Ram Narayan Patro

Certifying whether an arbitrary quantum system is entangled or not, is, in general, an NP-hard problem. Though various necessary and sufficient conditions have already been explored in this regard for lower-dimensional systems, it is hard to extend them to higher dimensions. Recently, an ensemble bagging and convex hull approximation (CHA) approach (together, BCHA) was proposed and it strongly suggests employing a machine learning technique for the separability-entanglement classification problem. However, BCHA does only incorporate the balanced dataset for classification tasks which results in lower average accuracy. In order to solve the data imbalance problem in the present literature, an exploration of the boosting technique has been carried out, and a trade-off between the boosting and bagging-based ensemble classifier is explored for quantum separability problems. For the two-qubit and two-qutrit quantum systems, the pros and cons of the proposed random under-sampling boost CHA (RUSBCHA) for the quantum separability problem are compared with the state-of-the-art CHA and BCHA approaches. As the data are highly unbalanced, performance measures such as overall accuracy, average accuracy, F-measure, and G-mean are evaluated for a fair comparison. The outcomes suggest that RUSBCHA is an alternative to the BCHA approach. Also, for several cases, performance improvements are observed for RUSBCHA since the data are imbalanced.

Hyperspectral imaging is a key tool in numerous remote sensing applications. Images with tens or even hundreds of spectral bands contain a wealth of information to retrieve multiple geophysical parameters, to perform target detection, or land classification with unbeatable high accuracies. In addition, hyperspectral sensors are becoming smaller, and today they even fit in CubeSats. However, the amount of data they generate is so large that satellite communication systems have severe limitations to download it, especially in SmallSats. It is therefore becoming urgent to develop efficient automated algorithms that can be executed in the limited capabilities of the onboard computers of these satellites, so as to reduce the amount of data to be stored and downloaded, while keeping as much information as possible for a given scene. In this work, a band selection algorithm has been designed to deal with this problem. The proposed algorithm consists of the sequential selection of the spectral bands ranked using the amount of information provided by each band, and also the correlation of these bands with the previously selected ones. The algorithm performance is assessed by means of a suite of classification tests with hyperspectral datasets from different sensors. Results show comparable or even better performance than other existing band selection algorithms, while outperforming in terms of computational complexity, which makes it more suitable for SmallSats with limited computing resources.

In the processing of remotely sensed data, classification may be preceded by feature extraction, which helps in making the most informative parts of the data emerge. Effective feature extraction may boost the efficiency and accuracy of the following classification, and hence various methods have been proposed to perform it. Recently, Singular Spectrum Analysis (SSA) and its 2-D variation (2D-SSA) have emerged as popular, cutting-edge technologies for effective feature extraction in Hyperspectral Images (HSI). Using 2D-SSA, each band image of an HSI is initially decomposed into various components, and then the image is reconstructed using the most significant eigen-tuples relative to their eigen-values, which represent strong spatial features for the classification task. However, instead of performing reconstruction on the whole image, it may be more effective to apply reconstruction to object-specific spatial regions, which is the proposed objective of this research. As an HSI may cover a large area, multiple objects are generally present within a single scene. Hence, spatial information can be highlighted accurately by specializing the reconstruction based on the local context. The local context may be defined by the so-called superpixels, i.e., finite sets of pixels that constitute a homogeneous set. Each superpixel may undergo tailored reconstruction, with a process expected to perform better than non-spatially-adaptive approaches. In this paper, a Superpixel-based SSA (SP-SSA) method is proposed where the image is first segmented into multiple regions using a superpixel segmentation approach. Next, each segment is individually reconstructed using 2D-SSA. In doing so, the spatial contextual information is preserved, leading to better classifier performance. The performance of the reconstructed features is evaluated using an SVM classifier. Experiments on four popular benchmark datasets reveal that, in terms of the classification accuracy, the proposed approach overperforms the standard SSA technique and various common spatio-spectral classification methods.

The objective of this paper is to develop an efficient noise detector and filter for random valued impulse noise (RVIN) in gray level images. A Dual Histogram based Noise Detector (DHND) is proposed and compared with other approaches using the classical measures like False Alarm (FA) and Miss Detection (MD). Also a novel filtering approach is proposed namely Hybrid Gaussian Filtering (HGF) which combines two filters such as Gaussian Impulse Kernel (GIK) and Gaussian Weighted Median (GWM). The proposed HGF approach is validated across state-of-art filters in terms of standard performance measures such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Feature Similarity Index Measure (FSIM). The robustness of the filter is experimented for various standard gray scale images to evaluate its global significance. Experimental results reveal the pros and cons of other algorithms along with the effectiveness of the proposed filter under varying noise conditions.

