An Automatic Shadow Compensation Method via a New Model Combined Wallis Filter with LCC Model in High Resolution Remote Sensing Images

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.

Balanced Cloud Shadow Compensation Method in High-Resolution Image Combined with Multi-Level Information

“…The results are shown in Figures 6b-8b. Then, other compensation methods such as linear correlation correction (LCC) [28], histogram matching (HM) [24], gamma transformation (GT) [29], corresponding shadow restoration (CSR) [44], and the Wallis and LCC combined method (WLC) [26] are selected as reference methods to compensate for the shadow area. The results are shown in Figures 6d-h-8d-h.…”

“…The difference between the mean brightness, B, and the mean gradient, T, of the pixels in the shadow area and that between the mean brightness, B NSD , and the mean gradient, T NSD , in the non-shadow area are the main indexes with which to measure the shadow compensation effect [26]. According to the brightness characteristics after shadow compensation, the normalized difference ratio is calculated in accordance with Equations ( 9) and (10).…”

“…Therefore, it is more suitable for the shadow information recovery of high-resolution images. Image enhancement methods mainly use linear stretching, histogram matching [24], logarithmic transformation [25], wallis filtering [26], and other image enhancement principles to improve the brightness of a shadow area. The effect of this method is related to the ability of the image enhancement algorithm.…”

Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing Images

“…Color transfer aims to make the standard deviation and average value of source images equal to those of reference images. General color transfer methods include Wallis transform [7], [8] and histogram matching [9], [10], [11]. These methods can effectively deal with images of similar content; however, when the image content is different, the resulting image exhibits a color deviation.…”