Object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine. Issue 17 (1st September 2016)

• Record Type: Journal Article
• Authors: Wieland, Marc; Torres, Yolanda; Pittore, Massimiliano; Benito, Belén
• Is Part Of: International journal of remote sensing. Volume 37:Issue 17(2016)
• Page Start: 4059
• Page End: 4083
• Publication Date: 2016-09-01
• Subjects: Machine learning -- object-based image analysis -- urban structure types -- Landsat -- Haiti; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1207261
• Languages: English
• ISSNs: 0143-1161

Abstract

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.