Hyperspectral change detection: an experimental comparative study

ABSTRACT

The Earth’s surface is constantly changing due to variations originating from the increasing human population. In the last decade, numerous methods were presented in the literature for change detection using multispectral image data. Owing to the increasing availability of hyperspectral images, these methods are now being applied to hyperspectral images. The main objective of this study is to present different change detection methods in hyperspectral imagery. Numerous algorithms (more than 43 algorithms) have been proposed for change detection in hyperspectral imagery over the last decade. In this work, we provide a comparative review of these algorithms through experimental results. We place the algorithms in five major groups: (1) match-based, (2) transformation-based, (3) direct classification-based, (4) post-classification-based, and (5) hybrid-based. We evaluate and compare the performances of all five groups using two real-world data sets of multi-temporal hyperspectral imagery. This comparative study investigates the advantages and disadvantages of the effects of preprocessing steps in the efficiency of the hyperspectral change detection (HSCD) methods. These preprocessing steps are considered in four scenarios, including: (1) considering only spatial or geometric correction without noise reduction and spectral correction; (2) spatial, atmospheric, and radiometric corrections without noise reduction; (3) spatial correction and noise reduction without atmospheric and radiometric corrections; and (4) spatial, atmospheric, and radiometric correction with noise reduction. The empirical results, followed by a summary of the pros and cons of each algorithm, aim to help researchers select the procedures with the best characteristics for HSCD applications.     

Gain-scheduled controller synthesis for a nonlinear PDE

Linear parameter-varying (LPV) modelling and control of a nonlinear partial differential equation (PDE) is considered in this article. The one-dimensional viscous Burgers' equation is discretised using a finite difference scheme; the boundary conditions are taken as control inputs and the velocities at two grid points are assumed to be measurable. A nonlinear high-order state space model is generated and proper orthogonal decomposition is used for model order reduction. After assessing the accuracy of the reduced model, a low-order functional observer is designed to estimate the reduced states which are linear combinations of the velocities at all grid points. A discrete-time quasi-LPV model that is affine in scheduling parameters is derived based on the reduced model. A polytopic LPV controller is synthesised based on a generalised plant containing the LPV model and the functional observer. More generally, the proposed method can be used to design an LPV controller for a quasi-LPV system with non-measurable scheduling parameters. Simulation results demonstrate the high tracking performance and disturbance and measurement noise rejection capabilities of the designed LPV controller compared with a linear quadratic Gaussian (LQG) controller based on a linearised model.     

Preparation of a novel potassium ion imprinted polymeric nanoparticles based on dicyclohexyl 18C6 for selective determination of K+ ion in different water samples

This work reports the first application of the ion imprinting technology for determination of potassium ion by precipitation polymerization method. Ion imprinted polymeric (IIP) nanoparticles were prepared by using dicyclohexyl 18C6 (DC18C6) as a K⁺ ion selective crown ether, in the acetonitrile–dimethylsulfoxide (3:1; v/v) mixture as porogen. The imprint potassium ion was removed from the polymeric matrix using 0.5 M HNO₃. The scanning electron microscopy (SEM) micrographs showed colloidal nanoparticles of 60–90 nm in diameter and slightly irregular in shape. The obtained ion-imprinted particles for K⁺ showed selective recognition with rapid adsorption and desorption processes. It was found that imprinting results in increased affinity of the material toward K⁺ ion over other competitor metal ions with the same charge and/or close ionic radius. The synthesized IIP nanobeads were shown to be promising for solid-phase extraction coupled with flame photometry for determination of trace K⁺ ion in different water samples.     

A goal programming-TOPSIS approach to multiple response optimization using the concepts of non-dominated solutions and prediction intervals

Multiple response problems include three stages: data gathering, modeling and optimization. Most approaches to multiple response optimization ignore the effects of the modeling stage; the model is taken as given and subjected to multi-objective optimization. Moreover, these approaches use subjective methods for the trade off between responses to obtain one or more solutions. In contradistinction, in this paper we use the Prediction Intervals (PIs) from the model building stage to trade off between responses in an objective manner. Our new method combines concepts from the goal programming approach with normalization based on negative and positive ideal solutions as well as the use of prediction intervals for obtaining a set of non-dominated and efficient solutions. Then, the non-dominated solutions (alternatives) are ranked by the TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) approach. Since some suggested settings of the input variables may not be possible in practice or may lead to unstable operating conditions, this ranking can be extremely helpful to Decision Makers (DMs). The consideration of statistical results together with the selection of the preferred solution among the efficient solutions by Multiple Attribute Decision Making (MADM) distinguishes our approach from others in the literature. We also show, through a numerical example, how the solutions of other methods can be obtained by modifying the relevant approach according to the DM’s requirements.     

Sensitivity analysis of the OWA operator.

The successful design and application of the ordered weighted averaging (OWA) method as a decision-making tool depend on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability method, which give different behavior patterns for the OWA. These two methods will be first analyzed in detail by using sensitivity analysis on the outputs of the OWA with respect to the optimism degree of the decision maker, and then the two methods will be compared. The fuzzy linguistic quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the minimal variability method. However, in using the minimal variability method, the OWA has a linear behavior with respect to the optimism degree, and, therefore, it has better computation efficiency. Since maximizing the combined goodness measure and minimizing its sensitivity to optimism degree are conflicting objectives, a new composite measure of goodness will be defined to have more reliability in obtaining optimal solutions. The theoretical results will be illustrated in a water resources management problem.     

