Extraction of Soil Organic Matter Information by Multi-spectral Remote Sensing Based on Diverse Landforms

Based on diverse landforms, the correlation between soil organic matter content and multi-spectral band of remote sensing image was analyzed in this pa- per. In addition, the inversion models were built for the soil organic matter content in different landforms. The results showed that the spectral reflectance was nega- tively related to soil organic matter content; linear regression analysis of remove was performed throughout the bands using SPSS. When the inversion models were built based on all the bands, better fitting effect was obtained. The precision of in- version models built based on different landforms was higher than those built re- gardless landforms. Compared with the actual value, the identification level of soil organic matter content was 91 65% under the allowable error was 7%. It indicated that the extraction of soil organic matter with inversion model that was built based on different landforrrs was feasible with higher precision.

Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.

Extracting Feature Bands for Damaged Rice Leaves by Planthoppers Using Multi-spectral Imaging Technology

[Objective] The aim of this study was to extract effective feature bands of damaged rice leaves by planthoppers to make identification and classification rapidly from great amounts of imaging spectral data. [Method] The experiment, using multi-spectral imaging system, acquired the multi-spectral images of damaged rice leaves from band 400 to 720 nm by interval of 5 nm. [Result] According to the principle of band index, it was calculated that the bands at 515, 510, 710, 555, 630, 535, 505, 530 and 595 nm were having high band index value with rich information and little correlation. Furthermore, the experiment used two classification methods and calcu-lated the classification accuracy higher than 90.00% for feature bands and ful bands of damaged rice leaves by planthoppers respectively. [Conclusion] It can be con-cluded that these bands can be considered as effective feature bands to identify damaged rice leaves by planthoppers quickly from a large scale of crops.

Robust key point descriptor for multi-spectral image matching

Histogram of collinear gradient-enhanced coding （HCGEC）, a robust key point descriptor for multi-spectral image matching, is proposed. The HCGEC mainly encodes rough structures within an image and suppresses detailed textural information, which is desirable in multi-spectral image matching. Experiments on two multi-spectral data sets demonstrate that the proposed descriptor can yield significantly better results than some state-of- the-art descriptors.

MULTI-SPECTRAL AND HYPERSPECTRAL IMAGE FUSION USING 3-D WAVELET TRANSFORM

Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspeetral image. According to the characteristics and 3-Dimensional （3-D） feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures： Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient integration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling （RIBSR）method, is proposed to accomplish data resampling in spectral domain by utilizing the property of ratio image. And a new fusion rule, Average and Substitution （A＆S） rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral characteristics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A＆S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.

Chlorophyll Content Retrieval of Rice Canopy with Multi-spectral Inversion Based on LS-SVR Algorithm

To monitor growth and predict the yield of rice over a large area, the chlorophyll contents in the rice canopy were estimated using the unmanned aerial vehicle(UAV) remote sensing technology. In this work, multi-spectral image information of the rice crop was obtained using a 6-channel multi-spectral camera mounted on a fixed wing UAV, which was flown 600 m above the ground, between 11: 00-14: 00 on a sunny day in summer. The measured chlorophyll values were collected as sample sets. The s-REP index was screened out to estimate chlorophyll contents through the analysis of six kinds of spectral indexes of chlorophyll estimated capacity. An inversion model of the chlorophyll contents was then built using the least square support vector regression(LS-SVR)algorithm, with calibration and prediction R-square values of 0.89 and 0.83, respectively. Finally, remote sensing mapping for a UAV image of the Fangzheng County Dexter Rice Planting Park was accomplished using the inversion model. The inversion and measured values were then compared using regression fitting. R-square and root-mean-square error of the fitting model were 0.79 and 2.39,respectively. The results demonstrated that accurate estimation of rice-canopy chlorophyll contents was feasible using the LS-SVR inversion model developed using the s-REP vegetation index.

