Identification of urban surface materials using high-resolution hyperspectral aerial imagery

La connaissance des matériaux de surface est essentielle pour l’aménagement et la gestion des villes. Avec les avancées en télédétection, particulièrement en imagerie de haute résolution spatiale et spectrale, l’identification et la cartographie détaillée des matériaux de surface en milieu urbain sont maintenant envisageables. Les signatures spectrales décrivent les interactions entre les objets au sol et le rayonnement solaire, et elles sont supposées uniques pour chaque type de matériau de surface. Dans ce projet de recherche nous avons utilisé des images hyperspectrales aériennes du capteur CASI, avec une résolution de 1 m2 et 96 bandes contigües entre 380nm et 1040nm. Ces images couvrant l’île de Montréal (QC, Canada), acquises en 2016, ont été analysées pour identifier les matériaux de surfaces. Pour atteindre ces objectifs, notre méthode d’analyse est fondée sur la comparaison des signatures spectrales d’un pixel quelconque à celles des objets typiques contenues dans des bibliothèques spectrales (matériaux inertes et végétation). Pour mesurer la correspondance entre la signature spectrale d’un objet et la signature spectrale de référence nous avons utilisé deux métriques. La première métrique tient compte de la forme d’une signature spectrale et la seconde, de la différence des valeurs de réflectance entre la signature spectrale observée et celle de référence. Un classificateur flou utilisant ces deux métriques est alors appliqué afin de reconnaître le type de matériau de surface sur la base du pixel. Des signatures spectrales typiques ont été extraites des deux librairies spectrales (ASTER et HYPERCUBE). Des signatures spectrales des objets typiques à Montréal mesurées sur le terrain (spectroradiomètre ASD) ont été aussi utilisées comme références. Trois grandes catégories de matériaux ont été identifiées dans les images pour faciliter la comparaison entre les classifications par source de références spectrales : l’asphalte, le béton et la végétation. La classification utilisant ASTER comme bibliothèque de référence a eu le plus grand taux de réussite avec 92%, suivi par ASD à 88% et finalement HYPERCUBE avec 80%. Nous 5 n’avons pas trouvé de différences significatives entre les trois résultats, ce qui indique que la classification est indépendante de la source des signatures spectrales de référence.Knowledge of surface cover materials is crucial for urban planning and management. With advances in remote sensing, especially in high spatial and spectral resolution imagery, the identification and detailed mapping of surface materials in urban areas based on spectral signatures are now feasible. Spectral signatures describe the interactions between ground objects and solar radiation and are assumed unique for each type of material. In this research, we use airborne CASI images with 1 m2 spatial resolution, with 96 contiguous bands in a spectral range between 367 nm and 1044 nm. These images covering the island of Montreal (Quebec, Canada), obtained in 2016, were analyzed to identify urban surface materials. The objectives of the project were first to find a correspondence between the physical and chemical characteristic of typical surface materials, present in the Montreal scenes, and the spectral signatures within the images. Second, to develop a sound methodology for identifying these surface materials in urban landscapes. To reach these objectives, our method of analysis is based on a comparison of pixel spectral signatures to those contained in a reference spectral library that describe typical surface covering materials (inert materials and vegetation). Two metrics were used in order to measure the correspondence of pixel spectral signatures and reference spectral signature. The first metric considers the shape of a spectral signature and the second the difference of reflectance values between the observed and reference spectral signature. A fuzzy classifier using these two metrics is then applied to recognize the type of material on a pixel basis. Typical spectral signatures were extracted from two spectral libraries (ASTER and HYPERCUBE). Spectral signatures of typical objects in Montreal measured on the ground (ASD spectroradiometer) were also used as reference spectra. Three general types of surface materials (asphalt, concrete, and vegetation) were used to ease the comparison between classifications using these spectral libraries. The classification using ASTER as a reference library had the highest success rate reaching 92%, followed by the field spectra at 88%, and finally with HYPERCUBE at 80%. There were no significant differences in the classification results indicating that the methodology works independently of the source of reference spectral signatures

