Research On Shadow Detection Algorithm For Remote Sensing Images

There are more or less degradations owing to the limitations and influence of imaging techniques and conditions during the formation process of color remote sensing images. As one kind of typical degradations, image shadow interferes image processing in computer vision field, and affects the accurate interpretation of image information, which makes it much tougher to subsequent processing of remote sensing images, such as target classification, image matching, etc. Therefore, it is necessary to preprocess the image shadow. Shadow detection, as the first step, has been attended widely and researched extensively. However, some defects still exist in the extant shadow detection algorithms, such as low detection precision and limited application scenario. These algorithms may be optimized and improved in future research.This paper proposes three improvement strategies to optimize the shadow detection algorithm based on C₃ channel. Firstly, the transform method of C₃ channel is optimized by changing the intensity of pixels with divided regions, so as to increase the B components of pixels linearly in the high threshold region, which expands the contrast of shadow regions and non-shadow regions, especially the degree of difference between shadow and vegetation regions. Secondly, a new exponent threshold shrinkage function is proposed to improve the image denoising method based on Independent Component Analysis by combining the merits of soft and hard threshold shrinkage functions. This improved method makes sure the shrinkage function is continuous and derivable, avoids excessive decrease of sparse coding coefficient and miscalculation of high frequency image information, and improves the precision of image denoising. Thirdly, pseudo color enhancement method is improved by computing the segmentation thresholds based on the potential function of histogram. The improved method widens the contrast of shadow and non-shadow regions further, and improves degree of separation between the two kinds of regions in transitory stage. So that it reduces the possibility of miscalculation in whole region and provides a better image as input of subsequent processing operations. In order to ensure the integrity and high precision requirements of algorithms, the edge of shadow is extracted by Sobel edge detection method which has a better performance. Then the final shadow region of remote sensing images is extracted through image addition, binaryzation and morphological process with the obtained shadow edge and processed C₃ image.Simulation experiments are carried out on many remote sensing images in this paper. According to the existing evaluation system and quantification of detection results, the validity of proposed method is verified by comparing some different algorithms. It shows that the proposed algorithm in this paper improves the precision of image shadow detection effectively, i.e. increases the correct detection ratio of shadow regions, and reduces the error detection ratio of non-shadow regions. Moreover, the proposed method has good robustness.Because of the practicability, Aerial remote sensing images play an important role in the fields of urban monitoring, environment protection and so on. It is always emphasis and difficulty to detect the shadow regions in images effectively and comprehensively. This paper does not process the special objects, such as the high reflective constructions and water regions. Therefore, how to analyze the related characteristics of shadow accordingly and detect shadow regions in images efficiently to improve the precision of shadow detection is the next step research direction.