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Application Study of the Microwave Emissivity Spectra in the Estimation of Salt Content of Saline Soil
Available online at www.sciencedirect.com
ScienceDirect Procedia Computer Science 107 (2017) 727 – 732
International Congress of Information and Communication Technology (ICICT 2017)
Application Study of the Microwave Emissivity Spectra in the Estimation of Salt Content of Saline Soil Tao Chia*, Bingchun Lib, Longlong Mub, Guangpu Caoa a
Shanghai Ocean University, 999 Huchenghuan Rd, Shanghai and 201306, China b Kashgar University, 29 Xueyuan Rd, Kashgar and 844006, China * Corresponding author˖[email protected]
Abstract
The objective of this paper is to ascertain the mechanism of microwave emissivity spectra for soil salinization, which is of great importance for improving the accuracy of the microwave emissivity. The saline soils were selected and the microwave emissivity spectra data of soils were obtained. According to the analysis the microwave reflectance based on the soil dielectric constant and soil salinity conductivity data or salt content data, the relationships between them were studied. The dielectric constant of the soil moisture was compared with the microwave reflectance in the different band and then the inversion model was established. The results of this study can solve the NP-problem on the microwave inversion model, and provide a theoretical basis to improve lowcost and remote sensing monitoring nodeÿ accuracy of soil salinization. Keywords:Infrared radiation; silicon detector; Dielectric constant; Salt content; Microwave emissivity;
1. Introduction Soil salinization is a common phenomenon in the arid/semi-arid plains in recent years because of the climate and human factors, especially the saline soil area of southern Xinjiang area accounted for 19.35% of the available land, development has been a serious threat to the local ecological, economic and social fields. The rapid, accurate and automatic access to the saline soil salinity information is of great significance to the management of saline soil and rational planning and utilization. Different from the traditional soil salinization monitoring using field fixed point survey combined with laboratory testing technology, hyperspectral remote sensing has become a means of largescale soil salinization monitoring [1]. At present, many scholars have studied the hyperspectral characteristics and quantitative inversion of soil salinization, and have made a lot of research results. Khan N M found soil salinity sensitive response data band in
1877-0509 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 7th International Congress of Information and Communication Technology doi:10.1016/j.procs.2017.03.155
728
Tao Chi et al. / Procedia Computer Science 107 (2017) 727 – 732
the ETM+ data[2]; Herve N found positive correlation conductivity and soil salinity index[3]; Luan Fuming established the inversion model of spectral reflectance and soil PH. Domestic and foreign experts have done a lot of research on infrared characteristics of soil heat, especially the use of infrared spectrometer measurement of soil samples has accumulated large amounts of data. Here the reflection spectrum curve is mainly concentrated in the 2.5Pm ~ 14Pm , the thermal infrared radiation rate can be obtained by the conversion. The results show that soil salinity and soil spectral feature bands are mainly concentrated in the range of 450nm ~ 620nm , 1.4Pm , 1.9Pm , 2.2Pm ~ 2.4Pm band, and these bands are coincident with the distribution range of water vapor and minerals, so the difficulty of extracting the information of soil salinity was increased. So far, few scholars have tried to extract the microwave emission spectrum of soil salinization information, mainly due to the detection objects belonging to the target surface, where the diffuse reflection echo is more serious and there is no obvious peak. Another reason is the complex structures of detection object. Our work focuses on the microwave radiation of a series of field experiments under controlled conditions, the purpose is to establish a salt/water content of saline soil separated from the dielectric mixing model and then to develop a soil salinity detection device with low cost and automation. 2. Detection Device Our device is based on Microwave Scattering by two polarized antenna, a group for the launch of the microwave signal, the other group used for receiving microwave signal, the frequency range of emission end is 1GHz~10GHz. First of all, according to the sensitive frequency band of microwave in saline soil with different water content and salt content, we establish the electromagnetic pulse sequence specific (timing function), which can be converted into frequency signal on the on-chip FFT unit and then the specific electromagnetic wave will issue through the antenna. Next, the electromagnetic wave emission to the measured soil layer will be reflected and scattered to the receiving end and then the receiving antenna receives the echo signal. Experiments show that the dielectric constants of the microwave scattering with different water content and salt content is significantly different. Since the received signal strength caused significant changes, the obvious difference signal can be used to detect the inversion of soil salinity. 