Impervious Surface Estimation And Mapping Via Remotely Sensed Techniques

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2016Thesis (M.Sc.) -- İstanbul Technical University, Instıtute of Science and Technology, 2016Bu çalışmada iki farklı tarihte elde edilmiş farklı spektral çözünürlükte Landsat uydu görüntülerinin “İstanbul” örneğinde; geçirimsiz alanların ve boş alanların belirlenmesinde kullanılabilirlikleri için uygulanabilecek farklı uzaktan algılama indeksleri ele alınmıştır. Kullanılan yöntemler ile elde edilen yeni işlenmiş görüntülerin performanslarının karşılaştırılması ile Istanbul için kullanılan indeksler arasından en doğru sonuç veren indeksin belirlenmesi hedeflenmiştir. Gerek ülkemizde gerek dünyamızda, şehir alanların yönetilmesi ve geliştirilmesi için doğru ve güvenilir verilere ve veri elde etme yöntemlerine gereksinim duyulmaktadır. Veri desteği ile çok amaçlı haritaların üretilmesi ve bu alanların sürekli izlenmesini sağlayacak teknolojilerin kullanımı gerekmektedir. Uzaktan algılama teknolojisi farklı ölçekte, tekrarlı ve devamlı gözlemler ile yeryüzünü anlamak için kullanılan etkin bir yöntemdir. Farklı çözünürlüklere sahip uzaktan algılanmış görüntüler meteorolojik veri toplama, arazi örtüsü ve kullanımı haritalarının üretilmesi ve değişim tespiti, şehir planlama, iklim değişimi, doğal afetlerin izlenmesi gibi çok sayıda uygulamada etkin ve yaygın olarak kullanılmaktadır. Şehir alanlarında geçirimsiz yüzeylerin (yapay yüzeylerin) belirlenmesi, izlenmesi ve değişimlerinin tespit edilmesi uzaktan algılama teknolojisinin önemli uygulama alanları arasında bulunmaktadır. Geçirimsiz yüzeyler şehir alanlarının büyük bir bölümünü kaplamaktadır. Geçirimsiz yüzeyler ve bunların olumsuz etkileri en yaygın olarak şehirleşme ve endüstirileşme ve bunlara bağlı olarak gözlemlenen hızlı nüfus artışlarının meydana geldiği gelişmekte olan mega şehirlerde gözlenmektedir. Şehir alanlarının plansız ve kontrolsüz büyümesinden dolayı son yıllarda meydana gelen arazi kullanımı ve arazi örtüsü değişimleri ve bunların çevresel fakörler üzerindeki etkileri çok sayıda bilimsel çalışmada incelenmiştir. Arazi kullanımı /örtüsü ve geçirimsiz yüzeyler çevresel çalışmalar ve bunların sonuçlarını kullanan karar vericiler için önemli parametrelerdir. Orta çözünürlükte uydu görüntüleri ve uzaktan algıma teknikleri şehir alanlarının analizi ve sürdürülebilir yönetimi için çok önemli kaynaklardır. Uzaktan algılama indeksleri ile uydu görüntülerinin sınıflandırılması geçirimsiz yüzeylerin ve boş alanların tespit edilmesi ve izlenmesi için yaygın olarak kullanılan etkili bir yöntem olarak kabul edilmektedir. Bu araştırmada, özellikle 1980 yılı sonrasında hızlı nüfus artışı, sanayileşme ve buna bağlı olarak yerleşim alan artışı ve farklı arazi örtüsü değişimlerinin gözlemlendiği İstanbul ili çalışma bölgesi olarak seçilmiştir. Bu tez çalışmasında, İstanbul iline ait geçirimsiz alanların ve boş alanların tespiti için farklı spektral özelliklere sahip uzaktan algılama verilerinin performanslarını analiz etmek amacı ile ücretsiz olarak elde edilebilen orta mekansal çözünürlüğe sahip Landsat 5 TM ve Landsat 8 OLI&TIRS görüntüleri kullanılmıştır. Uygulamanın ilk aşamasında, orta mekansal çözünürlüğe sahip 20 Agustos 2003 tarihli Landsat 5 TM ve 6 Eylül 2015 tarihli Landsat 8 OLI& TIRS görüntüleri elde edilmiştir. Bu Çalışmada, literatürde yaygın olarak kullanılan farklı uzaktan algılama indeksleri ile Istanbul geçirimsiz alanlarının ve boş alanların belirlenmesi ve farklı indekslerin performanslarının karşılaştırılması amaçlanmıştır. Çalışma kapsamında kullanılan uzaktan algılama indeksleri UI (Urban Index (Şehir Indeksi)), IBI (Index Based Built Up Index (Indeks tabanlı yapay alan indeksi)), NDBI (Normalized Difference Built-Up Index (Normalleştirilmiş Fark Yapay Alan Indeksi)), NDBal (Normalized Difference Bare Land Index (Normalleştirilmiş