Classification of Real-World Objects Using Supervised ML-Assisted Polarimetry: Cost/Benefit Analysis

We study the problem of classification of various real-world objects using as input a database (DB) of laboratory polarimetric measures (Mueller matrix elements—MMEs). It can work as a complementary technology of surroundings’ imaging that can be used, in particular, in autonomous driving. To this end, we look for an algorithm using less input parameters without great loss of the quality of classification. We start by analyzing the data in order to understand the attributes that are more important for associating the objects with one of several predefined classes. Different sets of attributes are studied using an artificial neural network (ANN), which is optimized in terms of the number of hidden layers and the activation function. After that, an improved machine learning (ML) architecture is built using the K-nearest neighbors (KNN) classifier on each cluster generated by applying the pre-trained ANN to the training set. This article focuses on the situation wherein one may not be able to measure all MMEs or it would be too expensive or challenging to implement when the measurement time is crucial. The results obtained for a reduced set of attributes using different ML architectures are very good, especially for the proposed combined ANN-KNN approach (wherein the ANN acts as a predictor and KNN as a corrector), which can help to avoid measuring all MMEs.