Shadow Detection and Compensation in Aerial Images using MATLAB

Abstract

Recently, Kuo-Liang Chung presented an efficient algorithm uses the ratio of hue over intensity as the parameter to determine the coarse shadow map and then the local thresholding method (STS) in which it needs the different threshold levels which is comparatively more time consuming (and if the threshold is not proper then the resultant image is not the desired image, because threshold determination is typical task in itself) for fine shadow determination in color aerial images. In our proposed method we again modify the ratio with some empirical relation that give better result taking less time. Under four testing images, experimental results show that, for all four images that are low and medium intensity images have better shadow detection but the shadow compensation algorithm is gives the good result in all the testing images. We also have the comparison between three algorithms Tsai's, STS and our proposed algorithms with result of shadow detected images [15].

Modified Hue over Intensity Ratio Based Method for Shadow Detection and Removal in Arial Images

“…Actually it is the combination of previous two types of accuracy produces and overall accuracy. It is used to determine the correctness percentage [15] of the algorithm. It denotes by the total number of correctly true shadow and non-shadow pixels are equal to the number of total pixel in the image.…”

“…The lots of work is done on the image enhancement by various researchers, to enhancing edges as fundamental component of the images [11,12], unstill shadows [13], objects clearness [14][15][16][17][18][19][20] etc. In this paper, an efficient shadow detection method is discussed to improve its performance which are confirm the results a better performance, compared with the other detection algorithms.…”

Method for Shadow Removal of Moving Object in YUV Color Space

“…Property-based methods are using the specific shadow properties such as edges, brightness, texture, different color spaces to separate shadow from the object and can obtain better result. Angie WKS et al [4] used the boundary information to remove shadows in traffic video. Because shadow didn't change the background texture, Hoang M A et al [5] presented a texture-based method for object extraction.…”

Image Dehazing (Defogging) by using Depth Estimation and Fusion with Guided Filter

“…Experimental results show the effectiveness of the proposed model for defogging [7]. The theoretical background can be discussed in terms of the basic image features and the image properties [8][9][10][11][12][13][14][15][16][17][18]. [19] Image has important applications in many fields such as marine surveillance, environment monitoring and so on.…”