Fark Boş Alan Indeksi)) ve EBBI (Enhanced Based Built Up Index (Zenginleştirilmiş Yapay Alan ve Boş Alan Indeksi) olarak belirlenmiştir. Indekslerin tümü Landsat 5 TM ve Landsat 8 OLI & TIRS görüntülerinin ilgili bantları kullanılarak hesaplanmıştır. Özellikle seçilen indeksler arasında ısıl bant kullanılan tek indeks olan EBBI indeksi bu çalışma ile ilk defa heterojen özelliklere sahip bir mega şehir alanında kullanılmıştır. Ayrıca görüntü bantları kullanılarak hesaplanan farklı indekslerin kullanılması ile hesaplanan tek indeks olma özelliği taşıyan IBI indeksi bu çalışmada hem Landsat 5 TM hem de yeni nesil Landsat 8 OLI & TIRS görüntüsüne uygulanmıştır. Her indeks için görsel yorumlama yöntemi ile eşik değerler üç farklı sınıf için (geçirimsiz yüzeyler, boş alanlar ve diğer) belirlenmiştir. Belirlenen eşik değerler kullanılarak yoğunluk dilimleme yöntemi ile Istanbul için tematik haritalar oluşturulmuştur. Farklı indekslere ait performansların karşılaştırılması için doğruluk değerlendirmesi görsel yorumlama, alan ve uzunluk hesaplamaları için sayısallaştırma, genel doğruluk ve Kappa istatistiği hesaplamaları ile gerçekleştirilmiştir. İstanbul için Landsat 5 TM ve Landsat 8 OLI &TIRS görüntüleri ile geçirimsiz yüzeylerin ve boş alanların doğru ve güvenilir olarak belirlenebildiği indeksler belirlenmiştir. . Sonuç olarak, Landsat 5 TM (20 Agustos 2003) görüntüsü ile en yüksek genel doğruluk EBBI indeksi kullanılarak elde edilmiştir. 2003 tarihli görüntü ile hesaplanan indeksler için genel doğruluk değerleri geçirimsiz yüzeyler ve boş alanlar için karşılaştırıldığında EBBI indeksi kullanılarak yüksek genel doğruluk boş alan için % 93, IBI indeksi kullanılarak yüksek genel doğruluk geçirimsiz yüzeyler için % 84, NDBI indeksi kullanılarak yüksek genel doğruluk geçirimsiz yüzey için % 73, UI indeksi ile yüksek genel doğruluk geçirimsiz yüzeyler için % 90 ve NDBal indeksi ile yüksek genel doğruluk boş alan için % 88 olarak hesaplanmıştır. Landsat 8 OLI & TIRS (06 Eylül 2015) görüntüsü ile en yüksek genel doğruluk NDBal indeksi kullanılarak % 90.3 olarak edilmiştir. 2015 tarihli görüntü ile hesaplanan indeksler için genel doğruluk değerleri geçirimsiz yüzeyler ve boş alanlar için karşılaştırıldığında EBBI indeksi kullanılarak yüksek genel doğruluk geçirimsiz alanlar için % 87, IBI indeksi kullanılarak yüksek genel doğruluk boş alan için % 89, NDBI indeksi kullanılarak yüksek genel doğruluk boş alan için % 86, UI indeksi ile yüksek genel doğruluk boş alan için % 87 ve NDBal indeksi ile yüksek genel doğruluk boş alan için % 92 olarak hesaplanmıştır. Bu çalışma ile uzaktan algılama indeksleri uzun ve sürekli zaman arşivine sahip orta çözünürlüklü çok bantlı Landsat görüntüleri ile heterojen bir yapıya sahip Istanbul için kabul edilebilir doğrulukta geçirimsiz yüzeylerin ve boş alanların belirlenmesinde etkin olarak kullanılabileceğini gösteren bir altlık çalışma gerçekleştirilmiştir.Land cover and land use of earth has significantly been changed by unplanned and uncontrolled expansion of urban areas throughout time and the increasing change by the passing years, were verified by various studies. Only remote sensing from space, can provide the global, repeatable, continuous observations of processes, needed to understand the Earth system as a whole. Remote sensing data can be used in several applications such as, meteorological data collection, change detection and land cover mapping, urban planning, climate change, disaster monitoring and so on. One of the important applications of remote sensing is to detect, monitor and map impervious surfaces in urban areas. Impervious surfaces are present in many areas of the world. The areas most affected by this change are developing countries and the metropolitan cities, which are under pressures due to an unprecedented increase in population growth and developments as the result of urbanization and industrialization. Land Use and Land Cover (LULC) and Impervious Surface Area (ISA) are important parameters for many environmental studies, and serve as an essential tool for decision makers and stakeholders in Urban & Regional planning. Istanbul is among the cities that is facing the problem of a large amount of land cover changes because of various factors specifically urbanization and population growth. Urbanization phenomena with all its advantages for people, causes