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高光谱图像处理

Development of a classification algorithm for efficient handling of multiple classes in sorting systems basesd on hyperspectral imaging

基于高光谱成像的分类算法在分拣系统中高效处理多类目的开发
原文链接

Abstruct

When dealing with practical applications of hyperspectral imaging, the development of efficient, fast and flexible classification algorithms*/'ælgərɪð(ə)mz/* is of the utmost importance.
在处理高光谱图像的实际应用时,开发高效、快速和灵活的分类算法是极其重要的。
Indeed, the optimal classification method should be able, in a reasonable time, to maximise the separation between the classes of interest and, at the same time, to correctly reject possible outlier samples.
实际上,最佳的分类方法应该能够在合理的时间去增加兴趣类别之间的距离,并且同时正确地排除可能的异常样本。
To this aim, a new extension of Partial Least Squares Discriminant Analysis (PLS-DA), namely Soft PLS-DA, has been implemented.
为此,已实施了偏最小二乘分析(PLS-DA)的新扩展,即Soft PLS-DA。
The basic engine of Soft PLS-DA is the same as PLS-DA, but class assignment is subjected to some additional criteria which allow samples not belonging to the target classes to be identified and rejected.
Soft PLS-DA的基本引擎和PLS-DA相同,但是类别分配要遵循一些附加的条件,从而辨别和排除一些不属于目标分类的样本。
The proposed approach was tested on a real case study of plastic waste sorting based on near infrared hyperspectral imaging.
在基于近红外高光谱图像的塑料垃圾分类的实例研究中对提出的方法进行了测试。
Household plastic waste objects made of the six recyclable plastic polymers/'pɔliməs/ commonly used for packaging were collected and imaged using a hyperspectral camera mounted on an industrial sorting system.
使用安装在工业分类系统上的高光谱相机收集由六种常用于包装的可回收塑料聚合物制成的家用塑料废品,并对其成像。
In addition, paper and not recyclable plastics were also considered as potential foreign materials that are commonly found in plastic waste.
此外,纸和不可回收的塑料也被视为潜在的异物,通常在塑料废料中发现。
For classification purposes, the Soft PLS-DA algorithm was integrated into a hierarchical classification tree for the discrimination of the different plastic polymers.
出于分类的目的,将Soft PLS-DA算法集成到用于区分不同塑料聚合物的分层分类树中。
Furthermore, Soft PLS-DA was also coupled with sparse-based variable selection to identify the relevant variables involved in the classification and to speed up the sorting process.
此外,Soft PLS-DA还与基于稀疏的变量选择相结合,以识别分类中涉及的相关变量并加快分类过程。
The tree-structured classification model was successfully validated both on a test set of representative spectral of each material for a quantitative evaluation, and at the pixel level on a set of hyperspectral images for a qualitative assessment.
树状分类模型已在每种材料的代表性光谱测试集上成功进行了验证,以进行定量评估,并在一组高光谱图像的像素级上进行了定性评价。

Keywords: PLS-DA, multivariate classification, hierarchical /ˌhaɪəˈrɑːkɪkl/ classification, sparse methods, feature selection, plastic*/ˈplæstɪk/* sorting
关键词: PLS-DA ,多元分类 ,层次分类, 稀疏方法 ,特征选择, 塑性排序

Introduction

Over the­ past­ decades, ­Hyperspectral ­Imaging­(HSI)­ has­ gained increasing­ attention­ from ­industries ­interested­ in­ the ­implementation­ of automated­ sorting systems ­to­ solve­ a­n umber­ of ­different ­problems.­
在过去的几十年里,高光谱成像(HSI)越来越受到工业界的关注,他们对自动分拣系统的实现感兴趣,想要解决许多不同的问题。

Indeed,­HSI­ has­ found ­a­wide­ range­ of­ applications ­in­ the ­food­ industry,­ including ­the ­quality ­evaluation ­and­ safety­ assessment of several food products ,such as fruits and vegetables,meat,cereals and dairy products.
实际上,高光谱成像技术在食品工业中已经得到了广泛的应用,包括一些食品的质量评估和安全评价,比如水果和蔬菜,肉类,谷物和奶制品。

