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Han B.,Xidian University | Han B.,State Key Laboratory of Remote Sensing Science | Jia Z.,Xidian University | Gao X.,Xidian University
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | Year: 2017

The mysterious aurora is changeable, and the different forms of the aurora represent various physical processes which often affect our lives. So, it is of significant scientific value to classify the aurora images for the study of space physics. Based on the PCANet, a simple deep learning model, we develop an improved PCANet algorithm for aurora images classification. Firstly, the map of aurora images are extracted by the improved PCANet. Then the support vector machine is used to classify the feature of aurora images. Experimental results with the dataset obtained from the All-sky Imager at the Chinese Arctic Yellow River Station demonstrate that the scheme can obtain higher accuracy in aurora image classification than the PCANet. © 2017, The Editorial Board of Journal of Xidian University. All right reserved.


Zhao T.J.,State Key Laboratory of Remote Sensing Science | Zhao T.J.,Beijing Normal University | Zhang L.X.,State Key Laboratory of Remote Sensing Science | Zhang L.X.,Beijing Normal University | And 5 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2011

Surface soil moisture is the key state variable in various hydrological processes. A physically based statistical methodology for surface soil moisture measurement in the Tibet Plateau was developed in this study. The approach was established based on theoretical relationships from the derivation of physical models. The methodology was calibrated using statistical analysis of a large data set obtained during a long-term experiment in Tibet. The procedure was conducted using multichannel brightness temperature observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). The most interesting results of this study were that the newly developed microwave vegetation indices (MVIs) are a function of vegetation water content or vegetation transmissivity. The B parameter of MVIs decreased with increased vegetation water content but increased with increased vegetation transmissivity. This enabled the use of MVIs for the correction of vegetation effects in soil moisture inversion. The methodology was tested against several experimental data sets collected from Tibet and was shown to be an effective method of soil moisture retrieval for areas with sparse vegetation coverage. The results also provided a complementary data set of soil moisture for hydrology and climatology studies in the Tibet Plateau. Copyright © 2011 by the American Geophysical Union.


Wang W.-B.,Wuhan University of Science and Technology | Wang W.-B.,State Key Laboratory of Remote Sensing Science | Wang X.-L.,Wuhan University of Technology
Wuli Xuebao/Acta Physica Sinica | Year: 2013

In order to improve the de-noising effect of the pulsar signal, an empirical mode decomposition (EMD) denoising algorithm based on the prediction of noise mode cell is put forward. The core steps of the proposed method is as follows: firstly, the noisy pulsar signal is decomposed into a group intrinsic mode function (IMF) by EMD, and the noise mode cell is predicted according to the IMF coefficients statistics and local minimum mean square error criteria. The selected noise mode cells are set to be zero. Then the IMF which has been processed according to noise mode cell prediction is denoised by optimal mode cell proportion shrinking, for removing the noise and retaining the signal details. The experimental results show that compared with the Sure Shrink wavelet threshold algorithm, Bayes Shrink wavelet threshold algorithm and the EMD mode cell proportion shrinking algorithm, the proposed method performs well in removing the pulsar signal noise and retaining the signal details information. The proposed method can achieve a higher signal-to-noise, the lower root mean square error, error of the peak position, relative error of the peak value and phase error. © 2013 Chinese Physical Society.


Wang W.-B.,Wuhan University of Science and Technology | Wang W.-B.,State Key Laboratory of Remote Sensing Science | Zhang X.-D.,Hubei Engineering University | Wang X.-L.,Wuhan University of Technology
Tien Tzu Hsueh Pao/Acta Electronica Sinica | Year: 2013

In order to solve the problem of nonlinear and nonstationary signal de-noising, a novel de-noising method is proposed by combining the principal component analysis (PCA) and empirical mode decomposition (EMD). The method removes noise of intrinsic mode functions(IMFs) using PCA, after the noisy signal is decomposed by EMD. Firstly, the signal details of the first IMF are extracted by using 3σ criterion, and the noise energy of each level IMF is estimated. Secondly, the PCA is implemented on each IMF, and the part of principle components are selected to reconstruct the IMF according to noise energy of IMFs, then the noise of IMF is removed efficiently. Numerical simulation and real data test were carried out to evaluate the performance of the proposed method. The experimental results showed that the proposed method outperformed the Bayesian wavelet threshold de-noising algorithm and mode cell EMD de-noising algorithm. So it is an effective signal de-noising method.


