Image Segmentation for Dust Detection Using Semi-supervised Machine Learning
Image Segmentation for Dust Detection Using Semi-supervised Machine Learning
复制标题
DOI：
10.1109/bigdata50022.2020.9378198
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Manzhu Yu;J. Bessac;Ling Xu;A. Gangopadhyay;Y. Shi;Jianwu Wang
中科院分区：
文献类型：
--
作者：
Manzhu Yu;J. Bessac;Ling Xu;A. Gangopadhyay;Y. Shi;Jianwu Wang
来自地球主要干旱和半干旱地区的尘羽会对气候系统和人类健康产生重大影响。从遥感的角度来看，已有许多方法可以从非尘像元中识别尘埃。然而，这些方法使用经验规则，因此很难检测到高于或低于可检测阈值的灰尘。监督机器学习方法也被应用于从卫星图像中检测灰尘，但由于地面真实数据量不足，这些方法在应用于训练数据以外的区域时尤其有限。在这项工作中，我们提出了一个基于半监督机器学习的自动粉尘分割框架，该框架基于使用可见光红外成像辐射计套件（VIIRS）和云气溶胶激光雷达和红外探路者卫星观测（CALIPSO）的并置数据集。该方法利用无监督机器学习对VIIRS图像进行分割，并利用CALIPSO的粉尘剖面产品从粉尘标签中获取指导，确定粉尘簇作为最终产品。尘埃星团是根据沿CALIPSO轨迹的尘埃像素的光谱特征的相似性来确定的。实验结果表明，该框架在CALIPSO轨迹上的精度优于传统的物理红外方法。此外，所提出的方法在三个不同的研究区域（北大西洋、东亚和北非）中表现一致。
Dust plumes originating from the Earth’s major arid and semi-arid areas can significantly affect the climate system and human health. Many existing methods have been developed to identify dust from non-dust pixels from a remote sensing point of view. However, these methods use empirical rules and therefore have difficulty detecting dust above or below the detectable thresholds. Supervised machine learning methods have also been applied to detect dust from satellite imagery, but these methods are limited especially when applying to areas outside the training data due to the inadequate amount of ground truth data. In this work, we proposed an automatic dust segmentation framework using semi-supervised machine learning, based on a collocated dataset using Visible Infrared Imaging Radiometer Suite (VIIRS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The proposed method utilizes unsupervised machine learning for segmentation of VIIRS imagery, and leverages the guidance from the dust labels using the dust profile product of CALIPSO to determine the dust clusters as the final product. The dust clusters are determined based on the similarity of spectral signature from dust pixels along the CALIPSO tracks. Experiment results show that the accuracy of the proposed framework outperforms the traditional physical infrared method along CALIPSO tracks. In addition, the proposed method performs consistently over three different study areas, the North Atlantic Ocean, East Asia, and Northern Africa.