How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classif...How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.展开更多
如何及时、准确地获取大范围内作物冻害空间分布数据,是目前农业领域迫切需要解决的问题。本文根据冻害冬小麦的生长变化特点,提出了基于特征增强的冬小麦冻害精细空间分布遥感提取方法(Winter Wheat Frost Damage Fine Spatial Distrib...如何及时、准确地获取大范围内作物冻害空间分布数据,是目前农业领域迫切需要解决的问题。本文根据冻害冬小麦的生长变化特点,提出了基于特征增强的冬小麦冻害精细空间分布遥感提取方法(Winter Wheat Frost Damage Fine Spatial Distribution Extraction Method,WWFDFSDEM),用于从高分辨率遥感影像中提取高质量的冻害空间分布数据。选择冻害发生前后两期高分辨率遥感影像作为数据源,根据正常冬小麦和冻害冬小麦区域的影像特点,确定以红、近红、绿三个通道以及NDVI作为基础特征,充分利用像素级特征的空间相关性来增强特征的细节信息,以交叉熵为基础,加入特征类内差异因子和类间差异因子建立损失函数,用于增强特征的区分能力。选择山东省淄博市高青县为研究区,高分2号遥感影像为数据源,决策树、经典SegNet、RefineNet、ErfNet、UNet作为对比模型开展对比实验,WWFDFSDEM提取结果的精度(94.5%),查准率(90.8%),查全率(91.3%)均优于对比方法,证明了方法在提取冻害精细空间分布方面的有效性。方法能够满足农业生产管理、农业保险等领域提取作物冻害精细空间分布数据的需求。展开更多
基金supported by the National Natural Science Foundation of China(U1435220)
文摘How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.
文摘如何及时、准确地获取大范围内作物冻害空间分布数据,是目前农业领域迫切需要解决的问题。本文根据冻害冬小麦的生长变化特点,提出了基于特征增强的冬小麦冻害精细空间分布遥感提取方法(Winter Wheat Frost Damage Fine Spatial Distribution Extraction Method,WWFDFSDEM),用于从高分辨率遥感影像中提取高质量的冻害空间分布数据。选择冻害发生前后两期高分辨率遥感影像作为数据源,根据正常冬小麦和冻害冬小麦区域的影像特点,确定以红、近红、绿三个通道以及NDVI作为基础特征,充分利用像素级特征的空间相关性来增强特征的细节信息,以交叉熵为基础,加入特征类内差异因子和类间差异因子建立损失函数,用于增强特征的区分能力。选择山东省淄博市高青县为研究区,高分2号遥感影像为数据源,决策树、经典SegNet、RefineNet、ErfNet、UNet作为对比模型开展对比实验,WWFDFSDEM提取结果的精度(94.5%),查准率(90.8%),查全率(91.3%)均优于对比方法,证明了方法在提取冻害精细空间分布方面的有效性。方法能够满足农业生产管理、农业保险等领域提取作物冻害精细空间分布数据的需求。