2.1. The Transmitter and Receiver The detection device is based on the field sampling under the condition of microwave frequency control. The device can control and measure the microwave from the transmitting end to the receiving end, especially measuring the output power of the transmitting antenna and the receiving energy spectrum signal of the receiving antenna in outdoor scene. The system consists of two groups of polarized antenna to complete the launch operation and receive operation, the launch frequency range is 1GHz~10GHz. The transmitter end uses the lead low pass filter and collects the time as the main coordinate, while the receiver can receive the energy spectrum signal. In order to better measure the output energy of the transmitter, the transmitter sends a signal to a frequency in a period of time; terminal transmission power must be concentrated in one frequency emission value for each time period in the experiment. Rather than dispersed in a bandwidth, we measured the absolute value of the maximum transmission power. 2.2. Correlation Analysis Experiments show that the water or salinity content of saline soil will affect the conductivity of saline soil in the microwave band, thereby affecting the scattering coefficient and the microwave radiation. Figure 1 summarizes the relationship between soil salinity and the frequency of the dielectric constant of 1GHz~10GHz, and the correlation between the soil salt content and dielectric constant was extremely significant in the whole band. In the microwave frequency under a given condition, the soil salt content is increasing when the dielectric constant increases, and the increasing trend is obviously different; in the frequency of 1GHz to 10GHz, the dielectric constant with the frequency increasing change slows down. Figure 2 summarizes the relationship between soil moisture content and the dielectric constant of the soil under certain frequency. The main effect of the dielectric constant on microwave response comes from the changes of soil salinity and moisture content. Figure 3 describes the relationship between
Tao Chi et al. / Procedia Computer Science 107 (2017) 727 – 732
emissivity and soil moisture in the 5GHz band. With the increase of soil moisture, the reflectivity decreases. However, the experiment also found that, in most cases, due to low emission rate, the sensitivity to salinity change is very low in the whole band and sometimes does not exist according to the dielectric model of expected identifiable characteristics related to salinity curve.
Fig. 1. The relationship between soil salinity and the frequency of the dielectric constant of 1GHz~10GHz
Fig. 2. The relationship between soil moisture and the dielectric constant of the soil under certain frequency
3. Inversion model of soil salinity with microwave scattering Although the correlation between the microwave radiation coefficient and the salt content has reached a very significant level in the whole wave band, there exist multiple solutions or no solution in the quantitative dielectric constant due to no identifiable salt content dependent characteristic curve in the dielectric model. This research is based on the data of the output power of the transmitting antenna and the receiving energy spectrum of the receiving antenna under the controlled conditions. On the basis of this data, the model of the soil salinity content in microwave scattering / radiation was established. First, to process the received microwave energy spectrum data. There are a variety of methods for feature extraction of the microwave spectrum and a simple implementation is an effective threshold method. However, due to the reflection of the soil salinity, there will be a number of peak distribution. To search for an optimal multiple threshold combination in such complex microwave energy spectrum with multiple peak distributions, it can not meet the requirements of real-time. The accurate determination of threshold is the key to the effective extraction of microwave energy spectrum. Therefore, it will be difficult to find a fast and accurate search for multiple threshold combinations of microwave energy spectrum. Based on the previous work, this paper proposes a microwave energy spectrum matching technique based on mutual information. In a different frequency band of the transmitter when receiving antenna by microwave in different time domain and frequency domain on the energy spectrum, and the use of mutual information criterion combined with particle swarm algorithm (PSO algorithm) has improved mutual information matching speed. From our results, the measurement standard of mutual information is very suitable for multi band, multi microwave power spectrum matching operation, and can overcome the Gauss noise. In the case of low signal to noise ratio, a good matching result can be obtained by using mutual information directly. Firstly, one or more features are extracted from the real-time measurement of the microwave energy spectrum and the reference spectrum (sample map). And then by using a certain similarity measure, the characteristics of real time measurement are compared to the reference microwave energy spectra. Finally, a series of correct and deformation parameters can be obtained for correction and matching. The whole process can be regarded as the microwave extraction of spectral characteristics, the definition of matching search strategy and the similarity measure process.
