An Efficient Algorithm for Detection of Image Stretching Error from a Collection of Images Acquired by Unmanned Aerial Vehicles

摘要

Over the recent years, the advantages of using unmanned aerial vehicles (UAVs) have provided fascinating working areas, particularly for photogrammetric goals. One of the main problems preventing the UAV data to achieve fully automated processing is the image stretching error and reduced resolution or image blurring, which is caused by camera shake during shooting or slow shutter speed. Movements of the sensors may be due to normal motions during the flight, strong winds, lack of proper functioning of the gimbal stabilizer or an operator's lack of skill for properly controlling the drone. Image blurring negatively affects data interpretation and visual analysis, which in turn raises challenges for detection and matching algorithms; as a result, the precision of automatic processing and accuracy of the extracted geometrical information would decrease. Time-consuming and costly manual methods are typically adopted to identify and remove images with radiometric errors. Such methods are tedious, especially for large datasets and bring about high margin of errors. In this paper, an automatic and reliable algorithm is presented to identify and distinguish blurred images. The aim is to extract the saturation blue difference (SBD) parameter from the sets of images. It is known that SBD value has a direct relationship with the amount of blurring. The numerical value of the parameter is determined based on the variation in the extracted edge pixels of blurred and non-blurred images. Evaluation on two datasets indicates that the proposed algorithm based on the selected threshold limit (defined with regard to the geometric and visual requirements of images) for the SBD values can recognize the images with the SBD values less than the threshold value as blurred images with 100% certainty and extract them from the sets of obtained images.