摘要
国土变更调查涉及大量的地理空间数据,包括地形、地貌、地类、界线等,这些数据具有复杂性、多样性、动态变化性,这就导致国土变更调查数据采集与处理难度增加。而结合卷积神经网络和概率神经网络可以实现高效的数据处理,适用于大规模国土资源利用类型的识别和面积统计任务,为此,本文研究了国土变更调查数据采集与处理技术。利用搭载遥感传感器的无人机采集国土变更调查数据,对采集到的国土变更调查数据实施校正和拼接处理。在数据预处理的基础上,结合卷积神经网络和概率神经网络识别国土资源利用类型,统计不同土地类型的面积并对比不同时间前后国土变更情况,完成国土变更调查数据采集与处理。测试结果表明,2018—2023年研究区草地、水田面积有所减少,林地和旱地的面积有所增加,水域几乎没有变化,城市建设用地的面积增加最多且变更情况与实际情况相符,证明了该技术的有效性。
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.
作者
王兰玲
WANG Lanling(Guizhou Nonferrous Metals and Nuclear Industry Geological Exploration Bureau Third Brigade,Zunyi,Guizhou 563000,China)
出处
《北京测绘》
2025年第5期681-688,共8页
Beijing Surveying and Mapping
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
land change survey data
data collection
unmanned aerial vehicle(UAV)remote sensing technology
processing technology
identification
