Land Utilization Change Detection of Satellite Remote Sensing Image Based on AlexNet and Support Vector Machine

The rapid development of the satellite remote sensing technology provides important technical support for land utilization change detection. To further improve the accuracy of land utilization change detection, this paper proposes a land utilization change detection method combining AlexNet and support vector machine (SVM). This method involves the use of the GF-1 satellite remote sensing images in Nanchang City, Jiangxi Province, China, from 2013 to 2017 in order to generate the land utilization change map of the area in the five years. In addition, an analysis of the land utilization change characteristics is also conducted. The results reveal that the land types in the study area arc mainly vegetation, water, bare land, and building. In the past five years, the vegetation area has changed the most, which decreased by 54.74 km(2); the water arca has increased by 22.12 km(2), the building area has increased by 19.45 km(2), and the bare land area has decreased by 5.17 km(2).