遥感图像分类与后处理综合技术研究—基于约束满足神经网络方法   总被引：3，自引：0，他引：3  

李强  王正志 《遥感学报》1999,3(3):193-198

遥感图像计算机分类的精度问题是阻碍计算机遥感信息处理系统实用化的一个关键问题。将分类后处理中的分类结果平滑过程模型化为约束优化问题,采用神经网络方法把分类结果平滑过程与遥感图像分类过程结合起来,提出了基于约束满足神经网络的遥感信息分类与后处理综合技术。实验表明该方法可明显提高森林类型划分、土地利用调查等遥感应用专题的分类精度。  相似文献   

基于LBS和深度学习的旅游景区客流量的高时频预测     

谢谦  陆明  谢春山 《地球信息科学学报》2023,25(2):298-310

为实现精准的旅游景区客流量的高时频预测,本研究构建了一套基于LBS和深度学习模型的预测方法。此方法可通过对LBS数据的转换实现预测的空间范围与时频控制,并通过方法的核心模型——基于双向循环神经网络和GRU算法构建的深度双向GRU(DBi-GRU)模型完成预测。为检验方法的有效性,研究以深圳大梅沙海滨公园为例对方法进行实验测试。实验使用拟合曲线、误差指标及DM检验3种方法评估DBi-GRU模型的预测效果。此外,实验还设置了其他五种深度学习模型作为DBi-GRU的对照模型,测试基于不同深度学习算法的模型之间的预测水平差异。实验结果表明:(1)本研究提出的DBi-GRU模型在景区客流量高时频预测中具有理想的预测效果,在高峰时段的客流量预测方面也具有较高准确性,预测效果明显优于其他深度学习模型;(2)基于双向循环网络的模型的效果普遍优于基于常规循环网络的模型。尤其是基于双向LSTM算法的模型,虽然预测的准确度略逊色于DBi-GRU模型,但在模型性能上与其的差异并不显著;(3)在相同网络参数下,GRU算法较前人采用的LSTM和RNN算法有着更高的预测准确性。本研究为客流量预测领域的研究提供了一种...  相似文献   

Predicting plant water content in Eucalyptus grandis forest stands in KwaZulu-Natal,South Africa using field spectra resampled to the Sumbandila Satellite Sensor     

Z. Oumar  O. Mutanga 《International Journal of Applied Earth Observation and Geoinformation》2010

The measurement of plant water content is essential to assess stress and disturbance in forest plantations. Traditional techniques to assess plant water content are costly, time consuming and spatially restrictive. Remote sensing techniques offer the alternative of a non-destructive and instantaneous method of assessing plant water content over large spatial scales where ground measurements would be impossible on a regular basis. In the context of South Africa, due to the cost and availability of imagery, studies focusing on the estimation of plant water content using remote sensing data have been limited. With the scheduled launch of the South African satellite SumbandilaSat evident in 2009, it is imperative to test the utility of this satellite in estimating plant water content. This study resamples field spectral data measured from a field spectrometer to the band settings of the SumbandilaSat in order to test its potential in estimating plant water content in a Eucalyptus plantation. The resampled SumbandilaSat wavebands were input into a neural network due to its ability to model non-linearity in a dataset and its inherent ability to perform better than conventional linear models. The integrated approach involving neural networks and the resampled field spectral data successfully predicted plant water content with a correlation coefficient of 0.74 and a root mean square error (RMSE) of 1.41% on an independent test dataset outperforming the traditional multiple regression method of estimation. The best-trained neural network algorithm that was chosen for assessing the relationship between plant water content and the SumbandilaSat bands was based on a few points only and more research is required to test the robustness and effectiveness of this sensor in estimating plant water content across different species and seasons. This is critical for monitoring plantation health in South Africa using a cheaply available local sensor containing key vegetation wavelengths.  相似文献   

径向基神经网络GPS高程转换方法     

杨金玲  曹先革  曹先密 《地理空间信息》2010,8(6):65-66,70

针对GPS高程转换问题,给出了基于径向基神经网络转换GPS高程的模型。用实际观测数据对该模型进行了试验,结果表明,用径向基神经网络转换GPS高程精度高于二次拟合法和BP神经网络法。径向基神经网络能够有效克服BP神经网络局部极小值的缺点,并且具有较高的收敛速度,在GPS高程转换方面具有广阔应用前景。  相似文献   

Seismic ground-roll noise attenuation using deep learning     

Harpreet Kaur  Sergey Fomel  Nam Pham 《Geophysical Prospecting》2020,68(7):2064-2077

We propose to adopt a deep learning based framework using generative adversarial networks for ground-roll attenuation in land seismic data. Accounting for the non-stationary properties of seismic data and the associated ground-roll noise, we create training labels using local time–frequency transform and regularized non-stationary regression. The basic idea is to train the network using a few shot gathers such that the network can learn the weights associated with noise attenuation for the training shot gathers. We then apply the learned weights to test ground-roll attenuation on shot gathers, that are not a part of training input to obtain the desired signal. This approach gives results similar to local time–frequency transform and regularized non-stationary regression but at a significantly reduced computational cost. The proposed approach automates the ground-roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Tests on field-data examples verify the effectiveness of the proposed approach.  相似文献   

Free-Swell and Swelling Pressure of Unsaturated Compacted Clays; Experiments and Neural Networks Modeling   总被引：1，自引：0，他引：1  

Iman Ashayeri  Shahabodin Yasrebi 《Geotechnical and Geological Engineering》2009,27(1):137-153

