SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images.

ABSTRACT: Building detection in high-resolution satellite images has received great attention, as it is important to increase the accuracy of urban planning. The building boundary detection in the desert environment is a real challenge due to the nature of low contrast images in the desert environment. The traditional computer vision algorithms for building boundary detection lack scalability, robustness, and accuracy. On the other hand, deep learning detection algorithms have not been applied to such low contrast satellite images. So, there is a real need to employ deep learning algorithms for building detection tasks in low contrast high-resolution images. In this paper, we propose a novel building detection method based on a single-shot multi-box (SSD) detector. We develop the state-of-the-art SS

SUBMITTER: Shahin AI 

PROVIDER: S-EPMC8592250 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature