Abstract
Building change detection from very-high-resolution (VHR) urban remote sensing image frequently encounter the challenge of serious false alarm caused by different illumination or viewing angles in bi-temporal images. An approach to alleviate the false alarm in urban building change detection is proposed in this paper. Firstly, as shadows casted by urban buildings are of distinct spectral and shape feature, it adopts a supervised object-based classification technique to extract them in this paper. Secondly, on the opposite direction of sunlight illumination, a straight line is drawn along the principal orientation of building in every extracted shadow region. Starting from the straight line and moving toward the sunlight direction, a rectangular area is constructed to cover partial shadow and rooftop of each building. Thirdly, an algebra and geometry invariant based method is used to abstract the spatial topological relationship of the potential unchanged buildings from all central points of the rectangular area. Finally, based on an oriented texture curvature descriptor, an index is established to determine the actual false alarm in building change detection result. The experiment results validate that the proposed method can be used as an effective framework to alleviate the false alarm in building change detection from urban VHR image.
Highlights
Urban upgrading and sprawl is considered as one of the worldwide surface component alterations (Hussain et al 2013), and becomes more and more significant with the implementation of new urbanization policy in China
The increased spatial resolution does not facilitate the improvement of the classification accuracy, object-based image analysis (OBIA) which incorporate the spatial feature can be more efficient to deal with VHR images (Qin 2015)
The contribution of this study is to propose a framework to alleviate the false alarm in urban building change detection mainly caused by different view angles of sensors or solar elevation angles in bi-temporal VHR images
Summary
Urban upgrading and sprawl is considered as one of the worldwide surface component alterations (Hussain et al 2013), and becomes more and more significant with the implementation of new urbanization policy in China. The remote sensing data has become a major source for land-cover and land-use change monitor (Hussain et al 2013), and the VHR remote sensing images, i.e., images having spatial resolution of a meter or less, are more suitable to monitor detailed urban changes occurring at the level of ground structures such as buildings (Huang, Zhang and Zhu 2014). Change detection is to determine and analyse the changes of the ground objects utilizing multitemporal remotely sensed images. Depending on the requirements in change detection from remote sensing image, various techniques have been developed and can be mainly categorized into image difference, image transformation, and classification-based approaches (Wu, Zhang and Zhang 2016), or pixel-based and object-based methods according the unit of analysis (Hebel, Arens and Stilla 2013). It is an increasingly difficult task to select the most suitable algorithm for change detection in specific applications (Tewkesbury et al 2015)
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