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Author: search.filters.author.Insom, PatcharinHas files: No

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    The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery
    (2017-12-15)
    Insom, Patcharin
    ;
    Cao, Chunxiang
    ;
    Boonsrimuang, Pisit
    ;
    Torteeka, Peerapong
    ;
    Boonprong, Sornkitja
    Monitoring the dynamics of inundation areas in wetlands over contiguous years is important because it influences wetland ecosystem monitoring. However, because the variable nature of wetlands tends to hamper monitoring change analyses, the potential for misinterpretation increases. The Kalman filter (KF) or extended Kalman filter (EKF), which uses recursive processing based on the former information, can be applied to time-series remote sensing imagery. In the experiment, a periodic triangle function of two modulated parameters is treated as the system model, and Normalized Difference Vegetation Index (NDVI) time-series data are used for the measurement model in the correction processes of the state estimation. A decision metric is computed from the mean and amplitude sequence, which results from the state estimation filter. Consequently, an optimal threshold is calculated using a minimum error thresholding algorithm based on a pre-labelled sample. NDVI time-series data from Poyang Lake, China–derived from 250-m Moderate Resolution Imaging Spectroradiometer satellite data obtained from January 2009 to December 2013–are applied to monitor the dynamics of inundation changes. The results show that the EKF achieves satisfactory results, with 85.52% accuracy in the year 2009, while the KF has an accuracy of 84.16% during that same time.
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    A support vector machine-based particle filter for improved land cover classification applied to MODIS data
    (2016-11-01)
    Insom, Patcharin
    ;
    Cao, Chunxiang
    ;
    Boonsrimuang, Pisit
    ;
    Bao, Shanning
    ;
    Chen, Wei
    There are two important factors to improve the accuracy of the support vector machine(SVM) classifier. First, selected training samples should uniquely represent each class. Second, SVM training parameters which are pre-defined by the user should be suitable for training samples to obtain satisfied results of the SVM classifier. The proposed method of this paper presents a technique to adjust the SVM training parameters by particle filter algorithm. The parameters were tuned based on the weight of particles in each iteration time of particle filter processes. An experiment implemented annual mean of Normalized Difference Vegetation Index and Enhanced Vegetation Index extracted from the Moderate Resolution Imaging Spectroradiometer data with 250-m resolution in a study area of Poyang Lake, China, during the year of 2009. The proposed method for adjusting the SVM training parameters provided the an improved performance SVM classification model.
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    Joint iterative channel estimation and decoding under impulsive interference condition
    (2016-03-01)
    Insom, Patcharin
    ;
    Insom, Piyakiat
    ;
    Boonsrimuang, Pisit
    Even though Low-Density-Parity-Check (LDPC) code which has the decoding performance close to the Shannon Limit and it is designed as a powerful forward-error-correction (FEC) code in the Additive White Gaussian Noise (AWGN) channel, simulation results show that the performance of LDPC decoder is degraded when exposed to the impulsive noise. According to such a impulsive noise impact, joint iterative channel estimation and decoding technique is proposed in this paper so as to decrease the effect of impulsive interference while less complicated in processing. The proposed methods decreases the complexity by implementing the simple way of channel estimation and applying joint iterative technique between channel estimation and LDPC decoding under two kind of impulsive noise; pulsed radio frequency interference(RFI) and symmetric alpha-stable (SαS). In the optimal decoder, channel parameter estimation can be as accurate as possible. Because computed in every time of iterative decoder, channel parameters have been always optimized resulting in the enhancement of LDPC decoder performance.
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    A Support Vector Machine-Based Particle Filter Method for Improved Flooding Classification
    (2015-09-01)
    Insom, Patcharin
    ;
    Cao, Chunxiang
    ;
    Boonsrimuang, Pisit
    ;
    Liu, Di
    ;
    Saokarn, Apitach
    Support vector machines (SVMs) have been applied to land cover classification, and a number of studies have demonstrated their ability to increase classification accuracy. The high correlation between the data set and SVM training model parameters indicates the high performance of the classification model. To improve the correlation, research has focused on the integration of SVMs and other algorithms for data set selection and SVM training model parameter estimation. This letter proposes a novel method, based on a particle filter (PF), of estimating SVM training model parameters according to an observation system. By treating the SVM training function as the observation system of the PF, the new method automatically updates the SVM training model parameters to values that are more appropriate for the data set and can provide a better classification model than can the original model, wherein the parameters are set by trial and error. Various experiments were conducted using Radarsat-2 synthetic aperture radar data from the 2011 Thailand flood. The proposed method provides superior performance and a more accurate analysis compared with the standard SVM.
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    Joint iterative channel estimation and decoding under pulsed radio frequency interference condition
    (2014-01-01)
    Insom, Patcharin
    ;
    Liu, Rongke
    ;
    Duan, Ruifeng
    ;
    Hou, Yi
    ;
    Boonsrimuang, Pisit
    Pulsed Radio Frequency Interference (RFI) has severely effected on communication system in common. Even though Low-Density-Parity-Check (LDPC) code which has the decoding performance to approach the Shannon Limit and it is designed as a powerful forward-error-correction (FEC) code in the Additive White Gaussian Noise (AWGN) channel, simulation results show that the performance of LDPC decoder is degraded when exposed to the pulsed RFI. According to such a pulsed RFI impact, joint iterative channel estimation and decoding technique is proposed in this paper so as to decrease the effect of pulsed RFI while less complicated in processing. The proposed method decreases the complexity by implementing the simple way of SNR estimation and improves the efficiency and applying joint iterative technique between channel estimation and decoding. For optimal decoder performance, Signal-to-Noise Ratio (SNR) estimation must be as accurate as possible. Because computed in each time of iterative decoder, noise variance has been always change resulting in the performance of LDPC decoder has better in efficiency. The simulation results show that both less complexity and higher outcomes can be achieved by this proposed approach. © 2014 Global IT Research Institute (GIRI).