Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

The compact muon solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the large hadron collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present semi-supervised spatio-temporal anomaly detection (AD) monitoring for the physics particle reading channels of the hadronic calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector, and global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We have validated the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC Run-2 collision data sets. The GraphSTAD system has achieved production-level accuracy and is being integrated into the CMS core production system--for real-time monitoring of the HCAL. We have also provided a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.

Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification

Hyperspectral Image Classification (HSIC) is a difficult task due to high inter and intra-class similarity and variability, nested regions, and overlapping. 2D Convolutional Neural Networks (CNN) emerged as a viable network whereas, 3D CNNs are a better alternative due to accurate classification. However, 3D CNNs are highly computationally complex due to their volume and spectral dimensions. Moreover, down-sampling and hierarchical filtering (high frequency) i.e., texture features need to be smoothed during the forward pass which is crucial for accurate HSIC. Furthermore, CNN requires tons of tuning parameters which increases the training time. Therefore, to overcome the aforesaid issues, Sharpened Cosine Similarity (SCS) concept as an alternative to convolutions in a Neural Network for HSIC is introduced. SCS is exceptionally parameter efficient due to skipping the non-linear activation layers, normalization, and dropout after the SCS layer. Use of MaxAbsPool instead of MaxPool which selects the element with the highest magnitude of activity, even if it's negative. Experimental results on publicly available HSI datasets proved the performance of SCS as compared to the convolutions in Neural Networks.

Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification

Convolutional Neural Networks (CNN) are more suitable, indeed. However, fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. The convolution of different kernel size networks may overcome this problem by capturing more discriminating and relevant information. In light of this, the proposed solution aims at combining the core idea of 3D and 2D Inception net with the Attention mechanism to boost the HSIC CNN performance in a hybrid scenario. The resulting \textit{attention-fused hybrid network} (AfNet) is based on three attention-fused parallel hybrid sub-nets with different kernels in each block repeatedly using high-level features to enhance the final ground-truth maps. In short, AfNet is able to selectively filter out the discriminative features critical for classification. Several tests on HSI datasets provided competitive results for AfNet compared to state-of-the-art models. The proposed pipeline achieved, indeed, an overall accuracy of 97\% for the Indian Pines, 100\% for Botswana, 99\% for Pavia University, Pavia Center, and Salinas datasets.

3D/2D regularized CNN feature hierarchy for Hyperspectral image classification

Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) and are known to be effective in exploiting joint spatial-spectral information with the expense of lower generalization performance and learning speed due to the hard labels and non-uniform distribution over labels. Several regularization techniques have been used to overcome the aforesaid issues. However, sometimes models learn to predict the samples extremely confidently which is not good from a generalization point of view. Therefore, this paper proposed an idea to enhance the generalization performance of a hybrid CNN for HSIC using soft labels that are a weighted average of the hard labels and uniform distribution over ground labels. The proposed method helps to prevent CNN from becoming over-confident. We empirically show that in improving generalization performance, label smoothing also improves model calibration which significantly improves beam-search. Several publicly available Hyperspectral datasets are used to validate the experimental evaluation which reveals improved generalization performance, statistical significance, and computational complexity as compared to the state-of-the-art models. The code will be made available at https://github.com/mahmad00.

Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN

Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images. However, 2D CNN only considers the spatial information and ignores the spectral information whereas 3D CNN jointly exploits spatial-spectral information at a high computational cost. Therefore, this work proposed a lightweight CNN (3D followed by 2D-CNN) model which significantly reduces the computational cost by distributing spatial-spectral feature extraction across a lighter model alongside a preprocessing that has been carried out to improve the classification results. Five benchmark Hyperspectral datasets (i.e., SalinasA, Salinas, Indian Pines, Pavia University, Pavia Center, and Botswana) are used for experimental evaluation. The experimental results show that the proposed pipeline outperformed in terms of generalization performance, statistical significance, and computational complexity, as compared to the state-of-the-art 2D/3D CNN models except commonly used computationally expensive design choices.

Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, deep learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.

Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image Classification

Traditional Active/Self/Interactive Learning for Hyperspectral Image Classification (HSIC) increases the size of the training set without considering the class scatters and randomness among the existing and new samples. Second, very limited research has been carried out on joint spectral-spatial information and finally, a minor but still worth mentioning is the stopping criteria which not being much considered by the community. Therefore, this work proposes a novel fuzziness-based spatial-spectral within and between for both local and global class discriminant information preserving (FLG) method. We first investigate a spatial prior fuzziness-based misclassified sample information. We then compute the total local and global for both within and between class information and formulate it in a fine-grained manner. Later this information is fed to a discriminative objective function to query the heterogeneous samples which eliminate the randomness among the training samples. Experimental results on benchmark HSI datasets demonstrate the effectiveness of the FLG method on Generative, Extreme Learning Machine and Sparse Multinomial Logistic Regression (SMLR)-LORSAL classifiers.

A Fast 3D CNN for Hyperspectral Image Classification

Hyperspectral imaging (HSI) has been extensively utilized for a number of real-world applications. HSI classification (HSIC) is a challenging task due to high inter-class similarity, high intra-class variability, overlapping, and nested regions. A 2D Convolutional Neural Network (CNN) is a viable approach whereby HSIC highly depends on both Spectral-Spatial information, therefore, 3D CNN can be an alternative but highly computational complex due to the volume and spectral dimensions. Furthermore, these models do not extract quality feature maps and may underperform over the regions having similar textures. Therefore, this work proposed a 3D CNN model that utilizes both spatial-spectral feature maps to attain good performance. In order to achieve the said performance, the HSI cube is first divided into small overlapping 3D patches. Later these patches are processed to generate 3D feature maps using a 3D kernel function over multiple contiguous bands that persevere the spectral information as well. Benchmark HSI datasets (Pavia University, Salinas and Indian Pines) are considered to validate the performance of our proposed method. The results are further compared with several state-of-the-art methods.

Efficient Data Analytics on Augmented Similarity Triplets

Many machine learning methods (classification, clustering, etc.) start with a known kernel that provides similarity or distance measure between two objects. Recent work has extended this to situations where the information about objects is limited to comparisons of distances between three objects (triplets). Humans find the comparison task much easier than the estimation of absolute similarities, so this kind of data can be easily obtained using crowd-sourcing. In this work, we give an efficient method of augmenting the triplets data, by utilizing additional implicit information inferred from the existing data. Triplets augmentation improves the quality of kernel-based and kernel-free data analytics tasks. Secondly, we also propose a novel set of algorithms for common supervised and unsupervised machine learning tasks based on triplets. These methods work directly with triplets, avoiding kernel evaluations. Experimental evaluation on real and synthetic datasets shows that our methods are more accurate than the current best-known techniques.

Unsupervised adulterated red-chili pepper content transformation for hyperspectral classification

Preserving red-chili quality is of utmost importance in which the authorities demand the quality techniques to detect, classify and prevent it from the impurities. For example, salt, wheat flour, wheat bran, and rice bran contamination in grounded red chili, which typically a food, are a serious threat to people who are allergic to such items. This work presents the feasibility of utilizing visible and near-infrared (VNIR) hyperspectral imaging (HSI) to detect and classify the aforementioned adulterants in red chili. However, adulterated red chili data annotation is a big challenge for classification because the acquisition of labeled data for real-time supervised learning is expensive in terms of cost and time. Therefore, this study, for the very first time proposes a novel approach to annotate the red chili samples using a clustering mechanism at 500~nm wavelength spectral response due to its dark appearance at a specified wavelength. Later the spectral samples are classified into pure or adulterated using one-class SVM. The classification performance achieves 99% in case of pure adulterants or red chili whereas 85% for adulterated samples. We further investigate that the single classification model is enough to detect any foreign substance in red chili pepper rather than cascading multiple PLS regression models.