the large portion of land cover and land use change in mega cities such as Istanbul. Available medium spatial resolution satellite imagery, in combination with Remote Sensing techniques, are very important sources in analyzing the urban areas. Classification of Landsat images using remote sensing indices are the mostly used and simple methodology for detecting and extracting the impervious surface and bare land classes. In this study, the Impervious surface area and bare land determination of Istanbul from years, 2003 and 2015 were analyzed using different remote sensing indices such as UI (Urban Index), IBI (Index Based Built Up Index), NDBI (Normalized Difference Built-Up Index), NDBal (Normalized Difference Bare Land Index) and EBBI (Enhanced Based Built Up Index). Multitemporal data were acquired from freely available LANDSAT 5 TM and LANDSAT 8 OLI & TIRS satellite in two different dates (20-August-2003, 06-September-2015). In the introduction part of the thesis, definition of remote sensing and a brief summary of the topic are given. In the second chapter, general information about artificial surface is provided. In the third chapter, the electromagnetic spectrum, radiation and electromagnetic interaction with Earth’s features, spectral reflectance and remote sensing of artificial surfaces are explained. In the fourth chapter, digital image processing is defined and indices are explained. In the application chapter, the study area Istanbul and the data used including satellite data are defined. In this study, it is aimed to evaluate the impervious surfaces and bare lands in the study area, using remote sensing indices; and to analyze the potential of each urban index specially EBBI index since this study is among the first to apply this index on a heterogenous urban area and finally to produce the impervious surface map of Istanbul. With regard to the above objectives different built-up and bare soil indices were applied to analyze impervious surface and bare soil and threshold values for three categories as impervious surface, bare soil and others was decided by visual interpratation and then dencity slicing was applied for producing thematic map of Istanbul. Other category includes green areas, forest areas and water surfaces. Accuracy assessment was calculated for each of indices to determine how accurately the indices worked to solve classification problem of impervious surface and bare lands in heterogenous urban areas by implementing visual interpretation, digitizing for area and length, overall accuracy and Kappa statistics. As the result of study, UI index has 90% overall accuracy for impervious surface, EBBI index peresents 93% overall accuracy for bare land, IBI index has 84% overall accuracy for impervious surface, NDBI index has 73% overall accuracy for impervious surface and NDBaI has 88% overall accuracy for bare land. EBBI index shows the highest impervious surface area of 121,421 ha and for the bare land NDBaI index features highest area of 67,255 ha for Landsat 5 TM August 20, 2003 data. For Landsat 8 OLI & TIRS September 06, 2015 EBBI index shows 87% overall accuracy for impevious surface, NDBaI index has 92% overall acuuracy for bare land, IBI index shows 89% overall accuracy for bare land, NDBI has 86% overall accuracy for bare land and UI has 87% overall accuracy for bare land. EBBI index presents the highest impervious surface area of 137,406 ha and NDBaI Index confirms highest bare land area of 56,508 ha. It can be asserted that indices generated by utilizing the multispectral feature of Landsat imagery, which has the long-term archive of satellite images, can be used for determining the land cover/use changes. In addition, some recommendations for the future research and the problems which encountered during analysis are outlined.Yüksek LisansM.Sc

Urban scene description for a multi scale classication of high resolution imagery case of Cape Town urban Scene

Includes abstract.Includes bibliographical references.In this paper, a multi level contextual classification approach of the City of Cape Town, South Africa is presented. The methodology developed to identify the different objects using the multi level contextual technique comprised three important phases

Green Open Space and Barren Land Mapping for Flood Mitigation in Jakarta, the Capital of Indonesia

