiometric differences between the images in question. Linear features extraction, feature matching, image registration and pixel–pixel subtraction have been implemented using SPOT, Landsat, Ikonos and aerial photographs that have different radiometric, spatial and temporal resolutions. It has been shown that linear features (straight-line segments) have high semantics and can be reliably extracted from the images. These linear features can be used for accurate co-registration as an essential prerequisite for a reliable change detection procedure. For the purpose of change detection, image–image registration is more crucial than image–ground registration, where corresponding features in images are registered with respect to each other regardless of the associated absolute errors. The results illustrate that using edges as the base for change detection in urban areas is efficient and reliable.

Macro and micro geo-spatial environment consideration for landfill site selection in Sharjah, United Arab Emirates

Waste management involves various procedures and resources for proper handling of waste materials in compliance with health codes and environmental regulations. Landfills are one of the oldest, most convenient, and cheapest methods to deposit waste. However, landfill utilization involves social, environmental, geotechnical, cost, and restrictive regulation considerations. For instance, landfills are considered a source of hazardous air pollutants that can cause health and environmental problems related to landfill gas and non-methanic organic compounds. The increasing number of sensors and availability of remotely sensed images along with rapid development of spatial technology are helping with effective landfill site selection. The present study used fuzzy membership and the analytical hierarchy process (AHP) in a geo-spatial environment for landfill site selection in the city of Sharjah, United Arab Emirates. Macro- and micro-level factors were considered; the macro-level contained social and economic factors, while the micro-level accounted for geo-environmental factors. The weighted spatial layers were combined to generate landfill suitability and overall suitability index maps. Sensitivity analysis was then carried out to rectify initial theoretical weights. The results showed that 30.25% of the study area had a high suitability index for landfill sites in the Sharjah, and the most suitable site was selected based on weighted factors. The developed fuzzy-AHP methodology can be applied in neighboring regions with similar geo-natural conditions.

Spatiotemporal Analysis and Image Registration for Studying Growth of Transportation Infrastructure in Sharjah City, UAE

Sharjah is the third largest and populous city in the United Arab Emirates (UAE). It is located along the northern coast of the Persian Gulf on the Arabian Peninsula. Throughout the past few decades, Sharjah City has witnessed massive growth in its urban area and infrastructure facilities. Transportation infrastructure, in particular, is a key indicator of growth and development of the city. Transportation is a vital component of the economy and prosperity of any city as it provides easy access to land, moves large volumes of people and vehicles, enables larger markets, and saves time and costs. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, are allowing for efficient identification of change patterns and the prediction of areas of growth. This paper aims to quantify and analyze the spatial–temporal relationship between urban growth and the transportation development that took place at Sharjah City from 1976 until 2014. For growth detection and quantification, linear features extracted automatically from multi-temporal Landsat registered images were adopted as the basis of change detection where pixel-to-pixel subtraction has been implemented. Linear features were also chosen for image registration since they can be reliably extracted from imagery with significantly different geometric and radiometric properties. Digitized features of building and roads have been used as ground-truth of the adopted algorithm. Preliminary results show that the range of growth represented by linear features (building and roads) that occurred during the 1976–2014 period accounts for about 33% of the total area inside Sharjah City.

Assessing the Spatial and Temporal Capacity of a Semi-Enclosed Gulf to Absorb and Release CO 2 Using GIS and Remote Sensing

The increasing CO2 level in the atmosphere is threatening oceans’ ecosystems due to increased CO2 absorption and potential oceans’ acidification. In this study, we used geographic information system (GIS) and remote sensing (RS) techniques, coupled with chemical–mathematical models to evaluate the water capacity of a semi-enclosed gulf to absorb and release CO2. The water of the gulf exhibits a wide range of spatial and temporal salinity and temperature variations due to the gulf location in a hot, arid region, the high water evaporation rate, and the unique water circulation pattern. In this study, GIS and RS data were used to assess the spatial and temporal distributions of surface temperatures and salinity of the gulf, which, in turn, were used to assess the capacity of the gulf waters to absorb and release CO2. The results confirmed the profound impact of salinity and temperature on the CO2 absorption and release capacity of the gulf, which highly influences potential acidification of the gulf waters.