Abstract
Current automatic shadow compensation methods often suffer because their contrast improvement processes are not self-adaptive and, consequently, the results they produce do not adequately represent the real objects. The study presented in this paper designed a new automatic shadow compensation framework based on improvements to the Wallis principle, which included an intensity coefficient and a stretching coefficient to enhance contrast and brightness more efficiently. An automatic parameter calculation strategy also is a part of this framework, which is based on searching for and matching similar feature points around shadow boundaries. Finally, a final compensation combination strategy combines the regional compensation with the local window compensation of the pixels in each shadow to improve the shaded information in a balanced way. All these strategies in our method work together to provide a better measurement for customizing suitable compensation depending on the condition of each region and pixel. The intensity component I also is automatically strengthened through the customized compensation model. Color correction is executed in a way that avoids the color bias caused by over-compensated component values, thereby better reflecting shaded information. Images with clouds shadows and ground objects shadows were utilized to test our method and six other state-of-the-art methods. The comparison results indicate that our method compensated for shaded information more effectively, accurately, and evenly than the other methods for customizing suitable models for each shadow and pixel with reasonable time-cost. Its brightness, contrast, and object color in shaded areas were approximately equalized with non-shaded regions to present a shadow-free image.
Highlights
Shadows are a common phenomenon in nature when light is occluded by objects such as buildings, clouds, and trees
We compared the results of our method to other reference methods on several high-resolution remote sensing images with ground object shadows and cloud shadows
Image 6 was an International Society for Photogrammetry and Remote Sensing (ISPRS) public aerial image captured over Vaihingen in Germany; and the areas of the ground object shadows in this image were darker than the cloud shadow areas as they received less sunlight while the objects in the cloud shadow areas received some scattered sunlight
Summary
Shadows are a common phenomenon in nature when light is occluded by objects such as buildings, clouds, and trees. In the remote sensing image acquisition process, shadows exist in images because of low sun elevation, off-nadir viewing angles, high-rise buildings, and uneven terrain. Shadows can be categorized as cast shadows and self-shadows. A cast shadow is the part of an object that is cast on the ground, while a self-shadow is the part that is not illuminated [1]. Cast shadows were the focus of the study presented in this paper. Objects in cast shadow areas receive some scattered sunlight from the surrounding environment, their brightness is much darker than that of the surrounding non-shaded areas. Information about other objects inside cast shadows is not adequately presented, Appl.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
