An unsupervised approach for building change detection in VHR remote sensing imagery

Abstract

Continuous monitoring of changes is one of the intrinsic capabilities of remote sensing. With respect to the increasing availability of very high resolution (VHR) remote sensing imagery, the capabilities become more and more relevant for rapidly changing complex urban environments. Therefore highly automatic concepts for analysis of changes are more and more required. In addition, appropriate unsupervised change detection approaches should be capable of handling VHR remote sensing data acquired by different sensors with possibly deviating viewing geometries and varying solar illumination angles. Especially concerning the high level of detail present in VHR imagery over urban areas, object-based methods facilitate change detection in this context. Another asset of the object-based analysis is that it inherently tackles discrepancies in exact spatial, spectral and radiometric matching of VHR image pairs. The aim of this paper is to present a novel object-based approach for unsupervised change detection with focus on individual buildings. The object-based paradigm allows the characterization of image objects by a large number of features that can be derived from the multi-temporal VHR image pairs. Modern VHR space-borne sensors like QuickBird, GeoEye, WorldView or Pléiades offer at least four multispectral image channels at spatial resolutions of approximately 50 centimeters. Different groups of features (e.g. 1st and 2nd order statistics of image channels) are compared regarding their discriminative power for building change detection. Principal component analysis is used as a feature extraction technique which compensates redundancies among features and enables proper data representation in the multi-dimensional feature space. For discrimination of changed and unchanged buildings, a comprehensive number of clustering algorithms from different methodological categories are evaluated regarding their capability of handling this two-class change detection problem. Overall, the proposed approach returned viable results which show the general suitability of clustering for object-based change detection. In detail, highest consistent accuracies were achieved using the algorithms k-means, partitioning around medoids, genetic k-means and the self-organizing map (SOM) clustering technique. We conclude that the proposed approach offers new benefits for building change detection particularly in rapidly changing urban settings, such as in Chinese cities.