Hyperspectral image classification using graph-based wavelet transform

ABSTRACT

Graph-based methods are developed to efficiently extract data information. In particular, these methods are adopted for high-dimensional data classification by exploiting information residing on weighted graphs. In this paper, we propose a new hyperspectral texture classifier based on graph-based wavelet transform. This recent graph transform allows extracting textural features from a constructed weighted graph using sparse representative pixels of hyperspectral image. Different measurements of spectral similarity between representative pixels are tested to decorrelate close pixels and improve the classification precision. To achieve the hyperspectral texture classification, Support Vector Machine is applied on spectral graph wavelet coefficients. Experimental results obtained by applying the proposed approach on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) datasets provide good accuracy which could exceed 98.7%. Compared to other famous classification methods as conventional deep learning-based methods, the proposed method achieves better classification performance. Results have shown the effectiveness of the method in terms of robustness and accuracy.     

Hyperspectral image classification via compact-dictionary-based sparse representation

Multimedia Tools and Applications - In this paper, a compact-dictionary-based sparse representation (CDSR) method is proposed for hyperspectral image (HSI) classification. The proposed dictionary...     

Fractional differentiation and non-Pareto multiobjective optimization for image thresholding

Various techniques have previously been proposed for single-stage thresholding of images to separate objects from the background. Although these global or local thresholding techniques have proven effective on particular types of images, none of them is able to produce consistently good results on a wide range of existing images. Here, a new image histogram thresholding method, called TDFD, based on digital fractional differentiation is presented for gray-level image thresholding. The proposed method exploits the properties of the digital fractional differentiation and is based on the assumption that the pixel appearance probabilities in the image are related. To select the best fractional differentiation order that corresponds to the best threshold, a new algorithm based on non-Pareto multiobjective optimization is presented. A new geometric regularity criterion is also proposed to select the best thresholded image. In order to illustrate the efficiency of our method, a comparison was performed with five competing methods: the Otsu method, the Kapur method, EM algorithm based method, valley emphasis method, and two-dimensional Tsallis entropy based method. With respect to the mode of visualization, object size and image contrast, the experimental results show that the segmentation method based on fractional differentiation is more robust than the other methods.     

基于KMNF和BP神经网络的高光谱遥感影像分类

为了对高维非线性的高光谱影像进行降维及信息提取,提出了高光谱影像核最小噪声分离变换(kernel minimum noise fraction,KMNF)特征提取后利用BP神经网络分类的方法.以高光谱影像KMNF特征提取后的前几个特征分量作为BP神经网络的输入,进行BP神经网络分类,并与单独的高光谱影像BP神经网络分类进行比较.美国内华达州CUPRITE矿区AVIRIS数据的实验结果表明,基于KMNF和BP神经网络的高光谱影像分类较单独BP神经网络分类总体精度及时间性能均得到提高.     

Solving multiobjective problems using cat swarm optimization

This paper proposes a new multiobjective evolutionary algorithm (MOEA) by extending the existing cat swarm optimization (CSO). It finds the nondominated solutions along the search process using the concept of Pareto dominance and uses an external archive for storing them. The performance of our proposed approach is demonstrated using standard test functions. A quantitative assessment of the proposed approach and the sensitivity test of different parameters is carried out using several performance metrics. The simulation results reveal that the proposed approach can be a better candidate for solving multiobjective problems (MOPs).     

Hyperspectral image classification via parallel multi-input mechanism-based convolutional neural network

In recent years, Convolutional Neural Networks (CNNs) have succeeded in Hyperspectral Image Classification and shown excellent performance. However, the implicit spatial information between features, which significantly affect the classification performance of CNNs, are neglected in most existing CNN models. To address this issue, we propose a parallel multi-input mechanism-based CNN (PMI-CNN) fully exploiting the implicit spectral-spatial information in Hyperspectral Images. PMI-CNN employs four parallel convolution branches to extract spatial features with different levels, feature maps from each branch are spliced, and used as the classifier’s input. The proposed PMI-CNN’s classification performance is examined on three benchmark datasets and compared with six competing models. Experimental results show that PMI-CNN has better classification performance via exploiting spectral-spatial information. Compared with other models, the classification accuracy of PMI-CNN on the Indian Pines dataset is significantly improved, varying between 1.23%-25.36%. Likewise, the PMI-CNN, performed on the other two benchmark datasets, achieves 0.54%-12.26% and 0.96%-8.38% advantages in overall accuracy over the other six models, respectively.

     

Evolutionary multiobjective optimization using an outranking-based dominance generalization

One aspect that is often disregarded in the current research on evolutionary multiobjective optimization is the fact that the solution of a multiobjective optimization problem involves not only the search itself, but also a decision making process. Most current approaches concentrate on adapting an evolutionary algorithm to generate the Pareto frontier. In this work, we present a new idea to incorporate preferences into a multi-objective evolutionary algorithm (MOEA). We introduce a binary fuzzy preference relation that expresses the degree of truth of the predicate “x is at least as good as y”. On this basis, a strict preference relation with a reasonably high degree of credibility can be established on any population. An alternative x is not strictly outranked if and only if there does not exist an alternative y which is strictly preferred to x. It is easy to prove that the best solution is not strictly outranked. For validating our proposed approach, we used the non-dominated sorting genetic algorithm II (NSGA-II), but replacing Pareto dominance by the above non-outranked concept. So, we search for the non-strictly outranked frontier that is a subset of the Pareto frontier. In several instances of a nine-objective knapsack problem our proposal clearly outperforms the standard NSGA-II, achieving non-outranked solutions which are in an obviously privileged zone of the Pareto frontier.     

