| 基于自适应可变滤镜的地类变化预测模型 |
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| 引用本文： | 柳长源,刘鹏,毕晓君.基于自适应可变滤镜的地类变化预测模型[J].吉林大学学报(地球科学版),2019,49(5):1477-1485. |
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| 作者姓名： | 柳长源 刘鹏 毕晓君 |
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| 作者单位： | 1. 哈尔滨理工大学电气与电子工程学院, 哈尔滨 150080;2. 哈尔滨工程大学信息与通信工程学院, 哈尔滨 150001 |
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| 基金项目： | 国家自然科学基金项目（51779050）；黑龙江省自然科学基金项目（F2016022） |
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| 摘 要： | 随着土地开发建设规模不断扩大，土地利用情况也在逐年发生变化，准确预测未来土地利用的发展趋势，可以为本地区的土地利用规划提供依据，提升本地区的土地利用效率。传统方法一般采用CA_Markov、ANN以及CA_ANN模型进行预测，存在训练时间长、预测精度不足和缺乏说服力等问题。本文针对上述问题，结合元胞自动机以及人工神经网络模型，建立一种自适应可变滤镜网络模型，针对特定大小区域内的土地类别数目，创建多类数据集来训练不同参数的多个神经网络，可以成功预测未来土地变化的情况，这样就避免了训练单一网络时数据对网络权值的抵消。相比于传统模型中效果最好的CA_ANN模型，本文建立的自适应可变滤镜网络模型不仅总体精度提高了1%~3%，各种地类转化精度提高了12.82%~33.33%，模型预测时间也缩减了49.47%。
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| 关 键 词： | 遥感图像 土地利用预测 人工神经网络 元胞自动机 自适应可变滤镜 |
| 收稿时间： | 2018-08-07 |
Land Use Change Prediction Model Based on Adaptive Variable Filter |
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| Liu Changyuan,Liu Peng,Bi Xiaojun.Land Use Change Prediction Model Based on Adaptive Variable Filter[J].Journal of Jilin Unviersity:Earth Science Edition,2019,49(5):1477-1485. |
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| Authors: | Liu Changyuan Liu Peng Bi Xiaojun |
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| Institution: | 1. School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China;2. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China |
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| Abstract: | With the continuous expansion of land development and construction scale, land use conditions are also changing year by year. Accurate forecasting of future land use development trends can provide a basis for regional land use planning and improve the efficiency of regional land use. Traditionally,the methods of CA_Markov, ANN, and CA_ANN models are usually used for prediction; however,there are problems such as long training time, poor prediction accuracy, and lack of persuasiveness. Aiming at the above problems, the authors established an adaptive variable filter network model in combination with the cellular automaton and neural network models, and created multiple data sets based on the number of land use categories within a certain area for training of multiple neural networks with different parameters. This model can predict the future land change situation, thus avoid the cancellation of network weights when training a single network. Compared with the best model CA_ANN out of the traditional ones, the overall accuracy of this model is improved by 1%-3%, the accuracy of land conversion is improved by 12.82%-33.33%, and the model predicting time is reduced by 49.47%. |
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| Keywords: | remote sensing image land use forecast artificial neural network cellular automata adaptive variable filter |
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