729
730
Tao Chi et al. / Procedia Computer Science 107 (2017) 727 – 732
3.1. The Data of Microwave Spectrum The first step is to extract the characteristics of microwave energy spectrum. According to the data from the soil particle size, soil moisture content and dielectric permittivity, the characteristics of microwave spectrum on soil salinity was analyzed. The high-salinity saline soil has the characteristic of the obvious reflection microwave in specific band. Due to the lack of similarity of energy spectrum in wide band gap, the contrast features of the energy spectra are always kept in a limited band. For example, according to great difference in gray level of the characteristics of spectrum data of saline soil, the method of extracting edge is used for feature match. The second step is to establish a similarity measure. This step is closely related to the selection of feature matching. When the gray level is selected as the matching feature, the absolute difference is used as the similarity measure. When the spectral line is selected as the matching feature, the phase correlation is used as the similarity measure. When the selected point set matching is used as the matching feature, the minimum distance method is used as the similarity measure. Since the resolutions of the microwave reflection reference spectrum and the measured spectral line are different, the calculation result of mutual information is very unstable. and there are a large number of local extreme value. When the correct matching position is not sharp peak, the search process will consume more time and the error is larger. Therefore, how to reduce the influence of a large number of similar regions in the measured spectral line is a problem to be solved in the localization of microwave reflection spectrum. 3.2. Define Matching Search Strategy Particle swarm optimization algorithm is used in this study to establish the matching search strategy, which is an optimization algorithm proposed by Kennedy and Ebethart in 1995. All particles have a fit determined by the value of optimization function, each particle has a speed determined by the direction and distance of radiation. In each iteration, the particle is updated by following the two "extreme", a characterization of the local extremum, a characterization of the global extremum. In finding these two optimal values, the particles are updated according to the following formula.
sl 1
sl c1 u rand () u ( x pbest xl ) c2 u rand () u ( xgbest xl )
(1)
xl 1
xl sl 1
(2)
The first part of formula (1) represents particles on their current matching degree, which reflects the past particle radiation spectrum influence on the radiation spectrum; the second part is the "cognitive" part, thinking about the particle itself, which is a random act to strengthen in the future the probability increases, so as to achieve an enhanced the learning process; the third part is "social", said the inter particle information sharing and mutual cooperation. The part of the "society" can be explained by the particle itself as a reference to the movement of particles in a population. For example, the dielectric constant of water is 80, the dielectric constant of air is 1, and the dielectric constant of dry soil is 4~20. Since remote sensing microwave spectrum does not belong to the category of continuous function using mutual information measure, so this algorithm was applied to set the optimal parameters, the cycle ending conditions and many effective mechanisms. ķ To initial the position x01 and matching degree s01 . All the initial position usually was generated randomly and the coordinates of each particle was set to its current position. All the position need to calculate the corresponding individual extremum and x g b est takes the best individual extremum as the global extremum. And then we record the best position in the particle number and adjust the current position to the global extremum. ĸ To evaluate each position. Each evaluation of position is calculated. If the evaluation is better then the individual extreme, the current position will be set to the new position and update the individual extremum.
Tao Chi et al. / Procedia Computer Science 107 (2017) 727 – 732
4. Experimental results 4.1. The Microwave Energy Spectrum In this study, the microwave energy spectrum is used to match the experimental data into the microwave energy spectrum and the measured microwave energy spectrum. The data set has five non continuous characteristics, which are respectively, the soil water content, iron oxide content, light color mineral content (carbonate / soluble salt / silicon), soil particle size and weight, clay content.
Fig. 3. The measured and labeled spectra of microwave energy spectrum under 5GHz
4.2. Model Validation In order to verify the efficiency of algorithm for microwave spectral problems, this study will use exhaustive method, the standard PSO algorithm, improved PSO algorithm for processing. In standard PSO, the weight is reduced from 0.9 to 0.4 and c1 c2 2.0 . In the improved algorithm, this paper uses the conclusion of Clerc et al, c1 2.8 and c2 1.3 . The initial population is set to 50, the maximum number of iterations is 3000 times.If the 3000 time has not yet converged to the optimal solution, the operation fails. The following table reflects the efficiency comparison of the three algorithms. Table 1 The Comparison of the efficiency of three algorithms Microwave Exhaustiv Standard PSO Improved PSO Comparison items energy e method algorithm algorithm spectrum time consuming(ms) 5500 1087 709 Gray scale Call entropy function 17283 822 316 Success ratio 65% 79% time consuming(ms) 7568 1231 1207 Spectral line Call entropy function 26321 526 615 diagram Success ratio 57% 73%
731
732
Tao Chi et al. / Procedia Computer Science 107 (2017) 727 – 732
Fig. 4. The measured results of phase difference 5. Conclusion and discussion The correlation between the microwave radiation coefficient and the salt content of the soil reached a very significant level in the whole wave band, the inversion of microwave energy spectrum data can solve the quantitative problem of dielectric constant, even lack of the characteristic curve related to soil salt content. For the microwave energy spectrum data, the feature transform and feature extraction are performed to construct a data set as the input data of the model and PSO algorithm is used to construct the model due to its strong ability of linear and nonlinear fitting. There is a great advantage in simulating the complex relationship between the characteristic data of microwave energy spectrum and soil salinity, but it can only be used for training data. Acknowledgments This work is supported in part by the key program of National Natural Science Foundation of China under Grant No. 61561027. References 1. McCulloch W. S, Pitts W., A Logical Calculus of the Ideas Immanent in Nervous Active. Bulletin of Mathematical Btophysics, 1943, 5: 115133. 2. David C Hyland, et al. Neural Network System Identification for Improved Noise Rejection. International Journal of Control, 1997, 68(2): 233-258. 3. Horton M P. Real-time Identification of Missile Aerodynamics Using a Linearised Kalman Filter Aided by an Artificial Neural Network. IEE Proc. –Control Theory Appl., 1997,144(4): 299-308. 4. Anuradha M Annaswamy, Ssu-Hsin Yu. Adaptive Neural Networks: A New Approach to Prameter Estimation. IEEE Trans. On Neural Netwoks, 1996,7(4): 907-918. 5. Kumpati S. Narendua, Snehasis Mukhopadhyay. Adoptive Control Using Neural Networks and Approximate Models. IEEE Trans. On Neural Networks,1997,8(3):475-485. 6. Lightbody G, Prof. Irwin G W. Direct Neural Model Reference Adaptive Control. IEE Proc. –Control Theory Appl.,1995,142(1):31-43. 7. Zadeh L A, Fuzzy Sets. Information and Control.1965,8:338-353. 8. Mamdani E H. Applications of Fuzzy Algorithms for Control of Simple Dynamic Plant. Proc. IEEE,1974,121(21):1585-1588: 9. Jamshidi M,et al. Fuzzy Logic and Control. Englewood Cliffs,NJ:Prentice Hall,1993 10. Kandel A, Langholz G, (Eds). Fuzzy control systems. Boca Raton, FL.:CRC Press, 1994. 11. Pedrycz W, Fuzzy Control and Fuzzy Systems. Taunton, Somerset, England: Research Studies Press LTD., 1989 12. Lee C C. Fuzzy Logic Control Systems: Fuzzy Logic Controller-Part I. IEEE Trans. Syst.,Man Cyber., 1990, 20, 404-415 13. Ying, H. General Analytical Structure of Typical Fuzzy Controllers and Their Limiting Structure Theorems. Automation, 1993, 29: 11391143
ScienceDirect Procedia Computer Science 107 (2017) 727 – 732
International Congress of Information and Communication Technology (ICICT 2017)
Application Study of the Microwave Emissivity Spectra in the Estimation of Salt Content of Saline Soil Tao Chia*, Bingchun Lib, Longlong Mub, Guangpu Caoa a
Shanghai Ocean University, 999 Huchenghuan Rd, Shanghai and 201306, China b Kashgar University, 29 Xueyuan Rd, Kashgar and 844006, China * Corresponding author˖[email protected]
Abstract
The objective of this paper is to ascertain the mechanism of microwave emissivity spectra for soil salinization, which is of great importance for improving the accuracy of the microwave emissivity. The saline soils were selected and the microwave emissivity spectra data of soils were obtained. According to the analysis the microwave reflectance based on the soil dielectric constant and soil salinity conductivity data or salt content data, the relationships between them were studied. The dielectric constant of the soil moisture was compared with the microwave reflectance in the different band and then the inversion model was established. The results of this study can solve the NP-problem on the microwave inversion model, and provide a theoretical basis to improve lowcost and remote sensing monitoring nodeÿ accuracy of soil salinization. Keywords:Infrared radiation; silicon detector; Dielectric constant; Salt content; Microwave emissivity;
