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  • Received: Jan. 27, 2019

    Accepted: Apr. 9, 2019

    Posted: Aug. 1, 2019

    Published Online: Aug. 5, 2019

    The Author Email: Yang Changjun (yangcj@cma.gov.cn)

    DOI: 10.3788/LOP56.162804

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    Jingfeng Hu, Xiuzai Zhang, Changjun Yang. Cloud Detection of RGB Color Remote Sensing Images Based on Improved M-Net[J]. Laser & Optoelectronics Progress, 2019, 56(16): 162804

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  • 遥感与传感器
Laser & Optoelectronics Progress, Vol. 56, Issue 16, 162804 (2019)

Cloud Detection of RGB Color Remote Sensing Images Based on Improved M-Net

Hu Jingfeng2, Zhang Xiuzai1,2, and Yang Changjun3,*

Author Affiliations

  • 1 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China
  • 2 School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China
  • 3 National Satellite Meteorological Center, Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing 100081, China

Abstract

Cloud detection is prone to error and considerable loss of details because clouds do not have obvious color distribution and texture pattern in RGB color images. Therefore, this study proposes an improved M-Net model called the RM-Net model to achieve end-to-end pixel-level semantic segmentation. An original dataset is enhanced and a corresponding pixel-level label is marked. Multi-scale image features are extracted without losing data via atrous spatial pyramid pooling, and residual units are combined to make the network resistant to degradation. Global context informations of the images are extracted using the encoder module and the left path. The spatial resolutions of the images are restored using the decoder module and the right path. Each pixel's category probability is determined based on fused features, and pixel-level cloud and non-cloud segmentation are performed using the input classifier. When training and testing Landsat8 and GaoFen-1 WFV RGB color images, experimental results show that the proposed method can well detect cloud edge details under various conditions and achieve high-precision cloud shadow detection, thus demonstrating that the proposed method has high generalization and robustness.

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