Multi-Spectral and Fluorescence Imaging in Prevention of Overdose of Herbicides: The Case of Maize

Evaluation of the impact of herbicides on maize was done through multi- spectral and multi-modal imaging and multi-spectral fluorescence imaging combined with statistical methods. Spectra containing 13 wavelengths ranging from 375 nm to 940 nm were derived from multi-spectral images in transmission, reflection and scattering mode and fluorescence images obtained using high-pass filters (F450 nm, F500 nm, F550 nm, F600 nm, F650 nm) on control maize samples and maize samples treated with Herbextra herbicide were used. The appearance of the spectra allowed us to characterize the effect of the herbicide on the maize pigment concentration. The fluorescence images allowed us to track the fate of absorbed energy and through PLS-DA and SVM-DA to discriminate the two leaf categories with very low error rates for the test, i.e. 4.9% and 2% respectively. The results of this technique can be used in the context of precision agriculture.

In Situ Atomic Reconstruction Engineering Modulating Graphene-Like MXene-Based Multifunctional Electromagnetic Devices Covering Multi-Spectrum

With the diversified development of big data,detection and precision guidance technologies,electromagnetic(EM)functional materials and devices serving multiple spectrums have become a hot topic.Exploring the multispectral response of materials is a challenging and meaningful scientific question.In this study,MXene/TiO_(2)hybrids with tunable conduction loss and polarization relaxation are fabricated by in situ atomic reconstruction engineering.More importantly,MXene/TiO_(2)hybrids exhibit adjustable spectral responses in the GHz,infrared and visible spectrums,and several EM devices are constructed based on this.An antenna array provides excellent EM energy harvesting in multiple microwave bands,with|S11|up to−63.2 dB,and can be tuned by the degree of bending.An ultra-wideband bandpass filter realizes a passband of about 5.4 GHz and effectively suppresses the transmission of EM signals in the stopband.An infrared stealth device has an emissivity of less than 0.2 in the infrared spectrum at wavelengths of 6-14μm.This work can provide new inspiration for the design and development of multifunctional,multi-spectrum EM devices.

New Abnormal Cervical Cell Detection Method of Multi-Spectral Pap Smears

Considering the problem of traditional cervical cancer detection method that brings high false negative rate （FNR） and high false positive rate （FPR）, a new abnormal cervical cells detection method of multi-spectral Pap smear is proposed in this thesis, on the basis of multi-spectral microscopic imaging technology and computer automotive recognition technology. At first, image in a specific wave band is segmented according to the relationship between intensity and spectrum of each pixel. Then, multi-spectral features of each pixel are extracted making use of improved cosine correlation analysis （CCA） algorithm. Combined with the characteristic of each cell＇s area, final definition is made. Experiments have proved the new approach could identify abnormal cells efficiently as well as lower FNR and FPR.

Solar Multi-Spectral Radiometric Observations of Atmospheric Optical Thickness over Passarlapudi Gas Well Blow-Out Site in India

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Design of pharmacodynamic monitoring system based on acupuncture differential thermometry

To control drug effects by detecting temperature difference between biologically active point（BAP）and intact area of skin for treatment of mental illness,a device is developed for monitoring the temperature of BAP and the dose medication and its change in real time to increase effectiveness of treatment.Two electrodes by Foll R method are used and BAP is determined based on topographic anatomical reference points.The temperature values are measured by integral thermometers DS18B20.the received data are processed and temperature difference is calculated and displayed under the control of microcontroller Atmega32.The obtained data confirm the correlation between the temperature difference indicators BAP C7,Gi4 and neurological scales assessing severity of mental illness.The experimetal results show that the temperature difference can be criteria for evaluating the effects of drugs,which is the basis for computer control systems of of medical process of mental patients.

