Image Segmentation for Dust Detection Using Semi-supervised Machine Learning

Manzhu Yu, Julie Bessac, Ling Xu, Aryya Gangopadhyay, Yingxi Shi, Jianwu Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781728162515
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020


Conference8th IEEE International Conference on Big Data, Big Data 2020
Country/TerritoryUnited States
CityVirtual, Atlanta

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality


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