1. Introduction Soil salinization is a common phenomenon in the arid/semi-arid plains in recent years because of the climate and human factors, especially the saline soil area of southern Xinjiang area accounted for 19.35% of the available land, development has been a serious threat to the local ecological, economic and social fields. The rapid, accurate and automatic access to the saline soil salinity information is of great significance to the management of saline soil and rational planning and utilization. Different from the traditional soil salinization monitoring using field fixed point survey combined with laboratory testing technology, hyperspectral remote sensing has become a means of largescale soil salinization monitoring [1]. At present, many scholars have studied the hyperspectral characteristics and quantitative inversion of soil salinization, and have made a lot of research results. Khan N M found soil salinity sensitive response data band in
1877-0509 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 7th International Congress of Information and Communication Technology doi:10.1016/j.procs.2017.03.155
728
Tao Chi et al. / Procedia Computer Science 107 (2017) 727 – 732
the ETM+ data[2]; Herve N found positive correlation conductivity and soil salinity index[3]; Luan Fuming established the inversion model of spectral reflectance and soil PH. Domestic and foreign experts have done a lot of research on infrared characteristics of soil heat, especially the use of infrared spectrometer measurement of soil samples has accumulated large amounts of data. Here the reflection spectrum curve is mainly concentrated in the 2.5Pm ~ 14Pm , the thermal infrared radiation rate can be obtained by the conversion. The results show that soil salinity and soil spectral feature bands are mainly concentrated in the range of 450nm ~ 620nm , 1.4Pm , 1.9Pm , 2.2Pm ~ 2.4Pm band, and these bands are coincident with the distribution range of water vapor and minerals, so the difficulty of extracting the information of soil salinity was increased. So far, few scholars have tried to extract the microwave emission spectrum of soil salinization information, mainly due to the detection objects belonging to the target surface, where the diffuse reflection echo is more serious and there is no obvious peak. Another reason is the complex structures of detection object. Our work focuses on the microwave radiation of a series of field experiments under controlled conditions, the purpose is to establish a salt/water content of saline soil separated from the dielectric mixing model and then to develop a soil salinity detection device with low cost and automation. 2. Detection Device Our device is based on Microwave Scattering by two polarized antenna, a group for the launch of the microwave signal, the other group used for receiving microwave signal, the frequency range of emission end is 1GHz~10GHz. First of all, according to the sensitive frequency band of microwave in saline soil with different water content and salt content, we establish the electromagnetic pulse sequence specific (timing function), which can be converted into frequency signal on the on-chip FFT unit and then the specific electromagnetic wave will issue through the antenna. Next, the electromagnetic wave emission to the measured soil layer will be reflected and scattered to the receiving end and then the receiving antenna receives the echo signal. Experiments show that the dielectric constants of the microwave scattering with different water content and salt content is significantly different. Since the received signal strength caused significant changes, the obvious difference signal can be used to detect the inversion of soil salinity. 2.1. The Transmitter and Receiver The detection device is based on the field sampling under the condition of microwave frequency control. The device can control and measure the microwave from the transmitting end to the receiving end, especially measuring the output power of the transmitting antenna and the receiving energy spectrum signal of the receiving antenna in outdoor scene. The system consists of two groups of polarized antenna to complete the launch operation and receive operation, the launch frequency range is 1GHz~10GHz. The transmitter end uses the lead low pass filter and collects the time as the main coordinate, while the receiver can receive the energy spectrum signal. In order to better measure the output energy of the transmitter, the transmitter sends a signal to a frequency in a period of time; terminal transmission power must be concentrated in one frequency emission value for each time period in the experiment. Rather than dispersed in a bandwidth, we measured the absolute value of the maximum transmission power. 