Analysis and evaluation of improved algorithm for aerial image homogenization processing

Abstract

The imaging time, light change, sensor lens angle and ground characteristics have all contributed to significant discrepancies in the brightness and colour distribution of the aerial images. These phenomena will have a significant impact on the production of DOM, and will present challenges in the interpretation, transliteration, feature extraction, and other related processes. In addressing these issues, the current methodologies exhibit shortcomings in terms of artificial subjectivity and an inability to exert comprehensive control over the processing effects, an improved method based on Mask dodging algorithm is proposed after a comprehensive evaluation of some different aerial image dodging algorithms. The aerial image is subjected to the Mask uniform light algorithm for processing. However, due to the uneven distribution of tonal contrast, a gradient stretching algorithm for the image histogram is employed to stretch the aerial image and drone image. Based on statistics, we are aiming to enhance the evaluation index value of image quality and improve contrast stretching through parameter selection and the Linear2% stretching processing algorithm. The comparative analysis reveals that the improved gradient stretching algorithm, based on image histogram, effectively ensures consistent overall brightness and texture contrast in aerial images while significantly improve the clarity of images.