Shadow detection of the satellite images of earth using ratio image pixels

Shadows, in aerial and satellite high-resolution images of earth, are a common phenomenon. Shadow causes false-color image, loss of information in the image, and false image segmentation. This leads to incorrect outputs of many image processing applications. In this paper, we address the problem of shadow detection in aerial high-resolution images of earth. The paper presents a proposed method that can be valuable in comparing it with other existing methods. The proposed work exploits the impact of ratio image pixel values on the process of shadow detection. The ratio image is the mathematical division of hue over the intensity component in the invariant color model. We propose a method, design, and develop an algorithm. In the designed algorithm, the input RGB aerial image of the earth is transformed into the invariant color model hue, saturation, and value (HSV). It acquires the average intensity value of pixels of the input RGB image components. Then, the ratio image of Hue (H) over Value (V) is calculated. Afterward, a power function is applied to this ratio to modify it by increasing the difference between pixel values very effectively. Finally, a threshold is applied to the modified ratio image to classify pixels into shadow and nonshadow. The proposed power function helps the threshold to better classify pixels into shadow and nonshadow. It has been implemented and experimented extensively. A comparative study has also been made with existing methods in the literature. In comparing the proposed algorithm and some existing algorithms, the experimental results show that the proposed has the ability to detect shadows with satisfying accuracy.