2.2. Correlation Analysis Experiments show that the water or salinity content of saline soil will affect the conductivity of saline soil in the microwave band, thereby affecting the scattering coefficient and the microwave radiation. Figure 1 summarizes the relationship between soil salinity and the frequency of the dielectric constant of 1GHz~10GHz, and the correlation between the soil salt content and dielectric constant was extremely significant in the whole band. In the microwave frequency under a given condition, the soil salt content is increasing when the dielectric constant increases, and the increasing trend is obviously different; in the frequency of 1GHz to 10GHz, the dielectric constant with the frequency increasing change slows down. Figure 2 summarizes the relationship between soil moisture content and the dielectric constant of the soil under certain frequency. The main effect of the dielectric constant on microwave response comes from the changes of soil salinity and moisture content. Figure 3 describes the relationship between
Tao Chi et al. / Procedia Computer Science 107 (2017) 727 – 732
emissivity and soil moisture in the 5GHz band. With the increase of soil moisture, the reflectivity decreases. However, the experiment also found that, in most cases, due to low emission rate, the sensitivity to salinity change is very low in the whole band and sometimes does not exist according to the dielectric model of expected identifiable characteristics related to salinity curve.
Fig. 1. The relationship between soil salinity and the frequency of the dielectric constant of 1GHz~10GHz
Fig. 2. The relationship between soil moisture and the dielectric constant of the soil under certain frequency
3. Inversion model of soil salinity with microwave scattering Although the correlation between the microwave radiation coefficient and the salt content has reached a very significant level in the whole wave band, there exist multiple solutions or no solution in the quantitative dielectric constant due to no identifiable salt content dependent characteristic curve in the dielectric model. This research is based on the data of the output power of the transmitting antenna and the receiving energy spectrum of the receiving antenna under the controlled conditions. On the basis of this data, the model of the soil salinity content in microwave scattering / radiation was established. First, to process the received microwave energy spectrum data. There are a variety of methods for feature extraction of the microwave spectrum and a simple implementation is an effective threshold method. However, due to the reflection of the soil salinity, there will be a number of peak distribution. To search for an optimal multiple threshold combination in such complex microwave energy spectrum with multiple peak distributions, it can not meet the requirements of real-time. The accurate determination of threshold is the key to the effective extraction of microwave energy spectrum. Therefore, it will be difficult to find a fast and accurate search for multiple threshold combinations of microwave energy spectrum. Based on the previous work, this paper proposes a microwave energy spectrum matching technique based on mutual information. In a different frequency band of the transmitter when receiving antenna by microwave in different time domain and frequency domain on the energy spectrum, and the use of mutual information criterion combined with particle swarm algorithm (PSO algorithm) has improved mutual information matching speed. From our results, the measurement standard of mutual information is very suitable for multi band, multi microwave power spectrum matching operation, and can overcome the Gauss noise. In the case of low signal to noise ratio, a good matching result can be obtained by using mutual information directly. Firstly, one or more features are extracted from the real-time measurement of the microwave energy spectrum and the reference spectrum (sample map). And then by using a certain similarity measure, the characteristics of real time measurement are compared to the reference microwave energy spectra. Finally, a series of correct and deformation parameters can be obtained for correction and matching. The whole process can be regarded as the microwave extraction of spectral characteristics, the definition of matching search strategy and the similarity measure process.