Moreover, other manufacturing environments, such as the pharmaceutical/ˌfɑːməˈsuːtɪkl/ industry , have employed real-time HSI systems for quality control and process monitoring in the frame of the process analytical technology.
此外,其他制造环境,比如制药行业已经使用了框架内过程分析技术中的高光谱成像系统的质量控制和过程监督。

Another relevant field of application of HSI is represented by the recycling industry, where hyperspectral sensors are used to separate end-of-life objects,such as plastic,paper or electronic waste, according to material type.
HIS的另一个相关应用领域是回收行业,根据材料类型,高光谱传感器用于分离报废对象,例如塑料,纸张或电子垃圾。

In these contexts, HSI can be considered as a step forward with respect to traditional spectroscopic/,spektrə’skɔpik/ techniques, which allow fast and non-destructive characterisation of the chemical properties of the analysed samples.
在这些情况下,相对于传统的光谱技术,HSI可以看作是向前迈出的一步,传统的光谱技术可以对分析样品的化学性质进行快速且无损的表征。

In fact, HSI systems couple these advantages with the possibility of also visualising the spatial distribution of the chemical features of interest within the sample surface.
事实上,高光谱成像系统将这些优势与还可以可视化样品表面内目标化学特征的空间分布相结合。

Furthermore, in sorting systems, HSI can also be employed to quickly identify the chemical composition of homogeneous objects moving on a conveyor belt, and to distinguish them from samples with different composition.
此外,在分拣系统中,高光谱成像还可以用于快速识别在传送带上移动的均质物体的化学成分,并将其与具有不同成分的样品区分开。

In practical situations,hyperspectral imaging can be applied to address complex classification issues,where the sorting problem under investigation requires the discrimination of several classes at the same time, with some classes sharing similar features.
在实际情况下,高光谱成像技术能够应用于解决复杂的分类问题,所研究的排序问题需要同时区分多个类,其中有些类具有相似的特征。

This can be easily managed by using HSI systems, since with a single measurement, i.e. with the acquisition of a single hyperspectral image, it is possible to have a wide range of information.
使用高光谱成像系统可以很容易地做到这一点,因为通过单个测量,即通过获取单个高光谱图像,可以获得广泛的信息。

However, in order to meet the needs of real-time applications, it is necessary to identify classification strategies able to handle a huge amount of spectral data,providing reliable results in short computational times.
然而,为了满足实时应用的需要,有必要识别能够处理大量光谱数据的分类策略,在短计算时间内提供可靠的结果。

When dealing with multiple classes, this issue can be addressed using a tree-structured classification model,where each branching (tree node) corresponds to a local classification model.
在处理多个类时,可以使用树结构分类模型来解决这个问题,其中每个分支(树节点)对应一个本地分类模型。

In this manner, classification is performed considering a top-down approach, where the samples are initially assigned to general macro-categories, and then each macro-class is split into increasingly specific categories,until reaching the classes of interest.
通过这种方式,采用自顶向下的方法来执行分类,在这种方法中,最初将样本分配给一般的宏观类别,然后将每个宏观类别划分为越来越具体的类别,直到达到感兴趣的类别。

Another relevant issue to be faced in practical applications of HSI in sorting systems is related to the fact that,generally, it is not easy to have a strict control of the input stream in order to avoid the presence of foreign objects, i.e. objects not belonging to the target classes of the specific application.
在分类系统中HSI的实际应用中要面对的另一个相关问题是,通常很难对输入流进行严格控制以避免异物的存在,即不属于物体 特定应用程序的目标类。

In this context, the availability of algorithms able to maximise the discrimination between the categories of interest and, at the same time, to identify possible foreign materials is of the utmost importance.
在这种情况下,最重要的是能够获得能够最大程度地区分所关注类别之间的区别并同时识别可能的异物的算法。

