熵平均密度分段压缩感知反射光谱重建方法

反射光谱作为物体表面重要特征被广泛用于远程遥感目标识别、物质成分的含量检测、农业作物成熟度检测、医学影像的疾病诊断等领域。为解决反射光谱数据冗余,实现全光谱数据稀疏表达和提高光谱重建精度,将压缩感知技术应用于光谱数据分析和处理。针对全局光谱压缩感知重建方法中各波段数据稀疏度的差异性对采样率的限制条件不同,提出熵平均密度分段压缩感知反射光谱重建方法。首先定义熵平均密度作为光谱分段参考量来寻找光谱分段断点和判定各分段光谱的熵密度高低。而后依据有限等距约束条件重新分配各分段光谱的采样率,生成测量矩阵和稀疏矩阵完成各局部反射光谱稀疏感知。采用正交匹配追踪算法求最优解,分配各分段光谱的迭代次数,用感知矩阵中的列原子和稀疏信号进行迭代匹配重构各局部反射光谱,将各重构的局部反射光谱缝合为全局反射光谱。用全局光谱压缩感知方法和该方法对标准色块24Munsell ColorChecker的反射光谱进行对比实验。实验结果表明,较之于全局光谱压缩感知方法,该方法重建光谱曲线高熵密度区重建精度更高,低熵密度区压缩效率更高,在总压缩采样率不变的情况下,RMSE和MAPE统计数据得到改善,提升了整体曲线重建效果。

High Temperature Spectrum for v3 Band of Carbon Dioxide

The total internal partition sums （TIPS） are calculated at the temperature up to 6000 K for 12 C16 02. Using the calculated partition functions, we produce the line intensities of v3 band of 12C1602 at several high temperatures. The results show that the calculated line intensities are in very good agreement with those of HITRAN database at the temperature up to 3000 K, which provides a strong support for the calculations of TIPS and line intensities at high temperature. Then the calculation is extended to further high temperature, and the simulated spectra of u3 band of 12C1602 at 5000 and 6000 K are reported.

Feature spectrum extraction of human fingernails based on LCTF multispectral imaging

A multispectral imaging system consisting of liquid crystal tunable filter(LCTF)and charge coupled device(CCD)camera was used to collect the images of fingernail samples at intervals of 10 nm during the spectral range of 450-1 000 nm,and a multispectral image of human fingernails containing 56 bands was obtained.The accurate reflectivity information of fingernails was obtained through referring whiteboard comparative measurement method.Principal component analysis(PCA)and band index method were used to reduce the dimension of the sample images respectively and two feature spaces were obtained.Spectral angle mapping(SAM)was used to classify human fingernails in these two feature spaces.The classification accuracy were above 92.5%and 82.9%respectively.Therefore,the feature space obtained by the PCA can be used as the characteristic spectrum of human fingernails,which provides a reliable basis for the analysis of multispectral spectrum of fingernails and human health assessment in the future.

Response of fuzzy clustering on different threshold determination algorithms in spectral change vector analysis over Western Himalaya, India

Abstract： Change detection is a standard tool to extract and analyze the earth＇s surface features from remotely sensed data. Among the different change detection techniques, change vector analysis （CVA） have an exceptional advantage of discriminating change in terms of change magnitude and vector direction from multispectral bands. The estimation of precise threshold is one of the most crucial task in CVA to separate the change pixels from unchanged pixels because overall assessment of change detection method is highly dependent on selected threshold value. In recent years, integration of fuzzy clustering and remotely sensed data have become appropriate and realistic choice for change detection applications. The novelty of the proposed model lies within use of fuzzy maximum likelihood classification （FMLC） as fuzzy clustering in CVA. The FMLC based CVA is implemented using diverse threshold determination algorithms such as double-window flexible pace search （DFPS）, interactive trial and error （T＆E）, and 3x3-pixel kernel window （PKW）. Unlike existing CVA techniques, addition of fuzzy clustering in CVA permits each pixel to have multiple class categories and offers ease in threshold determination process. In present work, the comparative analysis has highlighted the performance of FMLC based CVA overimproved SCVA both in terms of accuracy assessment and operational complexity. Among all the examined threshold searching algorithms, FMLC based CVA using DFPS algorithm is found to be the most efficient method.

Classification of Barley according to Harvest Year and Species by Using Mid-infrared Spectroscopy and Multivariate Analysis

In order to monitor malt quality in the malting industry, despite yearly variations in the barley quality, 394 barley samples were analysed using conventional （moisture, protein and B-glucan content） and mid-infrared Fourier transform spectroscopy FT-IR. The experimental dataset included barley from three harvest years, two barley species, 77 barley varieties, and two-row and six-row barley, from 16 cultivation sites. For each sample, the malt quality indices were also assessed according to European Brewing Convention （EBC） standards. Principal component analysis （PCA） was carried out on mean-centred, normalized and derivative spectra using 200/cm width spectral bands. The most informative spectral bands were observed in the 800-1,000/cm and 1,000-1,200/cm ranges. PCA revealed that barley harvested in 2010 and in 2011 had bands that were very close together, while 2009 harvest clearly displayed a difference in its quality. PCA made it possible to distinguish two species and confirmed that two-row winter barley quality was closer to two-row spring barley quality than to six-row winter barley. Results indicate that mid-infrared spectrometry （MIR） could be a very useful and rapid analytical tool to assess barley qualitative quality.