基于多特征光谱的番茄分选算法研究

为了实现番茄成熟度的自动分选,基于番茄可见-近红外波长的多特征光谱,设计一种环境光减除、双信号标记(Dual Signal Marking,DSM)的番茄分选算法。采用均值滤波、环境光减除的方法,有效消除实际干扰对信号的影响。通过研究番茄漫反射的光谱特征,DSM选用520 nm、620 nm、850 nm三种波长作为特征信息。利用TensorFlow分别建立双信号波长520 nm、620 nm的阈值函数φ(x)、γ(x)。将检测信号与所设函数阈值比较,使用标记器获取合格信号占总体信号的比例,从而判断番茄的成熟度。在工厂、农田现场等环境下进行测试。实验结果表明,该算法实现了对番茄成熟度的多档判断,平均识别率大于94. 0%,具有较强的抗干扰能力。

利用概率融合光谱-空间特征地物分类模型对高分影像地物进行提取

本文提出了一个联合光谱-空间多特征的基于支持向量机的分类器模型,首先将三类光谱-空间特征利用支持向量机对高分影像进行分类,然后将分类结果利用概率融合的方法进行整合,最终完成了地物的提取。试验结果显示,相比于VS-SVM算法,该模型取得了更好的提取效果。

基于FPGA+DSP的高速多光谱复现系统研究

为了在高速复现多光谱信息的同时,仍能保证对每个光谱复现的精度,提高系统的稳定性,研究了一种基于FPGA+DSP的复现系统。系统利用FPGA对高速AD进行控制,然后将携带不同特征波长信息的干涉条纹传输给DSP进行光谱复现。在DSP中,对不同中心波长的干涉信号采用非均匀插值处理,再通过NUFFT实现分段频域变换处理,进而达到等精度复现每个光谱的效果。实验将Virtex系列FPGA与6745型DSP联用构成处理模块,对660 nm、780 nm和808 nm三个激光同时入射静态干涉模块的干涉条纹进行处理,再完成混合光的光谱复现。结果显示,采用NUFFT分段处理的方式相比单一采样插值法复现的光谱分布效果更好,其三个特征波长信噪比都较高,并且速度比原有方法快近一倍。由此可见,该系统在多光谱数据复现应用中更具优势。

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.

Analysis of rice paper's morphological features based on multispectral imaging technology

Computer forensics and identification for traditional Chinese painting arts have caught the attention of various fields. Rice paper＇s feature extraction and analysis methods are of high significance for the rice paper is an important carrier of traditional Chinese painting arts. In this paper, rice paper＇s morphological feature analysis is done using multi spectral imaging technology. The multispectral imaging system is utilized to acquire rice paper＇s spectral images in different wave- length channels, and then those spectral images are measured using texture parameter statistics to acquire sensitive bands for rice paper＇s feature. The mathematical morphology and grayscale statistical principle are utilized to establish a rice paper＇s morphological feature analytical model which is used to acquire rice paper＇ s one-dimensional vector. For the purpose of eval- uating these feature vectors＇ accuracy, they are entered into the support vector machine（SVM） classifier for detection and classification. The results show that the rice paper＇s feature is out loud in the spectral band 550 nm, and the average classifi- cation accuracy of feature vectors output from the analytical model is 96 %. The results indicate that the rice paper＇s feature analytical model can extract most of rice paper＇s features with accuracy and efficiency.

PROJECTION BASED STATISTICAL FEATURE EXTRACTION WITH MULTISPECTRAL IMAGES AND ITS APPLICATIONS ON THE YELLOW RIVER MAINSTREAM LINE DETECTION

Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification;then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained.Experiments are performed to verify the applicability of the algorithm.The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection.

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.