Expansive soils have received attentions of several investigators in the past half of century in the problematic soils context. Volume change behavior of unsaturated compacted soils in presence of water and change of degree of saturation was observed in two form of heave or collapse. Low water content and low density compacted soils in presence of enough surcharge pressure lose stability and collapse, because of their metastable and susceptible structure to change of degree of saturation. Free-swell and swelling pressure of five compacted clays, covering low to high plastic clays have been investigated in respect to compaction states and swelling pressure was compared with collapse pressure threshold. The results of experiments were utilized in two Artificial Neural Networks to predict free-swell percent and swelling pressure of a soil sample based on index properties and compaction state.  相似文献   

基于粗糙集的BP神经网络在震例中的应用研究   总被引：2，自引：1，他引：1  

董晓娜  苏道磊  李希亮  曲利  张慧峰  吴晨 《地震研究》2012,35(2):251-259,296

采用《中国震例》作为数据源,通过初步整理分析和预处理,构建了较完备的震例研究样本集。尝试将粗糙集与BP神经网络相结合的方法引入到震例研究中,用基于粗糙集的属性约简算法从众多复杂的地震异常指标中筛选出对最终分类起决定作用的核心异常作为输入,震级作为输出,构建了泛化能力强的BP神经网络模型来模拟异常与地震之间的不确定关系。仿真测试结果表明:地震震级预测精度误差基本控制在-0.5～0.5级之间。  相似文献   

Radial Basis Functional Link Nets Used as a Prospectivity Mapping Tool for Orogenic Gold Deposits Within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield   总被引：1，自引：0，他引：1  

Vesa Nykänen 《Natural Resources Research》2008,17(1):29-48

Among the more popular spatial modeling techniques, artificial neural networks (ANN) are tools that can deal with non-linear relationships, can classify unknown data into categories by using known examples for training, and can deal with uncertainty; characteristics that provide new possibilities for data exploration. Radial basis functional link nets (RBFLN), a form of ANN, are applied to generate a series of prospectivity maps for orogenic gold deposits within the Paleoproterozoic Central Lapland Greenstone Belt, Northern Fennoscandian Shield, Finland, which is considered highly prospective yet clearly under explored. The supervised RBFLN performs better than previously applied statistical weights-of-evidence or conceptual fuzzy logic methods, and equal to logistic regression method, when applied to the same geophysical and geochemical data layers that are proxies for conceptual geological controls. By weighting the training feature vectors in terms of the size of the gold deposits, the classification of the neural network results provides an improved prediction of the distribution of the more important deposits/occurrences. Thus, ANN, more specifically RBFLN, potentially provide a better tool to other methodologies in the development of prospectivity maps for mineral deposits, hence aiding conceptual exploration.  相似文献   

Automatic classification of dome instabilities based on Doppler radar measurements at Merapi volcano, Indonesia: Part I     

M. Vöge  M. Hort 《Geophysical Journal International》2008,172(3):1188-1206

Monitoring lava dome instabilities is crucial to efficiently monitor active dome building volcanoes. The Doppler radar technique provides a unique opportunity to gather information about the number of instability events occurring at the growing dome and about the dynamic processes that take place during different types of instabilities. So far, three different kinds of processes have been identified: sliding material, gravitational break-offs and explosive outbursts. In addition, Doppler radars provide rain measurements, which can be used to investigate possible correlations between rainfall and dome activity. Two radar systems have been installed at Merapi volcano in October 2001 and January 2005 to continuously monitor dome instabilities. Due to the large number of instability events that occur during times of high activity, manual processing and analysis of instability events is not practical for monitoring purposes. Therefore, an automatic classification system has been developed, which is capable of identifying different kinds of instabilities as well as rainfall. Two different kinds of classifier models have been applied: (1) neural network and (2) K-nearest-neighbour classifier model. Both classify Doppler spectra according to the underlying dynamic process, that is, rain, sliding material, gravitational break-off or explosive outburst. The classifiers are able to identify disturbances, which have no physical source, but are merely artefacts from the radar device itself. Because radar events are sequences of Doppler spectra, a rule set has been defined, which finally determines the event class. All classifiers have been trained and tested on independent data sets to estimate the classification performance. The overall classification rate is about 90 per cent. Discrimination of instabilities and non-volcanic events reaches about 98 per cent accuracy.  相似文献   

Novel deseasonalizing models for improving the prediction of total ozone in column using evolutionary programming and neural networks     

S. Salcedo-Sanz  J.L. Camacho  .M. Prez-Bellido  E. Hernndez-Martín 《Journal of Atmospheric and Solar》2010,72(18):1333-1340

In this paper we present a novel method for deseasonalizing TOC data using non-linear models, with evolutionary computation techniques, and its performance with a neural network as regression approach. Specifically, the proposed deseasonalization method uses an evolutionary programming (EP) approach to carry out a curve fitting problem, where a given function model is optimized to be as similar as possible to an objective curve (a real TOC measurement in this case). Different non-linear models are proposed to be optimized with the EP algorithm. In addition, we test the possibility of deseasonalizing the TOC measurement and also the meteorological input data. The deseasonalized series is then used to train a neural network (multi-layer perceptron). We test the proposed models in the prediction of several TOC series in the Iberian Peninsula, where we carry out a comparison against a reference deseasonalizing model previously proposed in the literature. The results obtained show the good performance of some of the deseasonalizing models proposed in this paper.  相似文献