The Construction of J2EE-Based Spectrum Knowledge Base System for Typical Object in China
Res. Center for Remote Sensing, Beijing Normal Univ., China
DOI: 10.1109/IGARSS.2003.1295270 Conference: Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International, Volume: 6
The Spectrum Knowledge Base System (SKBS) for Typical Object in China, built up by taking advantage of J2EE technology, is capable of providing the functionalities in spectrum analysis, query and comparison. More importantly, the spectrum scale effect, especially the scale extension, can be achieved in SKBS, which is based on the model-driven theory with the support of the prior knowledge.
Available from: Yonghua Qu
- "Therefore, the approach can be used for estimating the biophysical and biochemical parameters of a standing crop over a wider temporal and spatial range than is possible with a limited amount of ground measurements. In our approach, we also focus on incorporating ancillary information extracted from a spectral library, namely the Spectral Library on Typical Land Surface Objects in China (SLTLSOC) (Qu et al., 2003), to support the inversion, which differs from other non-parametric regression methods such as ANN and PPR. Our study sought to develop a new hybrid inversion scheme supported by the SLTLSOC and was tested against data sets obtained from both simulations and field measurements. "
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ABSTRACT: A hybrid inversion technique based on Bayesian network is proposed for estimating the biochemical and biophysical parameters of land surface vegetation from remotely sensed data. A Bayesian network is a unified knowledge-inferring process that can incorporate information derived from multiple sources including remote sensing and information derived from a priori knowledge. Using this inversion approach, content of chlorophyll a and chlorophyll b (Cab) and leaf area index (LAI) of winter wheat were estimated from data derived from simulations as well as field measurements. Estimations from the simulated data proved accurate, with root mean square errors (RMSEs) of 0.54 m2/m2 in LAI and 4.5 μg/cm2 in Cab. In validating the estimates against field measurements, it was found that prior knowledge of target parameters improved the accuracy of estimates, in terms of RMSEs from 0.73 to 0.22 m2/m2 in LAI and 9.6 to 4.0 μg/cm2 in Cab. Bayesian inference in this hybrid inversion scheme produces a posterior probability distribution, which can reveal such properties of the inferred results as updated information contained in the inversion result. Using entropy, the revision of posterior information about the parameters of interest was calculated. Including more data may allow more information to be retrieved about parameters in general. Exceptions were also observed where data from some viewing angles slightly reduced the information on the parameters of interest. It was also found that data from these viewing angles were less sensitive to the parameters. The method proposed here was also validated using LandSat ETM+ imagery provided by the BigFoot project. When used for mapping LAI with ETM+ imagery, the proposed method with an RMSE of 0.70 and a correlation of 0.67 produced a slightly better result than that from empirical regression.
Remote Sensing of Environment 03/2008; 112(3-112):613-622. DOI:10.1016/j.rse.2007.03.031 · 6.39 Impact Factor
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ABSTRACT: In most cases, statistical models for monitoring the disease severity of yellow rust are based on hyperspectral information.
The high cost and limited cover of airborne hyperspectral data make it impossible to apply it to large scale monitoring. Furthermore,
the established models of disease detection cannot be used for most satellite images either because of the wide range of wavelengths
in multispectral images. To resolve this dilemma, this paper presents a novel approach by constructing a spectral knowledge
base (SKB) of diseased winter wheat plants, which takes the airborne images as a medium and links the disease severity with
band reflectance from environment and disaster reduction small satellite images (HJ-CCD) accordingly. Through a matching process
with a SKB, we estimated the disease severity with a disease index (DI) and degrees of disease severity. The proposed approach
was validated against both simulated data and field surveyed data. Estimates of DI (%) from simulated data were more accurate,
with a coefficient of determination (R
2) of 0.9 and normalized root mean square error (NRMSE) of 0.2. The overall accuracy of classification reached 0.8, with a
kappa coefficient of 0.7. Validation of the estimates against field measurements showed that there were some errors in the DI value
with the NRMSE close to 0.5. The result of the classification was more encouraging with an overall accuracy of 0.77 and a
kappa coefficient of 0.58. For the matching process, Mahalanobis distance performed better than the spectral angle (SA) in all
analyses in this study. The potential of SKB for monitoring the incidence and severity of yellow rust is illustrated in this
KeywordsYellow rust–Pushbroom imaging spectrometer (PHI)–Environment and disaster reduction small satellites (HJ-1A/B)–Mahalanobis distance–Spectral angle (SA)
Precision Agriculture 10/2011; 12(5):716-731. DOI:10.1007/s11119-010-9214-1 · 1.93 Impact Factor
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