Laser & Optoelectronics Progress, Volume. 57, Issue 20, 201006(2020)

Shadow Compensation of High-Resolution Remote Sensing Images Based on Improved Logarithmic Transformation and Local Enhancement

Yuanyuan Feng1, Xianjun Gao1,2、*, Yuanwei Yang1,2, and Fan Deng1
Author Affiliations
  • 1School of Geoscience, Yangtze University, Wuhan, Hubei 430100, China
  • 2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China
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    In order to solve the problem that the shadow in remote sensing images leads to the loss of ground object information and the degradation of image quality, we propose a shadow compensation method by combing logarithmic transformation with local enhancement in the high resolution remote sensing images. First, based on the shadow detection results, we design an improved logarithmic transformation image enhancement method and construct the logarithmic transformation compensation model to effectively increase the brightness of shadow areas. Then, we use the local compensation model and weighting treatment to improve the contrast of shadow areas. Finally, we obtain the automatic parameters of the compensation model using the information of similar points on both sides of the shadow boundary and realize the automatic compensation. The experimental results indicate that the proposed method can be used for shadow compensation, to improve the brightness and contrast of shadow areas, and to recover the true ground object information of shadow areas correctly.

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    Yuanyuan Feng, Xianjun Gao, Yuanwei Yang, Fan Deng. Shadow Compensation of High-Resolution Remote Sensing Images Based on Improved Logarithmic Transformation and Local Enhancement[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201006

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    Paper Information

    Category: Image Processing

    Received: Jan. 19, 2020

    Accepted: Feb. 24, 2020

    Published Online: Oct. 13, 2020

    The Author Email: Gao Xianjun (junxgao@whu.edu.cn)

    DOI:10.3788/LOP57.201006

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