High levels of rainfall, tidal flooding, land subsidence, intensified urban development, scarce barren land and a shortage of green open spaces (GOS) are contributing factors to the persistent flooding in Jakarta. Therefore, this study was conducted to map the GOS, built-up, and barren land in the city in order to calculate the biopore infiltration hole (LRB) potential for water infiltration as part of Jakarta's flood mitigation efforts using the Landsat 8 operational land imager (OLI). The Landsat data acquired on September 11, 2019, with path/row 122/064 were processed using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) method for the radiometric correction, and geometric correction with a root mean square error (RMSE) of 7.57 meters. Moreover, the normalized difference vegetation index (NDVI) was applied to classify the GOS, the normalized difference built-up index (NDBI) for the built-up areas, and the normalized difference barren land index (NDBaI) for barren land areas which were further confirmed using NDBI to distinguish them from the built-up areas. It is also important to note that the LRB potential was calculated by adding the GOS and barren land, dividing the result by the ideal land area multiplied by the ideal number of holes. The results showed that the GOS, built-up area, and barren land were 8.34%, 85.29%, and 2.48%, respectively. Furthermore, the LRB potential through the optimization of GOS and barren land was found to be 70.06 km2 and produced 16,816,248 LRB (18.27% of total needed). The realization of this value is expected to reduce the potential inundation in Jakarta by 15.6%

Road Condition Mapping by Integration of Laser Scanning, RGB Imaging and Spectrometry

Roads are important infrastructure and are primary means of transportation. Control and maintenance of roads are substantial as the pavement surface deforms and deteriorates due to heavy load and influences of weather. Acquiring detailed information about the pavement condition is a prerequisite for proper planning of road pavement maintenance and rehabilitation. Many companies detect and localize the road pavement distresses manually, either by on-site inspection or by digitizing laser data and imagery captured by mobile mapping. The automation of road condition mapping using laser data and colour images is a challenge. Beyond that, the mapping of material properties of the road pavement surface with spectrometers has not yet been investigated. This study aims at automatic mapping of road surface condition including distress and material properties by integrating laser scanning, RGB imaging and spectrometry. All recorded data are geo-referenced by means of GNSS/ INS. Methods are developed for pavement distress detection that cope with a variety of different weather and asphalt conditions. Further objective is to analyse and map the material properties of the pavement surface using spectrometry data. No standard test data sets are available for benchmarking developments on road condition mapping. Therefore, all data have been recorded with a mobile mapping van which is set up for the purpose of this research. The concept for detecting and localizing the four main pavement distresses, i.e. ruts, potholes, cracks and patches is the following: ruts and potholes are detected using laser scanning data, cracks and patches using RGB images. For each of these pavement distresses, two or more methods are developed, implemented, compared to each other and evaluated to identify the most successful method. With respect to the material characteristics, spectrometer data of road sections are classified to indicate pavement quality. As a spectrometer registers almost a reflectivity curve in VIS, NIR and SWIR wavelength, indication of aging can be derived. After detection and localization of the pavement distresses and pavement quality classes, the road condition map is generated by overlaying all distresses and quality classes. As a preparatory step for rut and pothole detection, the road surface is extracted from mobile laser scanning data based on a height jump criterion. For the investigation on rut detection, all scanlines are processed. With an approach based on iterative 1D polynomial fitting, ruts are successfully detected. For streets with the width of 6 m to 10 m, a 6th order polynomial is found to be most suitable. By 1D