Gong P.,State Key Laboratory of Remote Sensing Science | Gong P.,University of California at Berkeley | Li Z.,State Key Laboratory of Remote Sensing Science | Li Z.,University of Chinese Academy of Sciences | And 3 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2011

Although the Geoscience Laser Altimeter System (GLAS) onboard the NASA Ice, Cloud and Land Elevation Satellite was not designed for urban applications, its 3-D measurement capability over the globe makes it a nice feature for consideration in monitoring urban heights. However, this has not been previously done. In this paper, we report some preliminary assessment of the GLAS data for building height and density estimation in a suburb of Beijing, China. Building heights can be directly calculated from a GLAS data product (GLA14). Because GLA14 limits height levels to six in each ground footprint, we developed a new method to remove this restriction by processing the raw GLAS data. The maximum heights measured in the field at selected GLAS footprints were used to validate the GLAS measurement results. By assuming a constant incident energy and surface reflectance within a GLAS footprint, the building density can be estimated from GLA14 or from our newly processed GLAS data. The building density determined from high-resolution images in Google Earth was used to validate the GLAS estimation results. The results indicate that the newly developed method can produce more accurate building height estimation within each GLAS footprint ( R2 = 0.937, rmse = 6.4 m, and n = 26) than the GLA14 data product (R2 = 0.808, rmse = 11.5m, and n = 26). However, satisfactory estimation results on building density cannot be obtained from the GLAS data with the methods investigated in this paper. Forest cover could be a challenge to building height and density estimation from the GLAS data. It should be addressed in future research. © 2006 IEEE.


Huang H.,Beijing Forestry University | Liu Q.,State Key Laboratory of Remote Sensing Science | Qin W.,State Key Laboratory of Remote Sensing Science | Qin W.,NASA
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2010

This paper is the first part of a three-part article series. Simulations of directional brightness temperature over both simple canopies with triangular leaves and the row-planted wheat and corn were used to analyze the thermal emission hot-spot effect on crop canopies. Two models, Cupid and TRGM, were successively used to simulate the thermal hot-spot signatures under conditions which cannot be easily captured in reality. The investigation includes the planting row structure, the leaf area index (LAI), the leaf angle distribution (LAD), the component temperature distribution as well as variations in the microclimate. The results show that there are typically three types of directional emission shapes in the solar principle plane: the bowl, dome and bell shape. Regardless of the shape, the hot spot is significant and can be accurately fitted (R2=0.98 and RMSE=0.04°C) with a function of the phase angle (ξ), the hot-spot amplitude (Δ THS}) and the half width of the hot spot (ξ0), which can be quantified with the half width in the RED band. The planting row structure can reduce the Δ THS by a maximum amount (about 1.2 °C) when compared with an unstructured horizontal canopy. The Δ THS is linearly related to the component temperature differences between sunlit and shadowed parts. The linear equation can be used to predict the component temperature differences from Δ THS. The accuracy is very good for the horizontal canopies with triangular leaves (RMSE < 0.4°C and R2 > 0.99), and acceptable for the virtual wheat and corn canopies (RMSE < 1.8°C} and R2 > 0.81). © 2010 IEEE.


