A robust dynamic classifier selection approach for hyperspectral images with imprecise label information

Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors in data labels. Despite the growing importance of this aspect, it has not been sufficiently studied in the literature yet. In this paper, we analyze the effect of erroneous sample labels on probability distributions of the principal components of HSIs, and provide in this way a statistical analysis of the resulting uncertainty in classifiers. Building on the theory of imprecise probabilities, we develop a novel robust dynamic classifier selection (R-DCS) model for data classification with erroneous labels. Particularly, spectral and spatial features are extracted from HSIs to construct two individual classifiers for the dynamic selection, respectively. The proposed R-DCS model is based on the robustness of the classifiers’ predictions: the extent to which a classifier can be altered without changing its prediction. We provide three possible selection strategies for the proposed model with different computational complexities and apply them on three benchmark data sets. Experimental results demonstrate that the proposed model outperforms the individual classifiers it selects from and is more robust to errors in labels compared to widely adopted approaches

Signal and image processing methods for imaging mass spectrometry data

Imaging mass spectrometry (IMS) has evolved as an analytical tool for many biomedical applications. This thesis focuses on algorithms for the analysis of IMS data produced by matrix assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometer. IMS provides mass spectra acquired at a grid of spatial points that can be represented as hyperspectral data or a so-called datacube. Analysis of this large and complex data requires efficient computational methods for matrix factorization and for spatial segmentation. In this thesis, state of the art processing methods are reviewed, compared and improved versions are proposed. Mathematical models for peak shapes are reviewed and evaluated. A simulation model for MALDI-TOF is studied, expanded and developed into a simulator for 2D or 3D MALDI-TOF-IMS data. The simulation approach paves way to statistical evaluation of algorithms for analysis of IMS data by providing a gold standard dataset. [...

Multivariate analysis of Brillouin imaging data by supervised and unsupervised learning

Brillouin imaging relies on the reliable extraction of subtle spectral information from hyperspectral datasets. To date, the mainstream practice has been using line fitting of spectral features to retrieve the average peak shift and linewidth parameters. Good results, however, depend heavily on sufficient SNR and may not be applicable in complex samples that consist of spectral mixtures. In this work, we thus propose the use of various multivariate algorithms that can be used to perform supervised or unsupervised analysis of the hyperspectral data, with which we explore advanced image analysis applications, namely unmixing, classification and segmentation in a phantom and live cells. The resulting images are shown to provide more contrast and detail, and obtained on a timescale $10^2$ faster than fitting. The estimated spectral parameters are consistent with those calculated from pure fitting

Spectral-Spatial Classification of Hyperspectral Data based on a Stochastic Minimum Spanning Forest Approach

International audienceIn this paper, a new method for supervised hyperspectral data classification is proposed. In particular, the notion of stochastic Minimum Spanning Forest (MSF) is introduced. For a given hyperspectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of Minimum Spanning Forests. The next step consists in building an MSF from each of the M marker maps. Finally, all the M realizations are aggregated with a maximum vote decision rule, in order to build the final classification map. The proposed method is tested on three different data sets of hyperspectral airborne images with different resolutions and contexts. The influence of the number of markers and of the number of realizations M on the results are investigated in experiments. The performance of the proposed method is compared to several classification techniques (both pixelwise and spectral-spatial) using standard quantitative criteria and visual qualitative evaluation

DNN-based PolSAR image classification on noisy labels

Deep neural networks (DNNs) appear to be a solution for the classification of polarimetric synthetic aperture radar (PolSAR) data in that they outperform classical supervised classifiers under the condition of sufficient training samples. The design of a classifier is challenging because DNNs can easily overfit due to limited remote sensing training samples and unavoidable noisy labels. In this article, a softmax loss strategy with antinoise capability, namely, the probability-aware sample grading strategy (PASGS), is developed to overcome this limitation. Combined with the proposed softmax loss strategy, two classical DNN-based classifiers are implemented to perform PolSAR image classification to demonstrate its effectiveness. In this framework, the difference distribution implicitly reflects the probability that a training sample is clean, and clean labels can be distinguished from noisy labels according to the method of probability statistics. Then, this probability is employed to reweight the corresponding loss of each training sample during the training process to locate the noisy data and to prevent participation in the loss calculation of the neural network. As the number of training iterations increases, the condition of the probability statistics of the noisy labels will be constantly adjusted without supervision, and the clean labels will eventually be identified to train the neural network. Experiments on three PolSAR datasets with two DNN-based methods also demonstrate that the proposed method is superior to state-of-the-art methods.This work was supported in part by the National Natural Science Foundation of China under Grant 61871413 and Grant 61801015, in part by the Fundamental Research Funds for the Central Universities under Grant XK2020-03, in part by China Scholarship Council under Grant 2020006880033, and in part by Grant PID2020-114623RB-C32 funded by MCIN/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

Feature extraction and segmentation of hyperspectral images

This work proposes an approach for hyperspectral images segmentation without direct application of common clustering methods on the hyperspectral data. The proposed approach reduces the spectral dimension of the image, through principal component analysis, and its spatial dimension, through wavelet transform, in order to apply the clustering algorithm on a lower resolution version of the data and then train a classifier to label the high resolution image