cross-correlation, the centre of the rut is localized. An alternative method using local curvature shows a high sensitivity to the shape and width of a rut and is less successful. For pothole detection, the approach based on polynomial fitting generalized to two dimensions. As an alternative, a procedure using geodesic morphological reconstruction is investigated. Bivariate polynomial fitting encounters problems with overshoot at the boundary of the regions. The detection is very successful using geodesic morphology. For the detection of pavement cracks, three methods using rotation invariant kernels are investigated. Line Filter, High-pass Filter and Modified Local Binary Pattern kernels are implemented. A conceptual aspect of the procedure is to achieve a high degree of completeness. The most successful variant is the Line Filter for which the highest degree of completeness of 81.2 % is achieved. Two texture measures, the gradient magnitude and the local standard deviation are employed to detect pavement patches. As patches may differ with respect to homogeneity and may not always have a dark border with the intact pavement surface, the method using the local standard deviation is more suitable for detecting the patches. Linear discriminant analysis is utilized for asphalt pavement quality analysis and classification. Road pavement sections of ca. 4 m length are classified into two classes, namely: “Good” and “Bad” with the overall accuracy of 77.6 %. The experimental investigations show that the developed methods for automatic distress detection are very successful. By 1D polynomial fitting on laser scanlines, ruts are detected. In addition to ruts also pavement depressions like shoving can be revealed. The extraction of potholes is less demanding. As potholes appear relatively rare in the road networks of a city, the road segments which are affected by potholes are selected interactively. While crack detection by Line Filter works very well, the patch detection is more challenging as patches sometimes look very similar to the intact surface. The spectral classification of pavement sections contributes to road condition mapping as it gives hints on aging of the road pavement.Straßen bilden die primären Transportwege für Personen und Güter und sind damit ein wichtiger Bestandteil der Infrastruktur. Der Aufwand für Instandhaltung und Wartung der Straßen ist erheblich, da sich die Fahrbahnoberfläche verformt und durch starke Belastung und Wettereinflüsse verschlechtert. Die Erfassung detaillierter Informationen über den Fahrbahnzustand ist Voraussetzung für eine sachgemäße Planung der Fahrbahnsanierung und -rehabilitation. Viele Unternehmen detektieren und lokalisieren die Fahrbahnschäden manuell entweder durch Vor-Ort-Inspektion oder durch Digitalisierung von Laserdaten und Bildern aus mobiler Datenerfassung. Eine Automatisierung der Straßenkartierung mit Laserdaten und Farbbildern steht noch in den Anfängen. Zudem werden bisher noch nicht die Alterungszustände der Asphaltdecke mit Hilfe der Spektrometrie bewertet. Diese Studie zielt auf den automatischen Prozess der Straßenzustandskartierung einschließlich der Straßenschäden und der Materialeigenschaften durch Integration von Laserscanning, RGB-Bilderfassung und Spektrometrie ab. Alle aufgezeichneten Daten werden mit GNSS / INS georeferenziert. Es werden Methoden für die Erkennung von Straßenschäden entwickelt, die sich an unterschiedliche Datenquellen bei unterschiedlichem Wetter- und Asphaltzustand anpassen können. Ein weiteres Ziel ist es, die Materialeigenschaften der Fahrbahnoberfläche mittels Spektrometrie-Daten zu analysieren und abzubilden. Derzeit gibt es keine standardisierten Testdatensätze für die Evaluierung von Verfahren zur Straßenzustandsbeschreibung. Deswegen wurden alle Daten, die in dieser Studie Verwendung finden, mit einem eigens für diesen Forschungszweck konfigurierten Messfahrzeug aufgezeichnet. Das Konzept für die Detektion und Lokalisierung der wichtigsten vier Arten von Straßenschäden, nämlich Spurrillen, Schlaglöcher, Risse und Flickstellen ist das folgende: Spurrillen und Schlaglöcher werden aus Laserdaten extrahiert, Risse und Flickstellen aus RGB- Bildern. Für jede dieser Straßenschäden werden mindestens zwei Methoden entwickelt, implementiert, miteinander verglichen und evaluiert um festzustellen, welche Methode die