A correlation analysis study on satellite image nightlight features and development of Africa regional economy

The Affiliated High School of South China Normal University, Guangdong Province, China

^* lixz.alex2019@gdhfi.com

Abstract

Nightlight intensity has become an important factor of measuring the wealthiness of a country or an area, it mostly relies on extracting the feature from the satellite images and it becomes a dominate factor that determines the economic development. This study used CNN based deep learning model to extract the light intensity feature from the satellite images and associate it with additional survey information. CNN has been wildly used for image feature extraction. Then, the study combined the survey data and light intensity feature together and conducted comprehensive experiments on different regression models that using different regularizations and optimization approaches. The paper studied the influence of regularization and optimization approaches to the model. Through the feature selection, hyper-parameter tuning, and model evaluation, the study can select the best model. This paper compares different linear regression models. They utilize different regularization and optimization. The experiment results indicate that Lasso regression model is the best model.