Decrepitation Thermometry and Compositions of Fluid Inclusions of the Damoqujia Gold Deposit,Jiaodong Gold Province,China:Implications for Metallogeny and Exploration

The recently discovered Damoqujia （大磨曲家） gold deposit is a large shear zone-hosted gold deposit of disseminated sulphides located in the north of the Zhaoping （招平） fault zone, Jiaodong （胶东） gold province, China. In order to distinguish the temperature range of cluster inclusions from different mineralization stages and measure their compositions, 16 fluid inclusions and 5 isotopic geochemistry samples were collected for this study. Corresponding to different mineralization stages, the multirange peaks of quartz decrepitation temperature （250-270, 310-360 and 380-430℃） indicate that the activity of ore-forming fluids is characterized by multistage. The ore-forming fluids were predominantly of high-temperature fluid system （HTFS） by CO2-rich, and SO4^2--K^＋ type magmatic fluid during the early stage of mineralization and were subsequently affected by low-temperature fluid system （LTFS） of CH4-rich, and Cl^--Na^＋/Ca^2＋ type meteoric fluid during the late stage of mineralization. Gold is transferred by Au-HS^- complex in the HTFS, and Au-Cl^- complex can be more important in the LTFS. The transition of fluids from deeper to shallow environments results in mixing between the HTFS and LTFS, which might be one of the most key reasons for gold precipitation and large-scale mineralization. The ore-forming fluids are characterized by high-temperature, strong-activity, and superimposed mineralization, so that there is a great probability of forming large and rich ore deposit in the Damoqujia gold deposit. The main bodies are preserved and extend toward deeper parts, thereby suggesting a great potential in future.

Effect of arc on radiation thermometry in welding process

The effect of arc on radiation thermometry is analyzed in a field close to the arc during the welding process, and the ratio of signal to noise and other factors are obtained for a small current arc .The method of the temperature measurement is feasible when the arc current is decreased to a smaller value in the welding process.

Multispectral thermometry based on neural network

In order to overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials, a neural network based method is proposed for data processing while a blackbody furnace and three optical filters with known spectral transmittance curves were used to make up a true target. The experimental results show that the calculated temperatures are in good agreement with the temperature of the blackbody furnace, and the calculated spectral emissivity curves are in good agreement with the spectral transmittance curves of the filters. The method proposed has been proved to be an effective method for solving the problem of true temperature and emissivity measurement, and it can overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials.

Using zircon saturation thermometry of source magma in strongly altered volcanic ashes

The present study deals with the possibilities of applying the zircon saturation thermometry, which is based on the equilibrium between the zircon crystals and the melt, to strongly altered volcanic ashes—bentonites. It proposes an alternative to a widely used method of calculating magma temperature from Zr content and major component composition(Boehnke in Chem Geol351:324–333, 2013), that is not suitable for bentonites, as most of the major components have been largely altered in these rocks. For calculating source magma temperatures in strongly altered volcanic ashes, the exponential function from the Zr(ppm)/Al_(2)O_(3)(%) ratio with compositional corrections from the TiO_(2)/Al_(2)O_(3) ratio was found applicable. The idea to use the ratios of these elements is based on the low mobility of these elements in the earth’s surface conditions. Temperatures of magma, forming in the partial melting process, are assessed from the bulk rock composition. Pre-eruption temperatures were estimated from the composition of fine fractions of bentonites. The accuracy of the new method was established from comparison with the method by Boehnke et al.(Chem Geol 351:324–333,2013). The difference between the two methods was mostly less than ± 30° to ± 50°. The comparison with the magma temperature, estimated from the sanidine composition,revealed 13° lower values on average. Although the proposed method for estimating the source magma temperatures is less precise than the method of accounting for detailed rock compositions, it can be used in strongly altered rocks, where other methods are not usable. The new method still enables differentiation between felsic source magmas originating at low or high temperatures. Early Palaeozoic bentonites in the Baltic Basin can be divided,according to the source magma temperatures, into two types:(1) Low temperature(650–790 ℃), containing potassium-rich sanidine and abundant biotite(S type),(2)high temperature(770–850 ℃) with sodium-rich sanidine and scarce biotite(I type).

Methods of data processing in multi-wavelength thermometry

Three kinds of methods for processing the data of the multi-wavelength pyrometer are presented in this paper and are named curve auto-search method, curve auto-regression method and neural network method. Tbe experimental results indicate that the calculated temperature and the spectral emissivity compared with the true target temperature and spectral emissivity have significant deviation using the curve auto-search and the curve auto-regression methods. However, the calculated temperature and the spectral emissivity with higher accuracy can be obtained using the neural network method.

