Geographic object-based image analysis

The field of earth observation (EO) has seen tremendous development over recent time owing to the increasing quality of the sensor technology and the increasing number of operational satellites launched by several space organizations and companies around the world. Traditionally, the satellite data is analyzed by only considering the spectral characteristics measured at a pixel. The spatial relations and context were often ignored. With the advent of very high resolution satellite sensors providing a spatial resolution of ≤ 5m, the shortfalls of traditional pixel-based image processing techniques became evident. The need to identify new methods then led to focusing on the so called object-based image analysis (OBIA) methodologies. Unlike the pixel-based methods, the object-based methods which are based on segmenting the image into homogeneous regions use the shape, texture and context associated with the patterns thus providing an improved basis for image analysis. The remote sensing data normally has to be processed in a different way to that of the other types of images. In the geographic sense OBIA is referred to as Geographic Object-Based Image Analysis (GEOBIA), where the GEO pseudo prefix emphasizes the geographic components. This thesis will provide an overview of the principles of GEOBIA, describe some fundamentally new contributions to OBIA in the geographical context and, finally, summarize the current status with ideas for future developments

An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH) morphology and context) and graph-analysis algorithms (e.g., Dijkstra path). The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels

The Role of Small Satellites in the Establishment of the Gulf Region\u27s First Graduate Level Space Studies Program

Yahsat, Northrop Grumman, and Khalifa University created the Gulf region\u27s first master\u27s level advanced studies space program. To date this program has graduated three classes of master\u27s students and received accolades from the UAE Space Agency and Abu Dhabi\u27s Mubadala Investment Company. The program\u27s primary goal is to develop the resources and work force that the UAE requires to establish itself as a space-faring nation. Integral to this program are small satellites, initially used to train and educate the students and ultimately growing to accommodate new technologies and scientific payloads developed in the UAE. The first of these small satellites, a 1U CubeSat named MYSat-1 was launched in November of 2018 and deployed from the Northrop Grumman Cygnus on 13 February, 2019. In this paper we present the role small satellites played in the establishment of this program. We discuss the challenges of establishing a satellite program at a university without a formal aerospace curriculum and how the small satellite became the anchor project for the student development. In this context, we explore the advantages of making use of the broadly established small satellite COTS component marketplace relative to the didactical benefits to be gained from having the students develop the new hardware in-house. Finally, we review the process of setting up a new small satellite lab established to be used as the primary resource for developing and testing small satellites in the country

Geographic object-based image analysis

The field of earth observation (EO) has seen tremendous development over recent time owing to the increasing quality of the sensor technology and the increasing number of operational satellites launched by several space organizations and companies around the world. Traditionally, the satellite data is analyzed by only considering the spectral characteristics measured at a pixel. The spatial relations and context were often ignored. With the advent of very high resolution satellite sensors providing a spatial resolution of ≤ 5m, the shortfalls of traditional pixel-based image processing techniques became evident. The need to identify new methods then led to focusing on the so called object-based image analysis (OBIA) methodologies. Unlike the pixel-based methods, the object-based methods which are based on segmenting the image into homogeneous regions use the shape, texture and context associated with the patterns thus providing an improved basis for image analysis. The remote sensing data normally has to be processed in a different way to that of the other types of images. In the geographic sense OBIA is referred to as Geographic Object-Based Image Analysis (GEOBIA), where the GEO pseudo prefix emphasizes the geographic components. This thesis will provide an overview of the principles of GEOBIA, describe some fundamentally new contributions to OBIA in the geographical context and, finally, summarize the current status with ideas for future developments

Towards Automatic Extraction and Updating of VGI-Based Road Networks Using Deep Learning

This work presents an approach to road network extraction in remote sensing images. In our earlier work, we worked on the extraction of the road network using a multi-agent approach guided by Volunteered Geographic Information (VGI). The limitation of this VGI-only approach is its inability to update the new road developments as it only follows the VGI. In this work, we employ a deep learning approach to update the road network to include new road developments not captured by the existing VGI. The output of the first stage is used to train a Convolutional Neural Network (CNN) in the second stage to generate a general model to classify road pixels. Post-processing is used to correct the undesired artifacts such as buildings, vegetation, occlusions, etc. to generate a final road map. Our proposed method is tested on the satellite images acquired over Abu Dhabi, United Arab Emirates and the aerial images acquired over Massachusetts, United States of America, and is observed to produce accurate results

