Object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine

ABSTRACT

This study evaluates the potential of object-based image analysis in combination with supervised machine learning to identify urban structure type patterns from Landsat Thematic Mapper (TM) images. The main aim is to assess the influence of several critical choices commonly made during the training stage of a learning machine on the classification performance and to give recommendations for classifier-dependent intelligent training. Particular emphasis is given to assess the influence of size and class distribution of the training data, the approach of training data sampling (user-guided or random) and the type of training samples (squares or segments) on the classification performance of a Support Vector Machine (SVM). Different feature selection algorithms are compared and segmentation and classifier parameters are dynamically tuned for the specific image scene, classification task, and training data. The performance of the classifier is measured against a set of reference data sets from manual image interpretation and furthermore compared on the basis of landscape metrics to a very high resolution reference classification derived from light detection and ranging (lidar) measurements. The study highlights the importance of a careful design of the training stage and dynamically tuned classifier parameters, especially when dealing with noisy data and small training data sets. For the given experimental set-up, the study concludes that given optimized feature space and classifier parameters, training an SVM with segment-shaped samples that were sampled in a guided manner and are balanced between the classes provided the best classification results. If square-shaped samples are used, a random sampling provided better results than a guided selection. Equally balanced sample distributions outperformed unbalanced training sets.

KEYWORDS:

• Machine learning
• object-based image analysis
• urban structure types
• Landsat
• Haiti

Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for suggestions which helped to improve this paper and K. Fleming for English language revision. This study has been supported by the SENSUM project (Grant Agreement Number 312972) and a research scholarship jointly funded by the Technical University of Madrid (UPM) and the German Academic Exchange Programme (DAAD).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the German Academic Exchange Programme (DAAD), the Technical University of Madrid, and the SENSUM project [Seventh Framework Programme Grant Agreement Number 312972].