Object-oriented building extraction based on visual attention mechanism

Buildings, which play an important role in the daily lives of humans, are a significant indicator of urban development. Currently, automatic building extraction from high-resolution remote sensing images (RSI) has become an important means in urban studies, such as urban sprawl, urban planning, urban heat island effect, population estimation and damage evaluation. In this article, we propose a building extraction method that combines bottom-up RSI low-level feature extraction with top-down guidance from prior knowledge. In high-resolution RSI, buildings usually have high intensity, strong edges and clear textures. To generate primary features, we propose a feature space transform method that consider building. We propose an object oriented method for high-resolution RSI shadow extraction. Our method achieves user accuracy and producer accuracy above 95% for the extraction results of the experimental images. The overall accuracy is above 97%, and the quantity error is below 1%. Compared with the traditional method, our method has better performance on all the indicators, and the experiments prove the effectiveness of the method.