Recent developments in hyperspectral sensors have made it possible to acquire HyperSpectral Images (HSI) with higher spectral and spatial resolution. Hence, it is now possible to extract detailed information about relatively smaller structures. Despite these advantages, HSI suffers from many challenges also, like higher spatial variability of spectral signatures, the Hughes effect due to higher dimensionality, and a limited number of labeled training samples compared to the dimensions of the spectral space. Superpixels can be a potentially effective tool in tackling these challenges. Superpixel segmentation is a process of segmenting the spatial image into several semantic sub-regions with similar characteristic features. Such grouping by similarity can significantly ease the subsequent processing steps. Because of this, superpixels have been successfully applied to various fields of HSI processing like Classification, Spectral Unmixing, Dimensionality Reduction, Band Selection, Active Learning, Denoising, and Anomaly Detection. This paper focuses on Classification, presenting a detailed survey of superpixel segmentation approaches for the classification of HSI. The Superpixel creation algorithm framework and post-processing frameworks for superpixels in HSI are also analyzed. Also, a brief description of various application areas of superpixels is provided. An experimental analysis of existing superpixel segmentation approaches is also provided in this paper, supported by quantitative results on standard benchmark datasets. The challenges and future research directions for the implementation of superpixel algorithms are also discussed.

A hyperspectral image (HSI) is a collection of several narrow-band images that span a wide spectral range. Each band reflects the same scene, composed of various objects imaged at different wavelengths; the spatial information, however, remains generally consistent across bands. Both types of information, spectral and spatial, can be leveraged to identify and classify objects. Recently, the use of machine learning (ML) in object classification has become increasingly widespread. Regardless of the selected approach, object-specific spectral and spatial information is key to discriminating relevant categories. Whereas spatial information is usually repeated across bands, spectral information tends to be distributed more unevenly and often highly so. This poses the issue of removing redundancy, which is commonly called the band selection ( BS ) problem and refers to identifying an optimal subset of bands for further HSI processing.

Classification is an important problem in a large variety of applications, which makes it an open-ended forum for researchers in various disciplines. In this paper, the proposed two approaches are mostly focused on Nearest Regularized Subspace (NRS) classifier. The proposed pair of variants are: (1) reducing the computational complexity of NRS classifier termed as Conditional Fast Nearest Regularized Subspace (CFNRS) classifier and (2) incorporation of dissimilarity feature termed as Conditional Dissimilarity-based Nearest Regularized Subspace (CDNRS) classifier. Regarding the first approach, the simple k-NN classifier result is used as a condition to evaluate NRS classifier. Regarding the second approach, an intra-feature dissimilarity measure is considered to create a pair of dictionary which contains a conditional binary matrix and a dissimilarity feature matrix. Each conditional binary matrix is a collection of distinct sub-spaces representing their respective classes. The incoming data is classified with the help of a dictionary and classification performance is validated over other state-of-art approaches. As Hyper-Spectral (HS) data have a strong spectral correlation in terms of spectral signatures, various such HS data are included for validation of our approach. The experimental study reveals the pros and cons of different classifiers along with the effectiveness of our proposed approach. The proposed conditional classifier is found to be able to compete with other, standard classifiers in terms of accuracy and computational complexity, mostly where features possess some sort of sequence information. The suitability of the proposed classifier is also verified for both binary and multi-class classification problems. Along with the experimental validation, the statistical significance of the proposed conditional measure is assessed over other standard state-of-art methods, and the proposed one outperformed the others. The experimental results reveal the importance of dissimilarity features for classification and also suggest to incorporate a simple classifier such as k-NN, prior to any other classifier. This is mainly meant to reduce the computational complexity of the selected classifier without compromising the accuracy.

The problem of classification is shared across various disciplines. Designing even less computationally demanding and more effective classifiers has been a key challenge for researchers for many years. No single classifier can be highly effective for all types of datasets and thus, depending on the data distribution, various classifiers have been proposed in the literature. To our knowledge, feature values have been vastly exploited as the base for discriminating classes, while feature sequence information has been somehow under-exploited so far. In the proposed approach normalised features are sorted and ranked, creating a sequence of finite numbers. The associated rank of the created sequence is used as an additional feature, which in a way defines the sample-specific intra-feature relationship. Three novel dictionary-based approaches such as Sequence Classifier (SC), Sequence-dictionary-based k-Nearest Neighbours Classifier (SDk-NN) and Combined-dictionary-based k-Nearest Neighbours Classifier (CDk-NN) are proposed in this paper. In the case of remotely sensed data, and specifically in Hyper-Spectral Images (HSI), the spectral features (Spectral signatures) represent a strong, object-specific spectral relationship, which is a key point in our proposed approach. In this case, indeed, the proposed classifiers were tested over various (five) HS datasets and found to be effective. Based on the classifiers features, two derived distance measures are proposed and validated for the HS dataset, namely: the Normalised Sequence Distance (NSD) measure and Combined Distance (CD) measure. These measures appear to overperform the conventional Normalised Euclidean Distance (NED) in this context. Also, validation for both binary and multi-class datasets are experimented and their performances are evaluated in terms of accuracy and other standard measures. Experimental results over 21 datasets revealed that the proposed approaches perform comparably, and in some cases even better than other classifiers. Stack-operated, class-specific sparse dictionaries are also introduced in order to reduce the computational complexity, which can be used as an active learning-based approach for optimal training sample selection. Additional tests were performed with variable levels of dictionary sparsity for assessing its impact on accuracy.