A Deep Learning Model for Detecting Dust in Earth's Atmosphere from Satellite Remote Sensing Data
A Deep Learning Model for Detecting Dust in Earth's Atmosphere from Satellite Remote Sensing Data
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DOI：
10.1109/smartcomp50058.2020.00045
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Ping Hou;Pei Guo;Peng Wu;Jianwu Wang;A. Gangopadhyay;Zhibo Zhang
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文献类型：
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作者：
Ping Hou;Pei Guo;Peng Wu;Jianwu Wang;A. Gangopadhyay;Zhibo Zhang
在本文中，我们开发了一种深度学习模型，利用卫星遥感图像数据区分灰尘与云和表面。随着全球气候变化，沙尘暴的发生日益增多，特别是在干旱、半干旱地区。灰尘源自土壤，是一种气溶胶，对环境和人类健康造成重大影响。本文使用的尘埃和云数据标签来自CALIPSO（云气溶胶激光雷达和红外探路者卫星观测）卫星。来自 VIIRS（可见红外成像辐射计套件）卫星传感器的辐射通道和几何参数作为我们模型的特征。 2012 年 3 月，我们使用 10,000 个样本训练和测试了我们的深度学习模型。开发的模型有 5 个隐藏层，每层有 512 个神经元。测试集上的分类准确率为71.1%。此外，我们还执行了洗牌过程来识别特征的重要性，其计算方法是排列特征值后预测误差的增加。我们还开发了一种基于遗传算法的方法来寻找灰尘检测的最佳特征子集。结果表明，遗传算法可以选择与具有所有特征的模型性能相当的特征子集。洗牌过程和遗传算法都将几何信息识别为检测矿物粉尘的重要特征。所选子集将提高灰尘检测的计算效率并改进基于物理的方法。
In this paper we develop a deep learning model to distinguish dust from cloud and surface using satellite remote sensing image data. The occurrence of dust storms is increasing along with global climate change, especially in the arid and semi-arid regions. Originated from the soil, dust acts as a type of aerosol that causes significant impacts on the environment and human health. The dust and cloud data labels used in this paper are from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. The radiometric channels and geometric parameters from VIIRS (Visible Infrared Imaging Radiometer Suite) satellite sensor serve as features for our model. We trained and tested our deep learning model using 10,000 samples in March 2012. The developed model has five hidden layers and 512 neurons in each layer. The classification accuracy on the test set is 71.1%. In addition, we performed a shuffling procedure to identify the importance of features, which is calculated as the increase in the prediction error after we permute the feature's values. We also developed a method based on genetic algorithm to find the best subset of features for dust detection. The results show that the genetic algorithm can select a subset of features that have comparable performance as that of a model with all features. The shuffling procedure and the genetic algorithm both identify geometric information as important features for detecting mineral dust. The chosen subset will improve computational efficiency for dust detection and improve physical based methods.