Fuzzy quantifiers in sensitivity analysis of OWA operator

The efficient use of the Ordered Weighted Averaging (OWA) operator in decision making problems depends on the choice of the order weights. Using fuzzy quantifiers is one of the most popular methods to obtain them. In this study, a new method will be introduced for determining the order weights from the quantifiers, which is especially useful in the case of unimodal quantifiers. The new method is generic and has better computational efficiency in comparison to the previously applied methods. In addition, a new measure for sensitivity analysis on the outputs of OWA operator will be introduced. The theoretical results will be illustrated by a Ph.D. student selection problem discussed earlier in the literature.     

Aging fingerprint characterization of beer using electronic nose

In this work, attempts were made in order to characterize the change of aroma of alcoholic and non alcoholic beers during the aging process by use of a metal oxide semiconductor based electronic nose. The aged beer samples were statistically characterized in several classes. Linear techniques as principal component analysis (PCA) and Linear Discriminant Analaysis (LDA) were performed over the data that revealed non alcoholic beer classes are separated except a partial overlapping between zones corresponding to two specified classes of the aged beers. A clear discrimination was not found among the alcoholic beer classes showing the more stability of such type of beer compared with non alcoholic beer. In this research, to classify the classes, two types of artificial neural networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Backpropagation (BP) learning method. The classification success was found to be 90% and 100% for alcoholic and non alcoholic beers, respectively. Application of PNN showed the classification accuracy of 83% and 100%, respectively for the aged alcoholic and non alcoholic beer classes as well. Finally, this study showed the capability of the electronic nose system for the evaluation of the aroma fingerprint changes in beer during the aging process.     

Development of a parsimonious GA-NN ensemble model with a case study for Charpy impact energy prediction

A parsimonious genetic algorithm guided neural network ensemble modelling strategy is presented. Each neural network candidate model to participate in the ensemble model is structurally selected using a genetic algorithm. This provides an effective route to improve the performance of the individual neural network models as compared to more traditional neural network modelling approaches, whereby the neural network structure is selected through some trial-and-error methods or heuristics. The parsimonious neural network ensemble modelling strategy developed in this paper is highly efficient and requires very little extra computation for developing the ensemble model, thus overcoming one of the major known obstacles for developing an ensemble model. The key techniques behind the implementation of the ensemble model, include the formulation of the fitness function, the generation of the qualified neural network candidate models, as well as the specific definitions of the assemble strategies. A case study is presented which exploits a complex industrial data set relating to the Charpy impact energy for heat-treated steels, which was provided by Tata Steel Europe. Modelling results show a significant performance improvement over the previously developed models for the same data set.     

Stress Corrosion Cracking of Basalt/Epoxy Composites under Bending Loading

The purpose of this research is to study the stress corrosion behavior of basalt/epoxy composites under bending loading and submerged in 5% sulfuric acid corrosive medium. There are limited numbers of research in durability of fiber reinforced polymer composites. Moreover, studies on basalt fibers and its composites are very limited. In this research, mechanical property degradation of basalt/epoxy composites under bending loading and submerged in acidic corrosive medium is investigated. Three states of stress, equal to 30%, 50% and 70% of the ultimate strength of composites, are applied on samples. High stress states are applied to the samples to accelerate the testing procedure. Mechanical properties degradation consists of bending strength, bending modulus of elasticity and fracture energy of samples are examined. Also, a normalized strength degradation model for stress corrosion condition is presented. Finally, microscopic images of broken cross sections of samples are examined.     

Study of oil uptake and some quality attributes of potato chips affected by hydrocolloids

The use of coating agents is one effective way to reduce oil absorption in fried products. Reducing the fat content of fried foods by application of coatings is an alternative solution to comply with both health concerns and consumer preferences. The aim of this study was to analyze the effect of hydrocolloids as coating agent on the quantity of oil uptake and on sensory attributes of potato chips. The effect of the coating composition showed that the minimum fat content was related to 1% carboxymethyl cellulose (CMC), 0.5% xanthan, 0.3% guar and 1% xanthan with 21.2, 21.7, 22.4 and 24.8%, respectively, and the highest of fat content was related to blank sample (non-coated), 2% tragacanth, 0.5% guar gum with 49.4, 41.7 and 33.2% of oil content, respectively (p <0.05). The most effective coating agent reduced the oil uptake by 57.03, 55.94, 54.67 and 49.71%, respectively (p <0.05). Sensory evaluation showed that the best color was related to 1% CMC, 0.3% guar and 2% tragacanth, and with respect to flavor evaluation the best flavor was observed in tragacanth 2%, CMC 0.5% and CMC 0.1%, and the best texture referred to tragacanth 2%, CMC 0.5% and CMC 1%. In sensory evaluation, all coated chips got high scores compared with blank (non-coated chips) samples (p <0.05).