Partial Least Squares Discriminant Analysis (PLS-DA) is one of the most widely used methods for multivariate classification of hyperspectral data.
偏最小二乘判别分析(PLS-DA)是对高光谱数据多元分类最广泛的使用方法之一。

Basically, PLS-DA is an extension of the PLS algorithm, which aims at identifying a new set of variables, named Latent Variables
(LVs), by maximising the between-classes variance.
基本上,PLS-DA是PLS算法的一种延伸,该算法旨在辨别一组称为 潜在变量(LVs)的新变量,通过最大化类间差异。

Class membership is coded using a dummy Y matrix, and the assignment of unknown samples is based on the a posteriori probability associated with the corresponding Y predicted values.
使用虚拟Y矩阵对类成员资格进行编码,未知样本的分配基于与相应的Y预测值相关的后验概率。

The standard PLS-DA approach assigns a sample to the class for which it has the higher a posteriori probability, resulting in unknown samples always being assigned to one of the target classes.
标准的PLS-DA方法将样本分配给后验概率较高的类别,从而导致始终将未知样本分配给目标类别之一。

Conversely, the possibility of having unassigned samples is one of the major advantages of the so-called class-modelling techniques, which are essentially based on describing each single class independently from the others, and then verifying whether an unknown sample is compliant or not with the characteristics of each class of interest.
相反,拥有未分配样品的可能性是所谓的类建模技术的主要优势之一,该技术基本上基于彼此独立地描述每个单个类,然后验证未知样品是否兼容,或每个不感兴趣类别的特征。

In this manner, it is possible that a new unknown sample is rejected from all the class models, resulting in an unassigned sample.
以此方式,有可能所有类模型都拒绝了一个新的未知样本,从而导致未分配样本。

Soft Independent Modelling of Class Analogy /əˈnælədʒi/(SIMCA) is the most common class-modelling method.
类比的软独立建模(SIMCA)是最常见的类建模方法。

It calculates local Principal Component Analysis (PCA) models for each considered class, which are used to define class boundaries based on the distances both in the score space (Hotelling’s T2) and in the residual/rɪ’zɪdjʊə/ space (Qresiduals).
它为每个要考虑的类别计算局部主成分分析(PCA)模型,该模型用于根据分数空间(Hotelling T2)和残差空间(Qresiduals)中的距离来定义类别边界。

Notwithstanding the advantages of class-modelling methods like SIMCA,they can provide poor classification results when the modelled classes are quite overlapped, since the model is not oriented towards the discrimination of the considered categories.
尽管像SIMCA这样的类建模方法有很多优点,但是当建模的类完全重叠时,它们会提供较差的分类结果,这是因为该模型并不针对所考虑类别的区分。

Given these considerations, it is reasonable to assume that a classification algorithm to be efficiently employed in sorting systems should comprise the advantages of both classification techniques and of class-modelling methods, i.e. it should be able to maximise the discrimination between the categories of interest and to recognise and reject outlier samples at the same time.
考虑到这些考虑因素,可以合理地假设要在分类系统中有效使用的分类算法应同时包括分类技术和类建模方法的优点,即应能够最大程度地区分兴趣类别之间的区别, 同时识别和拒绝异常样本。

To this aim, in the present paper a modified version of the PLS-DA algorithm, namely Soft PLS-DA, is proposed.
为此,在本文中提出了PLS-DA算法的改进版本,即Soft PLS-DA。

The basic principle of Soft PLS-DA is the same as PLS-DA, but class assignment is performed by fixing additional limits both on the Y predicted values and on the Q residuals.
Soft PLS-DA的基本原理与PLS-DA相同,但是通过在Y预测值和Q残差的附加限制来执行类分配。

In this manner, the classification model is built by maximising the differences between the modelled classes; at the same time, the additional limits allow the rejection of samples belonging to unexpected categories and relegation /,reli’geiʃən/of them to a general category of unassigned samples.
通过这种方式,通过最大化建模类之间的差异来构建分类模型。 同时,附加限制排除属于意外类别的样本,并将其降级为未分配样本的一般类别。

The effectiveness of Soft PLS-

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