A Deep Learning Model for Detecting Dust in Earth's Atmosphere from Satellite Remote Sensing Data

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.