Toward a near real-time product of air temperature maps from satellite data and in situ measurements in arid environments

International audienceEarth observation data and in situ measurements were used to derive a method for the estimation of Air Temperature (AirT) and thereby produce maps. The methodology was developed and validated using data acquired at five stations located in the United Arab Emirates (UAE) during the year 2012. In the first step, the effect of different variables on the estimation of AirT values was investigated and the variables of high importance were used to build the final model. Land surface temperature (LST), relative humidity, global horizontal irradiance, direct normal irradiance, and diffuse horizontal irradiance were identified as the most important inputs for AirT estimation. Models were developed separately for four different cases based on the seasons (Winter/Summer) and the time of the day (Day/Night). The models were evaluated using jackknife validation on the stations resulting in root mean square errors of 2.25°C (Winter/Day), 2.24°C (Winter/Night), 2.56°C (Summer/Day), and 2.69°C (Summer/Night) and an overall average accuracy of 2.44°C. Finally, the model has been applied on a larger scale by assimilating the relevant Earth observation data and in situ measurements for the creation of AirT maps for the entire country. The resulting AirT maps were generated in near real time at a temporal resolution of 15 min from METEOSAT/SEVIRI LST and the above-mentioned in situ measurements. These maps can be considered as important resources for several applications (e.g., climate studies, solar energy applications, thermal comfort studies, etc.

Linear Versus Nonlinear PCA for the Classification of Hyperspectral Data Based on the Extended Morphological Profiles

International audienceMorphological profiles (MPs) have been proposed in recent literature as aiding tools to achieve better results for classification of remotely sensed data. MPs are in general built using features containing most of the information content of the data, such as the components derived from principal component analysis (PCA). Recently, nonlinear PCA (NLPCA), performed by autoassociative neural network, has emerged as a good unsupervised technique to fit the information content of hyperspectral data into few components. The aim of this letter is to investigate the classification accuracies obtained using extended MPs built from the features of NPCA. A comparison of the two approaches has been validated on two different data sets having different spatial and spectral resolutions/coverages, over the same ground truth, and also using two different classification algorithms. The results show that NLPCA permits one to obtain better classification accuracies than using linear PCA

A Study of Local Climate Zones in Abu Dhabi with Urban Weather Stations and Numerical Simulations

In many cities that have experienced rapid growth like Abu Dhabi, urban microclimate scenarios evolve rapidly as well and it is important to study the urban thermal dynamics continuously. The Local Climate Zone (LCZ) classification considers factors related to the physical properties like surface cover and surface structure of the city which allow to analyze urban heat flows. Abu Dhabi city is rapidly expanding and is characterized by highly heterogeneous types of built forms that comprise mainly of old mid-rise and modern high-rise buildings with varied degrees of vegetation cover in different parts of the city. The fact that it is a coastal city in a desert environment makes it quite unique. This paper presents an approach of studying urban heat flows in such heterogeneous setup. First, the city is classified into local climate zones using images acquired by Landsat Satellite. Numerical simulations are performed in the designated LCZs using a computational fluid dynamics software, Envi-met. The results of Envi-met are calibrated and validated using in-situ measurements across all four seasons. The calibrated models are then applied to study entire Abu Dhabi island across different seasons. The results indicate a clear presence of urban heat island (UHI) effect when averaged over the full day which is varying in different zones. The zones with high vegetation do not show large average UHI effect whereas the effect is significant in densely built zones. The study also validates previous observations on the inversion of UHI effect during the day and in terms of diurnal response

Continuous Mapping and Monitoring Framework for Habitat Analysis in the United Arab Emirates

In 2015, the Environment Agency—Abu Dhabi developed the extensive Abu Dhabi Habitat, Land Use, Land Cover Map based on very high resolution satellite imagery acquired between 2011 and 2013. This was the first integrated effort at such a scale. This information has greatly helped in environmental conservation and preservation activities along with future infrastructure planning. This map has created an excellent baseline and allows efficient monitoring. In this work, we aim to establish a framework for short term updates to the maps to quickly enable efficient planning. We make use of spectral–spatial approaches based on object-based image analysis to adapt the classification scheme. Training examples from the baseline maps and field surveys are used to train classifiers, such as support vector machines (SVM), to build the updated maps. Eventually, the goal is to develop a consistent classification approach and then adapt automatic change detection approaches to extend the baseline maps temporally