Joint Model and Observation Cues for Single-Image Shadow Detection
AbstractShadows, which are cast by clouds, trees, and buildings, degrade the accuracy of many tasks in remote sensing, such as image classification, change detection, object recognition, etc. In this paper, we address the problem of shadow detection for complex scenes. Unlike traditional methods which only use pixel information, our method joins model and observation cues. Firstly, we improve the bright channel prior (BCP) to model and extract the occlusion map in an image. Then, we combine the model-based result with observation cues (i.e., pixel values, luminance, and chromaticity properties) to refine the shadow mask. Our method is suitable for both natural images and satellite images. We evaluate the proposed approach from both qualitative and quantitative aspects on four datasets. The results demonstrate the power of our method. It shows that the proposed method can achieve almost 85% F-measure accuracy both on natural images and remote sensing images, which is much better than the compared state-of-the-art methods. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Li, J.; Hu, Q.; Ai, M. Joint Model and Observation Cues for Single-Image Shadow Detection. Remote Sens. 2016, 8, 484.
Li J, Hu Q, Ai M. Joint Model and Observation Cues for Single-Image Shadow Detection. Remote Sensing. 2016; 8(6):484.Chicago/Turabian Style
Li, Jiayuan; Hu, Qingwu; Ai, Mingyao. 2016. "Joint Model and Observation Cues for Single-Image Shadow Detection." Remote Sens. 8, no. 6: 484.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.