Abstract
The application potential of very high resolution (VHR) remote sensing imagery has been boosted by recent developments in the data acquisition and processing ability of aerial photogrammetry. However, shadows in images contribute to problems such as incomplete spectral information, lower intensity brightness, and fuzzy boundaries, which seriously affect the efficiency of the image interpretation. In this paper, to address these issues, a simple and automatic method of shadow detection is presented. The proposed method combines the advantages of the property-based and geometric-based methods to automatically detect the shadowed areas in VHR imagery. A geometric model of the scene and the solar position are used to delineate the shadowed and non-shadowed areas in the VHR image. A matting method is then applied to the image to refine the shadow mask. Different types of shadowed aerial orthoimages were used to verify the effectiveness of the proposed shadow detection method, and the results were compared with the results obtained by two state-of-the-art methods. The overall accuracy of the proposed method on the three tests was around 90%, confirming the effectiveness and robustness of the new method for detecting fine shadows, without any human input. The proposed method also performs better in detecting shadows in areas with water than the other two methods.
Highlights
Shadows exist in most very high resolution (VHR) orthoimagery and lead to more complex and detailed land-cover features, especially in urban areas [1]
As the height accuracies of the digital surface model (DSM) are usually not as good as its horizontal accuracies, the noise was only added to the height values of the DSM to analyze the extent of the errors generated by the inaccuracy of the geometric models
VHR orthoimagery and the corresponding DSMs can be obtained from unmanned aerial systems (UASs) or aerial images acquired by the traditional airborne platforms
Summary
Shadows exist in most very high resolution (VHR) orthoimagery (with a resolution of up to 1 m) and lead to more complex and detailed land-cover features, especially in urban areas [1]. Accurate and automatic shadow detection is an active area of research [8,9]. Most of the research into shadow detection is based on natural imagery and videos [10], and relatively few studies have addressed shadow detection in VHR orthoimagery with compound land-cover features and a broader variety of features co-existing in a scene [11,12]. The current methods for shadow detection can be divided into three types [11,12,13]: (1) property-based methods [9,13,14,15,16,17,18,19,20];
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