基于多层感知器神经网络的土地利用数据库更新模型及应用  

Updating model for land use database based on multi-layer perceptron network and its application

作　　者：纪亚洲[1,2] 顾和和[1] 李保杰[2] 

机构地区：[1]中国矿业大学环境与测绘学院,徐州221116 [2]江苏师范大学城市与环境学院,徐州221116

出　　处：《农业工程学报》2015年第7期227-237,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国土资源公益性行业科研专项经费资助(201111013)

摘　　要：针对土地利用数据库更新规则复杂、不同更新类型与更新规则自动匹配困难等问题,该文提出并构建了一种基于多层感知器神经网络的土地利用要素自适应更新模型。该模型将土地利用要素的每个变更类型判断及更新行为判断过程均设计成一个神经元,同类神经元组织成一个网络层,所有网络层采用全连接方式构成一个多层感知更新策略判断模型。该模型可以自动完成变更类型与更新规则的正确快速匹配,且可根据更新规则的变化,灵活改变知识库并产生新的推理机。试验表明:该模型明显减少了人工交互环节,综合更新效率较各基地软件可以提高30%左右,一次性更新正确率可以提高5个百分点以上,研究成果可为土地利用数据库的高效自动更新提供一整套新的解决方案。Land use database is the basis for the government departments at all levels to regulate land use, and the currency and quality of land use database directly determine the level and effect of land supervision. However, at present, the land use database updating technology and means are not advanced enough yet. The currency of the land use database significantly lags our economic development level. In light of the automatic matching complexity of change type and update strategy, artificial neural network is introduced into update strategy judgment field. According to the structure and main updating content, from the horizontal, land use database adaptive updating model is divided into land class polygon, linear feature and isolated feature. Then, in accordance with annual update implementation program of land use database and current updating progress, methods and habits, from the vertical, the above-mentioned updating model is divided into spatial analysis layer, input layer, change type judgment layer, spatial update strategy judgment layer and attribute update strategy judgment layer. Land class polygon is the first and the most important layer for land use database to update, so its updating model is designed on the top of the land use database adaptive updating model. To judge the change type and update strategy, 3 input conditions and 12 neurons are set up in land class polygon updating model, among which 4 neurons are responsible for judging the change type, 6 neurons are responsible for judging the spatial strategy, and 2 neurons are responsible for judging attribute strategy. Compared with land class polygon, linear feature updating model is more complicated. Therefore, linear feature updating model has 6 input conditions and 12 neurons, among which the distribution of neurons is the same as that of neurons in land class polygon updating model. Isolated feature belongs to one-dimensional element, so its updating model is relatively simple. In isolated feature updating model, 4 neurons are arranged to judge

关 键 词：土地利用 神经网络 数据库系统 更新模型 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] S126[自动化与计算机技术—控制科学与工程]