摘要
Abstract
Land change surveys involve a large amount of geographic spatial data,including terrain,landforms,land types,boundaries,etc.These data are characterized by complexity,diversity,and dynamic variability,which increases the difficulty of data collection and processing for land change surveys.By combining convolutional neural networks(CNN)and probabilistic neural networks(PNN),efficient data processing can be achieved,which is suitable for large-scale identification of land resource utilization types and area statistical tasks.Therefore,this paper studied the data collection and processing technology for land change surveys.Based on the data preprocessing,land resource utilization types were identified by combining CNN and PNN,and the areas of different land types were statistically analyzed.The land change between different time periods was compared to complete the land change survey data collection and processing.Test results show that from 2018 to 2023,the areas of grassland and paddy fields in the study area have decreased,while the areas of forestland and dryland have increased.The water body area has remained almost unchanged,and the urban construction land area has seen the most significant increase,which aligns with actual conditions,proving the effectiveness of this technology.关键词
国土变更调查数据/采集/无人机遥感技术/处理技术/识别Key words
land change survey data/data collection/unmanned aerial vehicle(UAV)remote sensing technology/processing technology/identification分类
信息技术与安全科学
