Abstract
In this paper, we present a novel approach for automatically detecting buildings from multiple heterogeneous and uncalibrated very high-resolution (VHR) satellite images for a rapid response to natural disasters. In the proposed method, a simple and efficient visual attention method is first used to extract built-up area candidates (BACs) from each multispectral (MS) satellite image. After this, morphological building indices (MBIs) are extracted from all the masked panchromatic (PAN) and MS images with BACs to characterize the structural features of buildings. Finally, buildings are automatically detected in a hierarchical probabilistic model by fusing the MBI and masked PAN images. The experimental results show that the proposed method is comparable to supervised classification methods in terms of recall, precision and F-value.
Highlights
IntroductionNatural hazards (e.g., earthquakes) can destroy buildings and often result in serious casualties and huge property losses
Natural hazards can destroy buildings and often result in serious casualties and huge property losses
The lack of appropriate satellite imagery poses a considerable challenge for current rapid building mapping techniques that require little or no human intervention [5], because (1) multiple monocular very high-resolution (VHR) images are obtained from sensors with different spectral, spatial and radiometric characteristics; (2) diverse buildings are scattered throughout areas with different backgrounds, such as plains, mountainous regions, rural or urban areas; and (3) variable imaging environments, such as haze or cloud cover conditions
Summary
Natural hazards (e.g., earthquakes) can destroy buildings and often result in serious casualties and huge property losses. The lack of appropriate satellite imagery poses a considerable challenge for current rapid building mapping techniques that require little or no human intervention [5], because (1) multiple monocular VHR images are obtained from sensors with different spectral, spatial and radiometric characteristics; (2) diverse buildings are scattered throughout areas with different backgrounds, such as plains, mountainous regions, rural or urban areas; and (3) variable imaging environments, such as haze or cloud cover conditions. One study [5] has conducted a thorough review of previous studies on building detection using single monocular remote-sensing images. In the remainder of this section, we will provide an overview of recent work combining multiple features to detect buildings
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