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Context-Based Classification of Urban Blocks According to Their Built-up Structure

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Abstract

This work presents an approach for classifying urban blocks according to their built-up structure based on high-resolution spaceborne InSAR images. Most attributes considered in the classification describe the geometric structure and spatial disposition of the polygon and line features extracted from each block. The feature extraction is carried out on two intensity images acquired at the satellite’s ascending and descending orbits. The strategy used for extracting polygon features is described in detail. We also present a Markov random field model used to perform context-based classification of built-up structures. The model establishes a probabilistic dependency between the class labels of two neighbouring blocks, taking in this way advantage of the fact that blocks with the same structure are frequently clustered. 1695 urban blocks were classified into five general built-up types. It is shown that the context-based classification accuracy is up to 6% more accurate than the standard classification on which the context-based model is based. We hence provide evidence (1) that urban block-based classifications can potentially be improved if context is considered and (2) that general built-up structures can be distinguished to a good extent using available high-resolution spaceborne radar images.

Zusammenfassung

Kontextbasierte  Klassifizierung von Baublöcken aufgrund ihrer Baustruktur. Wir präsentieren einen Ansatz zur Klassifizierung von Baublöcken bezüglich ihrer Stadtstrukturtypen, welcher auf hochaufgelösten InSAR-Satellitenbildern basiert. Baublöcke werden dabei über linienhafte und polygonale Strukturen (Features) beschrieben, wobei deren geometrische Strukturen und räumliche Verteilungen berücksichtigt werden. Die Extraktion dieser Features basiert hierbei auf zwei Intensitätsbildern, die jeweils aus gegenläufigen Aufnahmerichtungen aufgenommen wurden (ascending vs. descending). Darüber hinaus wird ein Markov-Random-Field-Modell zur kontext-basierten Klassifizierung verwendet. Dieses Modell basiert auf der Annahme probabilistischer Abhängigkeiten zwischen den Klassen benachbarter Blöcke, was aufgrund oft gleichartiger Baustruktur in unmittelbarer Nachbarschaft gerechtfertigt erscheint. In dieser Studie werden 1.695 Baublöcke zu fünf Klassen zugewiesen. Die Berücksichtigung des Block-Kontextes hat dabei verglichen mit einem konventionellen Random-Forest-Modell zu einer Verbesserung der Klassifizierungsgenauigkeit von 6% geführt. Die erzielten Ergebnisse zeigen zwei wesentliche Erkenntnisse auf: a) die Möglichkeit der Klassifizierung von Stadtstrukturtypen anhand hochaufgelöster, satellitengestützter InSAR-Bilder, sowie b) die wesentliche Verbesserung der Klassifizierungsergebnisse unter Einbezug der lokalen kontextuellen Informationen.

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Correspondence to Tessio Novack.

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Novack, T., Stilla, U. Context-Based Classification of Urban Blocks According to Their Built-up Structure. PFG 85, 365–376 (2017). https://doi.org/10.1007/s41064-017-0039-7

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Keywords

  • Urban structure types
  • Spaceborne InSAR
  • Context-based classification

Schlagwörter

  • Stadtstrukturtypen
  • InSAR-Satellitenbildern
  • kontext-basierte Klassifizierung