729
730
Tao Chi et al. / Procedia Computer Science 107 (2017) 727 – 732
3.1. The Data of Microwave Spectrum The first step is to extract the characteristics of microwave energy spectrum. According to the data from the soil particle size, soil moisture content and dielectric permittivity, the characteristics of microwave spectrum on soil salinity was analyzed. The high-salinity saline soil has the characteristic of the obvious reflection microwave in specific band. Due to the lack of similarity of energy spectrum in wide band gap, the contrast features of the energy spectra are always kept in a limited band. For example, according to great difference in gray level of the characteristics of spectrum data of saline soil, the method of extracting edge is used for feature match. The second step is to establish a similarity measure. This step is closely related to the selection of feature matching. When the gray level is selected as the matching feature, the absolute difference is used as the similarity measure. When the spectral line is selected as the matching feature, the phase correlation is used as the similarity measure. When the selected point set matching is used as the matching feature, the minimum distance method is used as the similarity measure. Since the resolutions of the microwave reflection reference spectrum and the measured spectral line are different, the calculation result of mutual information is very unstable. and there are a large number of local extreme value. When the correct matching position is not sharp peak, the search process will consume more time and the error is larger. Therefore, how to reduce the influence of a large number of similar regions in the measured spectral line is a problem to be solved in the localization of microwave reflection spectrum. 3.2. Define Matching Search Strategy Particle swarm optimization algorithm is used in this study to establish the matching search strategy, which is an optimization algorithm proposed by Kennedy and Ebethart in 1995. All particles have a fit determined by the value of optimization function, each particle has a speed determined by the direction and distance of radiation. In each iteration, the particle is updated by following the two "extreme", a characterization of the local extremum, a characterization of the global extremum. In finding these two optimal values, the particles are updated according to the following formula.
sl 1
sl c1 u rand () u ( x pbest xl ) c2 u rand () u ( xgbest xl )
(1)
xl 1
xl sl 1
(2)
The first part of formula (1) represents particles on their current matching degree, which reflects the past particle radiation spectrum influence on the radiation spectrum; the second part is the "cognitive" part, thinking about the particle itself, which is a random act to strengthen in the future the probability increases, so as to achieve an enhanced the learning process; the third part is "social", said the inter particle information sharing and mutual cooperation. The part of the "society" can be explained by the particle itself as a reference to the movement of particles in a population. For example, the dielectric constant of water is 80, the dielectric constant of air is 1, and the dielectric constant of dry soil is 4~20. Since remote sensing microwave spectrum does not belong to the category of continuous function using mutual information measure, so this algorithm was applied to set the optimal parameters, the cycle ending conditions and many effective mechanisms. ķ To initial the position x01 and matching degree s01 . All the initial position usually was generated randomly and the coordinates of each particle was set to its current position. All the position need to calculate the corresponding individual extremum and x g b est takes the best individual extremum as the global extremum. And then we record the best position in the particle number and adjust the current position to the global extremum. ĸ To evaluate each position. Each evaluation of position is calculated. If the evaluation is better then the individual extreme, the current position will be set to the new position and update the individual extremum.
Tao Chi et al. / Procedia Computer Science 107 (2017) 727 – 732
4. Experimental results 4.1. The Microwave Energy Spectrum In this study, the microwave energy spectrum is used to match the experimental data into the microwave energy spectrum and the measured microwave energy spectrum. The data set has five non continuous characteristics, which are respectively, the soil water content, iron oxide content, light color mineral content (carbonate / soluble salt / silicon), soil particle size and weight, clay content.