Zhang W.,State Key Laboratory of Remote Sensing Science | Wang C.,CAS Institute of Remote Sensing | Xi X.,State Key Laboratory of Remote Sensing Science
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2015

In ancient China, Huabiao was a type of ornamental column used to decorate important buildings. We carried out 3D scan of a Huabiao located in Peking University, China. This Huabiao was built no later than 1742. It is carved by white marble, 8 meters in height. Clouds and various postures of dragons are carved on its body. Two instruments were used to acquire the point cloud of this Huabiao, a terrestrial LiDAR (Riegl VZ-1000) and a hand-held imager (Mantis Vision F5). In this paper, the details of the experiment were described, including the differences between these two instruments, such as working principle, spatial resolution, accuracy, instrument dimension and working flow. The point clouds obtained respectively by these two instruments were compared, and the registered point cloud of Huabiao was also presented. These should be of interest and helpful for the research communities of archaeology and heritage.


Liu D.,State Key Laboratory of Remote Sensing Science | Sun G.,The Interdisciplinary Center | Guo Z.,State Key Laboratory of Remote Sensing Science | Ranson K.J.,NASA | Du Y.,Zhejiang University
IEEE Transactions on Geoscience and Remote Sensing | Year: 2010

A 3-D coherent radar backscatter model for forest canopies was developed and used to improve the understanding of synthetic aperture radar (SAR) interferometric data. The model was based on a realistic 3-D spatial structure of a forest stand, in which every scatterer has its deterministic location. A backscattering signal from a scatterer was mapped into a pixel according to its range or signal time delay. The range or the time delay also determines the phase of the scattered field. All scattering matrices within a pixel were coherently added to yield the total backscattering field of the pixel. The coherent radar backscatter model takes into account not only the scattering contribution from the scatterers in the forest canopy but also the direct backscattering of the ground surface. Forest stands with three different spatial structures were simulated using L-system and field measurements. The number and sizes of trees in these forest stands were identical, but the 2-D arrangements of the trees were different. The interferometric SAR (InSAR) signals of these scenes were simulated using the 3-D coherent SAR model, and the heights of scattering phase centers were estimated from the simulated InSAR data. The results reported in this paper show that the spatial structures of vegetation play an important role in the location of the scattering phase center. The height of scattering phase center depends on canopy height, attenuation of canopy, and the gaps within the canopy. This paper shows that the spatial structure needs to be considered when the InSAR data are used for the estimation of forest structural parameters. © 2009 IEEE.


Liu W.P.,Foshan Polytechnic | Wei F.,State Key Laboratory of Remote Sensing Science
Applied Mechanics and Materials | Year: 2014

Making use full of multi-source and multi-temporal information to extract richer and interesting information is a tendency in analysis of remote sensing images. In this paper, spatial and temporal contextual classification based on Markov Random Field (MRF) is used to classify ecological function vegetation in Poyang Lake. The results show that spatial and temporal neighborhood complementary information from different images can be used to remove the spectral confusion of different kinds of vegetation on single image and improve classification accuracy compared to MLC method. Building effective spatial and temporal neighborhood model for information extraction in special application is the key of multi-source and multi-temporal image analysis. Although spatial and temporal contextual classification method is computation demanding, it’s promising in the application emphasizing classification accuracy. © (2014) Trans Tech Publications, Switzerland.


Wang Y.,State Key Laboratory of Remote Sensing Science | Zhang L.,State Key Laboratory of Remote Sensing Science | Ma J.,PTV America Inc. | Liu L.,State Key Laboratory of Remote Sensing Science | You D.,State Key Laboratory of Remote Sensing Science
IEEE Computer Graphics and Applications | Year: 2011

Extended hierarchical node-relation model (EHI-NRM) approach has been used to represent a building's internal structure. The approach employs the improved cellular-automata model (ICA) to consider route-choice behavior. The EHI-NRM adds the minimum bounding box (MBB) of the discrete 3D objects and information relevant to rescue and evacuation to the geometric model. According to the principles for computing a hierarchical path, a building story is abstracted to boundary nodes representing its stairways and exits. For large complex buildings, each subgraph's internal evacuation paths are relatively independent. In preprocessing, any hallways were simplified, transforming those with twists and turns into several straight-line segments. After importing the building model, the EHI-NRM is automatically generated. This approach supports emergency managers in planning and training for responding to emergencies in the preemergency phase, and in coordinating and implementing evacuation or rescue operations during the emergency response.

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