Abstract: In order to help undergraduates understand the technology of satellite remote sensing and master machine learning algorithms, combined with college student innovation training program in Jilin University, a project named “Research on Change Detection of Buildings around Campus Based on Remote Sensing Images" is designed. GF-2 satellite images and JL1-3B night time glimmer images are used as experimental data, and the area for experiment is around a primary school in China. Various machine learning algorithms are used to extract the message of buildings in the area for experiment in different periods, and the precision of the results is analyzed. The results of building extraction are compared with ground truth data. Finally, the changes of buildings in different periods are gained. The JL1-3B night time glimmer images are used to analyze the buildings around the school and the activities of residents. The experimental results show that buildings in the remote sensing images can be effectively discerned by random forest algorithm and VGG(Visual Geometry Group) neural network algorithm. The number of buildings in different periods and the results of change detection of lamp light show the influence of campus on the development of surrounding area and provide reference information for city planning.

Key words: satellite remote sensing,  , change detection,  , glimmer data,  , building extraction