Abstract
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.
Highlights
Shadows, which are cast by elevated objects, such as buildings, trees, and clouds, are ever-present phenomena in remote sensing and computer vision
Our method is suitable for both natural images and remote sensing images
The accuracy of non-joint maps significantly decreases when the scenes become complex; and (2) the near-infrared channel is more suitable for shadow detection than the Imean channel because our approach achieved a better accuracy on complex scenes than that of the simple ones
Summary
Shadows, which are cast by elevated objects, such as buildings, trees, and clouds, are ever-present phenomena in remote sensing and computer vision. Shadows can degrade the accuracy of several tasks (e.g., image classification [1], change detection [2], object recognition [3], image segmentation [4], etc.) due to spurious boundaries and confusion between shading and reflectivity. For these reasons, shadow detection has become a crucial preprocessing stage of scene interpretation. Wu et al [7] formulated shadow detection as a matting problem, and users were asked to give several strokes to specify shadows and non-shadows Despite these methods being accurate, their requirements will dramatically reduce efficiency. Incorporating them into a fully automatic workflow is difficult
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