张量分类算法的遥感影像目标探测     

张乐飞  张良培  陶大程 《遥感学报》2010,14(3):526-539

提出了一种基于张量学习机的遥感影像目标探测方法。该方法基于张量数据模型和张量代数运算, 针对遥感影像数据多维或高维的特点, 将基于向量的监督法学习机扩展为基于张量的监督法学习机, 然后利用凸函数最优化理论和交互投影迭代法求得张量学习机的最优解。最后分别以高光谱遥感影像和高分辨率遥感影像为例, 使用张量学习机进行目标探测。实验表明, 与支持向量机等方法相比, 本文的方法在保持较高探测成功率的同时更好的抑制了虚警。  相似文献   

生成模型学习的遥感影像半监督分类   总被引：1，自引：0，他引：1  

任广波  张杰  马毅  郑荣儿 《遥感学报》2010,14(6):1097-1110

以生成模型最大似然估计为例,引入结合已标记样本和未标记样本的半监督分类方法来解决遥感影像分类中的小样本问题,应用已有的少量已标记样本初始化一个分类器,结合大量未标记样本,通过递归计算的方式对分类器进行优化,直到包含所有样本的似然函数收敛到局部极大值。通过分析遥感影像待分类类别与影像中地物类型固有特征之间的关系,设计两个在不同生成模型假设下的分类实验。结果表明,未标记样本的参与可在很大程度上提高小样本条件下的影像分类精度,但两种样本的数量应保持一个适当的比例。最后通过与在解决小样本分类问题方面有独特优势的SVM方法的分类比较,发现在小样本情况下,本文方法具有更好的应用潜力。  相似文献   

软件模块故障倾向预测方法研究   总被引：2，自引：0，他引：2  

罗云锋  普杰  贲可荣 《武汉大学学报(信息科学版)》2010,(5)

研究了在区分故障严重程度下的软件模块故障倾向预测方法,将故障分为高严重程度和低严重程度两种类型,用统计分析和机器学习方法分析静态代码度量与故障倾向之间的关系。以公开和私有两种类型的失效数据集作为实验数据,分析发现,故障的严重程度影响预测性能,预测不同严重程度的故障需要选择不同的度量和分类模型,预测低严重程度故障的性能好于预测高严重程度故障的性能。  相似文献   

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process     

《地学前缘(英文版)》2020,11(6):2207-2219

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.  相似文献   

"干中学"与产业集群核心能力的形成   总被引：2，自引：0，他引：2  

王步芳 《世界地理研究》2005,14(3):37-44

本文构建了产业集群核心能力的分析框架,从理论上说明学习效应是产业集群发展的内生动力,产业集群的学习是一个“干中学”的过程.  相似文献   

预测有效波高的深度学习模型研究     

秦易凡  罗锋  张杰  汪忆  张义丰 《海洋通报》2024,(3)

研究基于RNN、LSTM、GRU深度学习模型,针对NOAA浮标数据集中的44013、44014、44017浮标的数据,通过斯皮尔曼相关性分析提高模型预测效果。实验结果表明,在进行相关性分析后,S-RNN、S-LSTM、 S-GRU的预测效果均比原始RNN、LSTM、GRU模型预测效果好。此外,提出一种基于LSTM的LSTM-Attention 波高预测模型,并进行相关实验,量化LSTM-Attention模型的预测效果,实验结果表明LSTM-Attention模型有更好的预测效果。为评估模型的泛化能力,研究还提出了一种采用邻近浮标数据进行学习,预测浮标缺失数据的方 法。实验结果表明,该方法的预测精度可以达到97.93%。本研究为海浪预测提供了新的方法和思路,也为未来深 度学习模型在海浪预测中的应用提供了参考。  相似文献   

基于深度学习和时空特征融合的海洋渔船密度预测方法          下载免费PDF全文

丁依婷  胡志远  董帝渤 《应用海洋学学报》2024,43(2):350-359

为了从海量渔船轨迹数据中挖掘隐含的信息和知识,进而为渔业行政主管部门的决策提供科学依据,本研究以AIS渔船轨迹数据为研究对象,提出了一种基于深度学习和面向时空特征融合的海洋渔船密度预测方法：首先,利用渔船轨迹数据集对渔船行驶区域进行网格划分;其次,筛选出渔船高密度区域进行研究,避免数据稀疏性问题;再次,根据渔船轨迹数据的时空分析,构建三维时空融合矩阵;最后,通过卷积循环神经网络模型捕获渔船分布的时间和空间特征,并利用卷积神经网络的堆叠加强对空间特征的学习。实验通过东海海域渔船真实轨迹数据进行具体测试,结果表明渔船密度预测值与真实值非常接近,平均绝对误差为4×10-4,模型较好地拟合了渔船密度分布特征,有效地提高了渔船捕捞热点预测的准确性和鲁棒性。  相似文献   

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets     

Gustavo Côrte  Jesper Dramsch  Hamed Amini  Colin MacBeth 《Geophysical Prospecting》2020,68(7):2164-2185

In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.  相似文献   

Water quality monitoring at a virtual watershed monitoring station using a modified deep extreme learning machine     

Jian Jin  Lei Li  Huan Xu  Guang Lin 《水文科学杂志》2020,65(3):415-426

ABSTRACT

A new deep extreme learning machine (ELM) model is developed to predict water temperature and conductivity at a virtual monitoring station. Based on previous research, a modified ELM auto-encoder is developed to extract more robust invariance among the water quality data. A weighted ELM that takes seasonal variation as the basis of weighting is used to predict the actual value of water quality parameters at sites which only have historical data and no longer generate new data. The performance of the proposed model is validated against the monthly data from eight monitoring stations on the Zengwen River, Taiwan (2002–2017). Based on root mean square error, mean absolute error, mean absolute percentage error and correlation coefficient, the experimental results show that the new model is better than the other classical spatial interpolation methods.  相似文献   

长江中下游湖泊群不同生活型水生植物分布的时空变化(1986—2020年)     

刘兆敏  张玉超  关保华  曹振  来莱  杨启铎 《湖泊科学》2023,35(6):2022-2035

不同生活型水生植物对水环境的影响和碳固持能力不同,开展大尺度范围内不同生活型水生植物的时空分布和动态变化研究,是全面掌握湖泊水生态环境变化趋势、准确核算水生生态系统碳源/碳汇的前提。以长江中下游10 km²以上(共131个)的湖泊为研究对象,基于野外调查和先验知识,通过光谱分析,研发了不同生活型水生植物遥感高精度机器学习识别算法,解析了长江中下游湖泊群不同生活型水生植物的时空变化规律。研究表明,长江中下游湖泊群不同生活型水生植物遥感监测精度为0.81,Kappa系数为0.74;1986—2020年长江中下游湖泊群水生植物面积为2541.58~4571.42 km²,占湖泊总面积的15.99%~28.77%,沉水植物是优势类型(Max_1995年=2649.21 km²,Min_2005年=921.38 km²),其次是挺水植物(Max_2005年=1779.44 km²,Min_2020年=569.05 km²)和浮叶植物(Max_2015年=685.68 km²,Min_2000年=293.04 km²);水生植物主要分布在长江干流流域湖泊群,其次是鄱阳湖流域、洞庭湖流域、太湖流域和汉江流域;变化趋势上,1986—2020年长江中下游湖泊群水生植物面积呈现先增长(1986—1995年)、后下降(1995—2010年)、再增加(2010年后)的趋势。本研究可为长江中下游湖泊群生态环境调查及水环境管理提供重要参考。  相似文献