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A single-station ionospheric forecast model with LSTM considering multiple factors

A single-station ionospheric forecast model with LSTM consid...

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第十三届中国卫星导航年会

作者： Ting Xie Zhiqiang Dai Xiangwei Zhu Sun Yat-Sen university school of electronics and communication engineering

The purpose of this study is to verify the rationality and validity of directly adopting segmental modeling prediction of single-station regional scatter data by considering space environment information comprehensively. First, a single-station regional ionospheric model is constructed and predicted by the long short-term memory neural network（LSTM） method based on the single-station global positioning system total electron content（GPS-TEC） data of different regions（low-, mid-, and high latitude regions） and the space environment data. Then, the prediction results are compared and analyzed with the international reference ionosphere 2016（IRI2016） model, CMONOC Regional Ionosphere Maps（RIM） data, and GPS measurement data. The results show that: i) the LSTM model forecasts are consistent with GPS-TEC observations at high, mid and low latitudes, and the forecast error is less than 3 TECu. The forecast accuracy is much better than that of the IRI2016 model and RIM TEC, and is less susceptible to anomalies. Geographically, the forecast MAE and RMSE of LSTM model decreases with increasing latitude. Among them, the relative accuracy of LSTM forecasts in low and mid latitudes is high, up to 82% or more;ii) the RIM data as a whole are more consistent with the measured data, but the RIM TEC is overestimated during the daytime, a phenomenon related to the discrete anomalies;iii) the IRI2016 model only captures the general trend of TEC. The IRI model forecast values are poor in daytime forecasting, and its overestimation becomes more obvious as the latitude increases, while the forecast performance is better in the evening. This study is a foundation for subsequent regional modeling and forecasting of the ionosphere, and can provide ionospheric constraints to support navigation positioning.

关键词： LSTM Single-station VTEC model RIM IRI2016 accuracy comparison

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Classification of Alpine Wetlands Based on Feature Indices of Remote Sensing Image

Classification of Alpine Wetlands Based on Feature Indices o...

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2011 International Conference on Advanced Materials in Microwaves and Optics（AMMO 2011）

作者： Wentao Zou Huaiqing Zhang Hongbo Ju Hua Liu Research Institute of Forest Resource Information Techniques CAF

Based on the brightness, greenness, humidity indices after Tasseled Cap transformation, NDWI and DEM. Extracting the alpine wetland information in source regions of three Rivers. Result shows that: the humidity can be used to distinguish wetland types from non-wetland classes, brightness component is effective in grassland extracting, while bare rock and gravel land, sand land etc. could be distinguished through its high value on lightness component. Elevation and slope component could be taken as the threshold variable in distinguishing the marsh and bare rock and gravel land. It shows that RS feature index based classification method used on Alpine wetland could improve the overall accuracy by 10.71% and Kappa index by 0.1250 after comparing with the maximum likelihood method. The result indicates that, the method based on indices got from image band transformation is an effective way of alpine wetlands information remote sensing extracting.

关键词： Alpine wetland information exacting lightness,greenness and humidity component after tasseled cap transformation NDWI accuracy comparison

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