Photon-counting computed tomography thermometry via material decomposition and machine learning

Thermal ablation procedures,such as high intensity focused ultrasound and radiofrequency ablation,are often used to eliminate tumors by minimally invasively heating a focal region.For this task,real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings.Current computed tomography(CT)thermometry is based on energy-integrated CT,tissue-specific experimental data,and linear relationships between attenuation and temperature.In this paper,we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest.In our feasibility study,distilled water,50 mmol/L CaCl2,and 600 mmol/L CaCl2 are chosen as the base materials.Their attenuations are measured in four discrete energy bins at various temperatures.The neural network trained on the experimental data achieves a mean absolute error of 3.97°C and 1.80°C on 300 mmol/L CaCl2 and a milk-based protein shake respectively.These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dis-similar to our base materials.

Exposure to Long Magnetic Resonance Imaging Thermometry Does Not Cause Significant DNA Double-Strand Breaks on CF-1 Mice

The purpose of the study was to investigate if the high gradient strength and slew rate used for long MRI-thermometry monitoring could cause DNA double-stranded breaks (DSBs). To this end, an enzyme-linked immunosorbent assay (ELISA) was used to quantify γH2AX, a molecular marker for DSBs, in the blood of mice after a 6-hour exposure to magnetic resonance imaging (MRI). Fourteen CF-1 female mice were separated into 4 experimental groups: Untreated negative control, MRI-treated, MRI-Control, and exposed to ionizing radiation positive control. Untreated negative control was used as a baseline for ELISA to quantify γH2AX. MRI-treated consisted of a 6-hour continuous magnetic resonance imaging (MRI) echo planar imaging (EPI) sequence with a slew rate of 192 mT/m/s constituting a significantly longer imaging time than routine clinical imaging. MRI-control mice were maintained under the same conditions outside the MRI scanner for 6-hours. Mice in the irradiation group served as a positive control of DSBs and were exposed to either 2 Gy, 5 Gy or 10 Gy of ionizing radiation. DSBs in the blood lymphocytes from the treatment groups were analyzed using the γH2AX ELISA and compared. Total protein concentration in lysates was determined for each blood sample and averaged 1 ± 0.35 mg/mL. Irradiated positive controls were used to test radiation dose-dependency of the γH2AX ELISA assay where a linear dependency on radiation exposure was observed (r2 = 0.93) between untreated and irradiated samples. Mean and standard error mean of γH2AX formation were calculated and compared between each treatment group. Repeated measures 1-way ANOVA showed statistically significant differences between the means of irradiated controls and both the MRI-control and MRI-treated groups. There was no statistically significant difference between the MRI-treated samples and the MRI-control groups. Our results show that long MRI exposure at a high slew rate did not cause increased levels of γH2AX when compared to control mice, suggesting that no increase in DSBs was caused by the long MR thermometry imaging session. The novelty of this work contradicts other studies that have suggested MRI may cause DSBs;this work suggests an alternative cause of DNA damage.

红外阵列成像精确测温及温度补偿

针对现有红外热像测温存在精度低、成本高等问题,利用MLX90640搭建了红外阵列成像精确测温系统,建立了改进最小二乘曲线拟合的温度补偿模型提高测温精度。首先,通过红外阵列探测器采集物体温度信息,同时,采用改进双线性插值算法对温度数据进行扩充,增强红外图像细节特征显示。然后,利用BR125黑体校准源进行30~100℃的系统温度校准标定,通过采集不同设定距离下多种温度测试数据,对多种不同曲线拟合模型的拟合程度分析,进行温度误差拟合曲线修正优化,建立温度修正优化模型,实现了在不同距离下对红外测温估计值的精准预测。实验测试结果表明,所提修正模型在进行优化补偿后最大温度误差不超过1.0℃,平均温度误差从5.70℃减少到0.24℃,使得测温精度大幅提升。为了评估实验结果的偏差程度,进行了不确定度分析,平均标准偏差为0.848℃,达到了实验预期结果,验证了该方法测温的精确性及所提模型的可靠性。该系统实现了对物体温度快速精确测量,可实时观测出区域内物体温度分布情况,具有广泛的应用场景。