基于深度信念网络的喀斯特地区土壤石漠化评价

Guanyao Lu, Dan Xu, Y. Meng
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引用次数: 0

摘要

摘要利用深度学习和对称网络结构可以成功提取遥感照片的动态特征,并指导遥感照片进行准确分类。DBN模型由于使用了无监督学习,可以更有效地从照片中提取特征。它可以简化为多对称受限玻尔特曼机(RBM)的训练问题。针对喀斯特土壤石漠化风险评估影响因素复杂、包含多个地理因素的特点,建立了基于深度信念网络(DBN)的土壤石漠化风险评估模型。该模型建立在传统RBM框架的基础上,并将相关元素的影响层作为检索地理信息系统(GIS)分数数据的辅助需求。然后,通过学习多种要素的特征来预测土壤石漠化程度。实验结果表明,本文提出的模型具有更好的预测性能和更快的收敛速度,其对不同程度RD的分类结果与实际风险评估结果更加一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Soil Rocky Desertification in Karst Region Based on Deep Belief Network
Abstract Dynamic features from remote sensing photos may be successfully extracted using deep learning and symmetric network structure, which can then be used to direct them to carry out accurate classification. The DBN model can more effectively extract features from photos since it uses unsupervised learning. It can be reduced to the many symmetric Restricted Boltmann Machines (RBM) training problem. In this paper, a soil rocky desertification (RD) assessment model based on a deep belief network (DBN) is created in light of the complicated influencing aspects of Karst RD risk assessment encompassing several geographical elements. The model builds upon the conventional RBM framework and incorporates the influence layer of related elements as an auxiliary requirement for retrieving Geographic Information System (GIS) score data. Then, in order to forecast the level of soil rocky desertification, it learns the features of many elements. The experimental results show that the proposed model proposed in this paper has better prediction performance and faster convergence speed, and its classification results for different degrees of RD are more consistent with the actual risk assessment results.
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