Fig. 3. The measured and labeled spectra of microwave energy spectrum under 5GHz
4.2. Model Validation In order to verify the efficiency of algorithm for microwave spectral problems, this study will use exhaustive method, the standard PSO algorithm, improved PSO algorithm for processing. In standard PSO, the weight is reduced from 0.9 to 0.4 and c1 c2 2.0 . In the improved algorithm, this paper uses the conclusion of Clerc et al, c1 2.8 and c2 1.3 . The initial population is set to 50, the maximum number of iterations is 3000 times.If the 3000 time has not yet converged to the optimal solution, the operation fails. The following table reflects the efficiency comparison of the three algorithms. Table 1 The Comparison of the efficiency of three algorithms Microwave Exhaustiv Standard PSO Improved PSO Comparison items energy e method algorithm algorithm spectrum time consuming(ms) 5500 1087 709 Gray scale Call entropy function 17283 822 316 Success ratio 65% 79% time consuming(ms) 7568 1231 1207 Spectral line Call entropy function 26321 526 615 diagram Success ratio 57% 73%
731
732
Tao Chi et al. / Procedia Computer Science 107 (2017) 727 – 732
Fig. 4. The measured results of phase difference 5. Conclusion and discussion The correlation between the microwave radiation coefficient and the salt content of the soil reached a very significant level in the whole wave band, the inversion of microwave energy spectrum data can solve the quantitative problem of dielectric constant, even lack of the characteristic curve related to soil salt content. For the microwave energy spectrum data, the feature transform and feature extraction are performed to construct a data set as the input data of the model and PSO algorithm is used to construct the model due to its strong ability of linear and nonlinear fitting. There is a great advantage in simulating the complex relationship between the characteristic data of microwave energy spectrum and soil salinity, but it can only be used for training data. Acknowledgments This work is supported in part by the key program of National Natural Science Foundation of China under Grant No. 61561027. References 1. McCulloch W. S, Pitts W., A Logical Calculus of the Ideas Immanent in Nervous Active. Bulletin of Mathematical Btophysics, 1943, 5: 115133. 2. David C Hyland, et al. Neural Network System Identification for Improved Noise Rejection. International Journal of Control, 1997, 68(2): 233-258. 3. Horton M P. Real-time Identification of Missile Aerodynamics Using a Linearised Kalman Filter Aided by an Artificial Neural Network. IEE Proc. –Control Theory Appl., 1997,144(4): 299-308. 4. Anuradha M Annaswamy, Ssu-Hsin Yu. Adaptive Neural Networks: A New Approach to Prameter Estimation. IEEE Trans. On Neural Netwoks, 1996,7(4): 907-918. 5. Kumpati S. Narendua, Snehasis Mukhopadhyay. Adoptive Control Using Neural Networks and Approximate Models. IEEE Trans. On Neural Networks,1997,8(3):475-485. 6. Lightbody G, Prof. Irwin G W. Direct Neural Model Reference Adaptive Control. IEE Proc. –Control Theory Appl.,1995,142(1):31-43. 7. Zadeh L A, Fuzzy Sets. Information and Control.1965,8:338-353. 8. Mamdani E H. Applications of Fuzzy Algorithms for Control of Simple Dynamic Plant. Proc. IEEE,1974,121(21):1585-1588: 9. Jamshidi M,et al. Fuzzy Logic and Control. Englewood Cliffs,NJ:Prentice Hall,1993 10. Kandel A, Langholz G, (Eds). Fuzzy control systems. Boca Raton, FL.:CRC Press, 1994. 11. Pedrycz W, Fuzzy Control and Fuzzy Systems. Taunton, Somerset, England: Research Studies Press LTD., 1989 12. Lee C C. Fuzzy Logic Control Systems: Fuzzy Logic Controller-Part I. IEEE Trans. Syst.,Man Cyber., 1990, 20, 404-415 13. Ying, H. General Analytical Structure of Typical Fuzzy Controllers and Their Limiting Structure Theorems. Automation, 1993, 29: 11391143