erfolgreichste ist. Im Hinblick auf die Materialeigenschaften werden Spektrometriedaten der Straßenabschnitte klassifiziert, um die Qualität des Straßenbelages zu bewerten. Da ein Spektrometer nahezu eine kontinuierliche Reflektivitätskurve im VIS-, NIR- und SWIR-Wellenlängenbereich aufzeichnet, können Merkmale der Asphaltalterung abgeleitet werden. Nach der Detektion und Lokalisierung der Straßenschäden und der Qualitätsklasse des Straßenbelages wird der übergreifende Straßenzustand mit Hilfe von Durchschlagsregeln als Kombination aller Zustandswerte und Qualitätsklassen ermittelt. In einem vorbereitenden Schritt für die Spurrillen- und Schlaglocherkennung wird die Straßenoberfläche aus mobilen Laserscanning-Daten basierend auf einem Höhensprung-Kriterium extrahiert. Für die Untersuchung zur Spurrillen-Erkennung werden alle Scanlinien verarbeitet. Mit einem Ansatz, der auf iterativer 1D-Polynomanpassung basiert, werden Spurrillen erfolgreich erkannt. Für eine Straßenbreite von 8-10m erweist sich ein Polynom sechsten Grades als am besten geeignet. Durch 1D-Kreuzkorrelation wird die Mitte der Spurrille erkannt. Eine alternative Methode, die die lokale Krümmung des Querprofils benutzt, erweist sich als empfindlich gegenüber Form und Breite einer Spurrille und ist weniger erfolgreich. Zur Schlaglocherkennung wird der Ansatz, der auf Polynomanpassung basiert, auf zwei Dimensionen verallgemeinert. Als Alternative wird eine Methode untersucht, die auf der Geodätischen Morphologischen Rekonstruktion beruht. Bivariate Polynomanpassung führt zu Überschwingen an den Rändern der Regionen. Die Detektion mit Hilfe der Geodätischen Morphologischen Rekonstruktion ist dagegen sehr erfolgreich. Zur Risserkennung werden drei Methoden untersucht, die rotationsinvariante Kerne verwenden. Linienfilter, Hochpassfilter und Lokale Binäre Muster werden implementiert. Ein Ziel des Konzeptes zur Risserkennung ist es, eine hohe Vollständigkeit zu erreichen. Die erfolgreichste Variante ist das Linienfilter, für das mit 81,2 % der höchste Grad an Vollständigkeit erzielt werden konnte. Zwei Texturmaße, nämlich der Betrag des Grauwert-Gradienten und die lokale Standardabweichung werden verwendet, um Flickstellen zu entdecken. Da Flickstellen hinsichtlich der Homogenität variieren können und nicht immer eine dunkle Grenze mit dem intakten Straßenbelag aufweisen, ist diejenige Methode, welche die lokale Standardabweichung benutzt, besser zur Erkennung von Flickstellen geeignet. Lineare Diskriminanzanalyse wird zur Analyse der Asphaltqualität und zur Klassifikation benutzt. Straßenabschnitte von ca. 4m Länge werden zwei Klassen („Gut“ und „Schlecht“) mit einer gesamten Accuracy von 77,6 % zugeordnet. Die experimentellen Untersuchungen zeigen, dass die entwickelten Methoden für die automatische Entdeckung von Straßenschäden sehr erfolgreich sind. Durch 1D Polynomanpassung an Laser-Scanlinien werden Spurrillen entdeckt. Zusätzlich zu Spurrillen werden auch Unebenheiten des Straßenbelages wie Aufschiebungen detektiert. Die Extraktion von Schlaglöchern ist weniger anspruchsvoll. Da Schlaglöcher relativ selten in den Straßennetzen von Städten auftreten, werden die Straßenabschnitte mit Schlaglöchern interaktiv ausgewählt. Während die Rissdetektion mit Linienfiltern sehr gut funktioniert, ist die Erkennung von Flickstellen eine größere Herausforderung, da Flickstellen manchmal der intakten Straßenoberfläche sehr ähnlich sehen. Die spektrale Klassifizierung der Straßenabschnitte trägt zur Straßenzustandsbewertung bei, indem sie Hinweise auf den Alterungszustand des Straßenbelages liefert

Policy Document on Earth Observation for Urban Planning and Management: State of the Art and Recommendations for Application of Earth Observation in Urban Planning

A policy document on earth observation for urban planning and management resulting from a workshop held in Hong Kong in November 2006 is presented. The aim of the workshop was to provide a forum for researchers and scientists specializing in earth observation to interact with practitioners working in different aspects of city planning, in a complex and dynamic city, Hong Kong. A summary of the current state of the art, limitations, and recommendations for the use of earth observation in urban areas is presented here as a policy document

Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages