Shadow Detection in Remotely Sensed Images Based on Self-Adaptive Feature Selection Jiahang Liu, Tao Fang, and Deren Li IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Vol. 49, Issue.10, Jue 2011 Reporter ： De-Cing Chen

Outline Introduction Proposed Shadow Detection Model Acquirement of Shadow Samples Intensity Transformation and Shadow Segmentation SAFS (self-adaptive feature selection) and Shadow Segmentation Combining Shadow Detection Result Refinement Experiments Conclusion Personal remake 2

Introduction(1/6) Shadows in remotely sensed images create difficulties in many applications; thus, they should be effectively detected prior to further processing. One property that provides a clue to the presence of shadows is lower luminance, which is the primary property of shadows. 3

Introduction(2/6) However, even though shadows have a lower luminance, the lowest intensity objects in the image are not necessarily shadows. Since panchromatic images only provide intensity information, some nonshadow regions may be identified as shadows. 4

Introduction(3/6) Huang et al. noted that shadow pixels have larger hues, lower values in the blue channel, and smaller differences between the green and blue channels. Based on these three features, three thresholds that were experimentally derived from a histogram were used to segment the shadow regions from nonshadow regions. 5

Introduction(4/6) Tsai proposed a new efficient shadow detection method for color aerial images that uses a ratio map of the hue to the intensity in conjunction with Otsu’s thresholding method. Using Tsai’s work as a foundation, Chung et al. proposed an improved approach, and experiments indicate that this improved method provides more reliable and accurate shadow detection results than the algorithms by Tsai or Huang et al. 6

Introduction(5/6) In this paper, we propose a novel shadow detection method based on self-adaptive feature selection (SAFS) that applies to both panchromatic and color remotely sensed images. We performed several experiments and comparisons to demonstrate that the proposed algorithm has improved performance and accuracy over the algorithms by Tsai and Chung et al. in the general case and also has better applicability and practicability. 7

Introduction(6/6) 8

Acquirement of Shadow Samples(1/2) Although automatic sampling of remotely sensed images is too difficult for state-of-the-art object recognition algorithms, leading us to use manual sampling in our model, but it results in more practicability and reliability. The sampling process contains two steps. In the first step, lines are drawn in the representative Shadow areas in the input images [hereafter, these lines are called sampling center lines (SCLs)]. 9

Acquirement of Shadow Samples(2/2) The second step uses a 5 × 5 window to sample the object image (map) and collect all the pixel values inside the sampling window. The first step is performed by the user and is the only manual operation in the proposed algorithm. The second step is performed automatically by the computer. Generally, ten or so SCLs are sufficient. 10

Intensity Transformation and Shadow Segmentation(1/6) The intensity of a pixel can be derived from the red, green, and blue components of a pixel in the RGB color space. Let C = [R,G,B] denote the color vector of a pixel in the input RGB image. I is the defined intensity. Then, the transformation is defined by where W = [1/3, 1/3, 1/3]. 11

Intensity Transformation and Shadow Segmentation(2/6) For a panchromatic image, the value of a pixel is its intensity. We define a new transformation to improve the separability between its shadow and nonshadow regions. The parameter k represents the intensity level of I 0 (x, y) so that the new intensity level k e in the enhanced intensity map I e (x, y) is defined by ρ k is a transformation coefficient θ is the mean value of the shadow intensity λ is a sensitivity factor 12

Intensity Transformation and Shadow Segmentation(3/6) λ = 20 is empirical value in our proposed 13

Intensity Transformation and Shadow Segmentation(4/6) 14

Intensity Transformation and Shadow Segmentation(5/6) We used Otsu’s method to automatically select the threshold T to separate shadow regions from nonshadow regions in the intensity map. p i is the probability of the gray level i. 15

Intensity Transformation and Shadow Segmentation(6/6) Then, a shadow map S I can be obtained through The distribution range of the shadow intensity can be denoted as [0, T]. S I (x, y) > 0 indicates that the pixel at position (x, y) is a candidate shadow pixel. 16

SAFS and Shadow Segmentation(1/2) An additional process is designed for color images only and is omitted for panchromatic input images. We define six candidate features through the chromatic Boolean relationship “≥?” between any two RGB components to denote the actual chromatic properties of an input image. The six Boolean relationships are R ≥?G, R ≥?B, G ≥?R, G ≥?B, B ≥?R, and B ≥?G; these are represented as C1, C2, C3, C4, C5, and C6, respectively. 17

SAFS and Shadow Segmentation(2/2) First, we obtain the R, G, and B components of every sampled shadow pixel. If M of six Boolean relationships exceeds the η threshold, then those M Boolean relationships are selected as modeling features ; otherwise, it will be omitted. Suppose a pixel’s chromatic properties meet N of M Boolean relationships. S C denotes the shadow segmentation result achieved through chromatic information. 18

Combining Shadow Detection(1/2) If the input image is panchromatic, then the transformation defined by (6) will be omitted. 19

Combining Shadow Detection(2/2) The detected shadow pixels are determined by T is hard limit threshold. is the mean value from the S 0 map. d is the standard deviation from the S 0 map. If the input image is panchromatic, the S I map is used instead of the S 0 map in (7). 20

Result Refinement There may be too small shadow regions which only contain a few pixels or too small holes in shadow regions which have little actual significance within an application. If the size of a shadow region is smaller than a certain T S, then this region will be rejected. Similarly, if the size of a hole in a shadow region is smaller than a certain T H, then this hole also will be automatically removed by the model. 21

Experiments(1/8) In this paper, we use error to evaluate the accuracy of the three investigated algorithms. The error contains two parts: omitted error and committed error. The omitted error (E O ) is caused by true shadow pixels that are identified as nonshadow pixels. The committed error (E C ) is caused by nonshadow pixels that are identified as shadow pixels. 22

Experiments(2/8) The omitted, committed, and total errors (E T ) are expressed as False negative (FN) denotes the number of true shadow pixels which are identified as nonshadow pixels. False positive (FP) denotes the number of nonshadow pixels which are identified as shadow pixels. Total shadow pixel (TS) denotes the total number of true shadow pixels in the ideal shadow map. 23

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Conclusion Experimental results indicate that our proposed method has the best performance in general when compared with the methods by Tsai and Chung et al. and is practicable in applications. Although the proposed method requires the user to manually determine the SCLs of shadow samples, which reduces the automation of the processing, the parameters derived from these samples conform well to the actual status of the input image and effectively improve the accuracy and adaptability of the algorithm. 29

30 Thanks for your attention!! Q&A