Abstract
Shadows are likely to cause flaws in color interpretation, loss of image information or deformation of objects; this is a relevant issue owing to the fact that with the development of unmanned aerial vehicles and satellite devices, object detection by aerial images has become an essential aim to research works. The main contribution of this article is the development of an algorithm that processes shadow detection by detaching the channels red, green, blue and an additional low saturation channel on individual masks; having created those masks, the task of shadow detection is accomplished by conducting a pixel-by-pixel basis with the aid of a decision tree and statistical descriptors extracted out of the image, where the low saturation channel information leads to improvements on shadow detection accuracy in regions that contain asphalt and concrete. Thus, experimental results show the good behavior of shadow detection algorithm in terms of accuracy, over-segmentation, and under-segmentation, in which the accuracy of obtained shadow detection approaches a 94%.
Highlights
Aerial image analysis is a research field which has aimed at a thorough, growing trend over the past few years due to the wide range of applications
The submitted method was tested using unmanned aerial vehicles (UAV) captured images, in this case the mentioned images were captured with the previously described drone, the resolution of each picture is 3840 × 2880 pixels; it could be noticeable that the saturation and brightness of colors have a different behavior in their statistical distributions compared to satellite images, this difference is noticeable at image saturation values
The fourth image, which is shown at the sub-figure Fig. 7 (g), embeds an industrial zone take, where the shadows cover about the 40 % of the frame as it is shown at its shadow detection result at sub-figure Fig. 7 (h)
Summary
Aerial image analysis is a research field which has aimed at a thorough, growing trend over the past few years due to the wide range of applications. Acquisition systems for taking aerial images were made up by means of a satellite capturing device, which took samples from a considerable distance [1], requiring the use of a high-resolution custom-made camera. Over the past decade, the outbreak of unmanned aerial vehicles (UAV) has been witnessed [3], providing an economically feasible alternative for the average population to take on demand aerial images. This support technology along with the current computing power enables the development of image analysis solutions to be updated under a low cost scheme [4]. Remote sensing solutions based on aerial imaging are extensible to the use of UAV, where
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