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Remote Sens. 2016, 8(6), 484;

Joint Model and Observation Cues for Single-Image Shadow Detection

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Beijing Advanced Innovation Center for Imaging Technology, Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education Capital Normal University, Beijing 100048, China
State Key Laboratory of Information Engineering, Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz, Norman Kerle and Prasad S. Thenkabail
Received: 3 February 2016 / Revised: 2 June 2016 / Accepted: 3 June 2016 / Published: 8 June 2016
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Shadows, 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
Keywords: shadow detection; bright channel prior (BCP); occlusion estimation; observation cues shadow detection; bright channel prior (BCP); occlusion estimation; observation cues

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Li, J.; Hu, Q.; Ai, M. Joint Model and Observation Cues for Single-Image Shadow Detection. Remote Sens. 2016, 8, 484.

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