A Combined PCA-SIs Classification Approach for Delineating Built-up Area from Remote Sensing Data

  • Khaled HazaymehEmail author
  • Mostafa K. Mosleh
  • Abdulla M. Al-Rawabdeh
Original Article


The aim of this study is to develop a method for delineating built-up areas based on remote sensing data. The proposed method evaluated 13 spectral indices (SIs) commonly used in assessing land use and land cover (LULC) and selected meaningful indices through a principle component analysis (PCA) and spectral separability analysis. These indices are combined into a built-up delineation index set (BDIS). The development was implemented at the example of the built-up area in Qena city, Egypt. The method was evaluated against ground truth data and one recently developed global product using confusion matrix statistics. The BDIS was computed from indices showing a high loading of each one of the most relevant principle components and high separability at the same time. Subsequently, the selected indices, i.e., the transformed difference vegetation index (TDVI), band ratio for a built-up area (BRBA), and a new built-up area index (NBI), was used as input variables for the supervised classification procedures. The results show an increase in the accuracy of the built-up area delineation using BDIS. The overall, producer’s, user’s accuracies, and Kappa coefficient were 96.3%, 96%, 93%, and 0.946, respectively. The results and a comparison with the global human settlement layer provided by the European Joint Research Center also verified the usefulness of the proposed method for utilizing Landsat 8 OLI imagery data in delineating a built-up area, providing a comprehensive view on built-up area at the local scale.


Principal component analysis Spectral indices Landsat 8 OLI (operational landsat imager) Urban areas Local scale 


Ein kombinierter PCA-SIs-Klassifizierungsansatz zur Abgrenzung des bebauten Gebiets aus Fernerkundungsdaten.

Ziel dieser Untersuchung ist es, ein Verfahren zur Abgrenzung von bebauten Gebieten auf der Grundlage von Fernerkundungsdaten zu entwickeln. Die vorgeschlagene Methode bewertet 13 Spektralindizes (SIs), die häufig zur Kartierung von Landnutzung und Landbedeckung (LULC) herangezogen werden und dient dazu, durch eine Hauptkomponentenanalyse (PCA) und eine spektrale Separabilitätsanalyse aussagekräftige Indizes auszuwählen. Letztere werden zu einem Built-up-Delineation-Index-Set (BDIS) zusammengefasst. Die Entwicklung wurde am Beispiel der Stadt Qena, Ägypten, umgesetzt und anhand von Ground-truth Informationen sowie einem kürzlich entwickelten globalen Produkt mittels Konfusionsmatrizen bewertet. Das BDIS wird aus den Indizes zusammengestellt, die gleichzeitig eine hohe Faktorenladung der relevantesten Hauptkomponenten und gleichzeitig hohe spektrale Trennbarkeit der urbanen Klasse aufweisen. Die ausgewählten Indizes, im Beispiel der Transformed Difference Vegetation Index (TDVI), die Band Ratio for a Built-up Area (BRBA), und der sogenannte New Built-up area Index (NBI), werden als Eingangsgrößen für eine überwachte Klassifizierung verwendet. Die Ergebnisse zeigen eine Erhöhung der Genauigkeit bei der Abgrenzung bebauter Fläche mit BDIS. Die Gesamtgenauigkeit, die Producer’s und User’s Accuracy sowie der Kappa-Koeffizient lagen bei 96.3%, 96%, 93% bzw. 0.946. Die Ergebnisse und ein Vergleich mit dem Global Human Settlement Layer des European Joint Research Center bestätigen auch die Nützlichkeit der vorgeschlagenen Methode zur Verwendung von Landsat 8 OLI-Bilddaten bei der Beschreibung eines bebauten Gebietes auf lokaler Ebene.


Hauptkomponentenanalyse Spektrale indizes Landsat 8 OLI (Operational landsat imager) Urbane Fernerkundung Lokaler Maßstab 



We greatly appreciate USGS and NASA for providing the required data at no cost. Also, we would like Yarmouk University for providing partial support to this research.


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Copyright information

© Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2019

Authors and Affiliations

  • Khaled Hazaymeh
    • 1
    Email author
  • Mostafa K. Mosleh
    • 2
  • Abdulla M. Al-Rawabdeh
    • 3
  1. 1.Department of Geography, Faculty of ArtsYarmouk UniversityIrbidJordan
  2. 2.Department of Geography, Faculty of ArtsSouth Valley UniversityQenaEgypt
  3. 3.Department of Earth and Environmental Sciences, Faculty of ScienceYarmouk UniversityIrbidJordan

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