Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes

Abstract

This study evaluates the performance of a Support Vector Machine (SVM) classifier to learn and detect changes in single-and multi-temporal X-and L-band Synthetic Aperture Radar (SAR) images under varying conditions. The purpose is to provide guidance on how to train a powerful learning machine for change detection in SAR images and to contribute to a better understanding of potentials and limitations of supervised change detection approaches. This becomes particularly important on the background of a rapidly growing demand for SAR change detection to support rapid situation awareness in case of natural disasters. The application environment of this study thus focuses on detecting changes caused by the 2011 Tohoku earthquake and tsunami disaster, where single polarized TerraSAR-X and ALOS PALSAR intensity images are used as input. An unprecedented reference dataset of more than 18,000 buildings that have been visually inspected by local authorities for damages after the disaster forms a solid statistical population for the performance experiments. Several critical choices commonly made during the training stage of a learning machine are being assessed for their influence on the change detection performance, including sampling approach, location and number of training samples, classification scheme, change feature space and the acquisition dates of the satellite images. Furthermore, the proposed machine learning approach is compared with the widely used change image thresholding. The study concludes that a well-trained and tuned SVM can provide highly accurate change detections that outperform change image thresholding. While good performance is achieved in the binary change detection case, a distinction between multiple change classes in terms of damage grades leads to poor performance in the tested experimental setting. The major drawback of a machine learning approach is related to the high costs of training. The outcomes of this study, however, indicate that given dynamic parameter tuning, feature selection and an appropriate sampling approach, already small training samples (100 samples per class) are sufficient to produce high change detection rates. Moreover, the experiments show a good generalization ability of SVM which allows transfer and reuse of trained learning machines.

Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions

“…The last row (Total) lists the average scores for the collapsed and non-collapsed samples. Our results show the same level of accuracy as the results of Wieland et al [16]. Bai et al [22] addressed a more challenging problem, namely the detection of damaged buildings based on post-event imagery only.…”

“…The results of Wieland et al [16] and Bai et al [22] were chosen for this purpose. Both research groups used the same TerraSAR-X images as in the present study.…”

“…In addition to the images used in our case study, Wieland et al [16] used three further SAR images: a TerraSAR-X image acquired on 19 June 2011 and two ALOS PALSAR images acquired on 5 October 2010 and 7 April 2011. Wieland et al [16] performed several tests to assess the effects of the number of images, the number of features, etc. For the present comparison, the results they obtained using the same images as in our study are used.…”

“…Consequently, more robust and versatile calibration methods are needed. To this end, machine learning techniques have been successfully applied [16][17][18][19][20][21][22]. Calibration using supervised machine learning is performed based on training samples, that is a set of features for which the corresponding damage states are known in advance.…”

“…Official reports from the government provide some of the most reliable information that can be used as training data; however, such reports usually become available only after several weeks or months. To reduce the time required to retrieve training data, Wieland et al [16] performed a quantitative evaluation of the influence of the number of training samples on the accuracy of damage classification using the support vector machine (SVM) technique, and these authors concluded that a minimum number of training samples from a small study area can be used to map building damage in a much wider region. Furthermore, remote sensing is defined as the acquisition of information about an object or phenomenon without making physical contact with that object or phenomenon.…”