多层感知机结合辐射传输模型的复杂陆地表面云检测

邓梦娇; 徐新; 马盈盈; 龚威; 金适宽; 胡瑞敏

doi:10.12263/DZXB.20210636

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多层感知机结合辐射传输模型的复杂陆地表面云检测

机器学习交叉融合创新 | 更新时间：2025-12-08

- 多层感知机结合辐射传输模型的复杂陆地表面云检测
- Multi-layer Perceptron Combined with Radiative Transfer Model for Complex Land Surface Cloud Detection
- 电子学报 2022年50卷第4期页码：932-942
- 作者机构：
  
  1.武汉科技大学计算机科学与技术学院，湖北武汉 430065
  2.武汉科技大学智能信息处理与实时工业系统重点实验室，湖北武汉 430065
  3.武汉大学测绘遥感信息工程国家重点实验室，湖北武汉 430072
  4.武汉大学计算机学院，湖北武汉 430072
- 作者简介：
  
  [ "邓梦娇女，1998年生，湖北咸宁人.现为武汉科技大学硕士研究生.主要研究方向为大气遥感云检测. E-mail: dengmjiao@qq.com" ]
  [ "徐新（通讯作者）男，1982年生，湖北武汉人.博士，武汉科技大学计算机科学与技术学院教授、博士生导师.主要研究方向为计算机视觉、人工智能、大气遥感. E-mail: xuxin@wust.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(U1803262)
- DOI：10.12263/DZXB.20210636
  中图分类号： TP751;
- 收稿：2021-07-17，
  
  修回：2022-03-07，
  
  纸质出版：2022-04-25
- 稿件说明：
移动端阅览
邓梦娇,徐新,马盈盈等.多层感知机结合辐射传输模型的复杂陆地表面云检测[J].电子学报,2022,50(04):932-942.

DENG Meng-jiao,XU Xin,MA Ying-ying,et al.Multi-layer Perceptron Combined with Radiative Transfer Model for Complex Land Surface Cloud Detection[J].ACTA ELECTRONICA SINICA,2022,50(04):932-942.
邓梦娇,徐新,马盈盈等.多层感知机结合辐射传输模型的复杂陆地表面云检测[J].电子学报,2022,50(04):932-942. DOI： 10.12263/DZXB.20210636.

DENG Meng-jiao,XU Xin,MA Ying-ying,et al.Multi-layer Perceptron Combined with Radiative Transfer Model for Complex Land Surface Cloud Detection[J].ACTA ELECTRONICA SINICA,2022,50(04):932-942. DOI： 10.12263/DZXB.20210636.

摘要

云检测是卫星遥感数据预处理中至关重要的工作.本文将多层感知机和辐射传输模型相结合，利用可见光和近红外波段反射率信息从卫星影像中识别出云像元.该方法利用SBDART辐射传输模型，模拟获得了各种复杂陆地地表的反射率值数据集，为多层感知机提供训练样本.随后，用训练好的多层感知机模型区分FY-3D卫星MERSI II影像中的云像元和非云像元，利用CALIPSO垂直特性掩膜产品（Vertical Feature Mask，VFM）逐像元进行验证，并与MODIS云掩膜产品（MYD35）进行横向对比.结果表明，以VFM数据集为标准的情况下，多层感知机识别云的总正确率为76.25%，其中在夏季和低纬度地区效果最好，如赤道附近地表识别的准确率可达到91.74%，而在城市、农田和裸地等复杂地表类型条件下的云检测识别正确率分别为83.37%、84.52%和73.11%，分别高于MYD35产品的83.25%、83.31%和72.66%.为了进一步验证多层感知机结合辐射传输模型云检测方法的有效性，将辐射传输模型模拟得到的训练样本分别用于k-最近邻、朴素贝叶斯以及随机森林算法，并与本文多层感知机算法进行对比.结果表明，将多层感知机和辐射传输模型相结合具有更高的正确率.

Abstract

Cloud detection is a key step in the preprocessing of satellite remote sensing data. This paper proposes a cloud detection method by combining a multilayer perceptron with a radiative transfer model. The method is to identify cloud from moderate resolution satellite image using visible and near-infrared band reflectance information. In this method

firstly

the santa barbara DISORT atmospheric radiative transfer model(SBDART) is used to simulate and obtain datasets of reflectance values for a variety of complex terrestrial surfaces

which provides training samples for the multilayer perceptron. Secondly

the trained network model is used to distinguish cloud pixels from total pixels of the advanced medium Resolution Spectral Imager(MERSI II) image in the FengYun3D satellite MERSI II image

and then verified using vertical feature mask(VFM) product of the cloud-aerosol LIDAR infrared pathfinder satellite observations satellite(CALIPSO) and compared horizontally with the cloud mask product(MYD35) of the moderate resolution imaging spectroradiometer(MODIS). The results show that the accuracy of cloud detection for the multilayer perceptron is 76.25%

and especially this method works best in summer and low latitudes

achieves an accuracy of 91.74% for surface identification near the equator. In this paper

the method is more effective in detecting clouds under complex surface type conditions such as urban

farmland and bare soil

with accuracies of 83.37%

84.52% and 73.11% respectively

which are higher than the 83.25%

83.31% and 72.66% of the MYD35 product respectively. To further validate the effectiveness of the multilayer perceptron combined with the radiative transfer model

the training samples obtained from the radiative transfer model simulations are used in the k-nearest neighbors

Naive Bayesian

and Random Forest algorithms

respectively

and compared with the multilayer perceptron algorithm in this paper. The results show that the combination of the multilayer perceptron and the radiative transfer model has a higher accuracy.

关键词

Keywords

references

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