A local multiobjective optimization algorithm using neighborhood field

A new local search algorithm for multiobjective optimization problems is proposed to find the global optima accurately and diversely. This paper models the cooperatively local search as a potential field, which is called neighborhood field model (NFM). Using NFM, a new Multiobjective Neighborhood Field Optimization (MONFO) algorithm is proposed. In MONFO, the neighborhood field can drive each individual moving towards the superior neighbor and away from the inferior neighbor. MONFO is compared with other popular multiobjective algorithms under twelve test functions. Intensive simulations show that MONFO is able to deliver promising results in the respects of accuracy and diversity, especially for multimodal problems.     

A multiobjective optimization solver using rank-niche evolution strategy

A rank-niche evolution strategy (RNES) algorithm has been developed in this paper to solve unconstrained multiobjective optimization problems. A required number of Pareto-optimal solutions can be generated by the algorithm in a single run. In addition to the operations of recombination, mutation and selection used in original evolution strategy (ES), an external elite set which contains a given number of non-dominated elites is updated and trimmed by a clustering technique to maintain a uniformly distributed Pareto front. The fitness function for each individual contains the information of rank and crowding status. The selection operation using this fitness function considers the superiority and distribution simultaneously. Eight test problems illustrated in other papers are used to test RNES. For some test problems the Pareto-optimal solutions obtained by RNES are better than those obtained by GA-based algorithms.     

Hybrid spatial-spectral feature in broad learning system for Hyperspectral image classification

Hyperspectral images (HSIs) classification have aroused a great deal of attention recently due to their wide range of practical prospects in numerous fields. Spatial-spectral fusion feature is widely used in HSI classification to get better performance. These methods are mostly based on a simple linear addition with the combined hyper-parameter to fuse the spatial and spectral information. It is necessary to fuse the features in a more suitable method. To solve this problem, we propose a novel HSI classification approach based on Hybrid spatial-spectral feature in broad learning system (HSFBLS). First, we employ an adaptive weighted mean filter to obtain spatial feature. Computing the weights of spatial and spectral channels in hybrid module by two BLS and uniting them with a weighted linear function. Then, we fuse the spectral-spatial feature by sparse autoencoder to get weighted fusion feature as the feature nodes to classify HSI data in BLS. By a two-stage fusion of spatial and spectral information, it can increase the classification accuracy contrast to simple combination. Very satisfactory classification results on typical HSI datasets illustrate the availability of proposed HSFBLS. Moreover, HSFBLS also reduce training time greatly contrast to time-consuming network.

     

Hyperspectral remote sensing image classification with information discriminative extreme learning machine

Multimedia Tools and Applications - Hyperspectral remote sensing image classification is important aspect of current research. Extreme learning machine (ELM) has been widely used in the field of...     

MIJ2K Optimization using evolutionary multiobjective optimization algorithms

This paper deals with the multiobjective definition of video compression and its optimization. The optimization will be done using NSGA-II, a well-tested and highly accurate algorithm with a high convergence speed developed for solving multiobjective problems. Video compression is defined as a problem including two competing objectives. We try to find a set of optimal, so-called Pareto-optimal solutions, instead of a single optimal solution. The two competing objectives are quality and compression ratio maximization. The optimization will be achieved using a new patent pending codec, called MIJ2K, also outlined in this paper. Video will be compressed with the MIJ2K codec applied to some classical videos used for performance measurement, selected from the Xiph.org Foundation repository. The result of the optimization will be a set of near-optimal encoder parameters. We also present the convergence of NSGA-II with different encoder parameters and discuss the suitability of MOEAs as opposed to classical search-based techniques in this field.     

Crashworthiness design of vehicle by using multiobjective robust optimization

Although deterministic optimization has to a considerable extent been successfully applied in various crashworthiness designs to improve passenger safety and reduce vehicle cost, the design could become less meaningful or even unacceptable when considering the perturbations of design variables and noises of system parameters. To overcome this drawback, we present a multiobjective robust optimization methodology to address the effects of parametric uncertainties on multiple crashworthiness criteria, where several different sigma criteria are adopted to measure the variations. As an example, a full front impact of vehicle is considered with increase in energy absorption and reduction of structural weight as the design objectives, and peak deceleration as the constraint. A multiobjective particle swarm optimization is applied to generate robust Pareto solution, which no longer requires formulating a single cost function by using weighting factors or other means. From the example, a clear compromise between the Pareto deterministic and robust designs can be observed. The results demonstrate the advantages of using multiobjective robust optimization, with not only the increase in the energy absorption and decrease in structural weight from a baseline design, but also a significant improvement in the robustness of optimum.     

Online image classification using IHDR

This paper presents an incremental algorithm for image classification problems. Virtual labels are automatically formed by clustering in the output space. These virtual labels are used for the process of deriving discriminating features in the input space. This procedure is performed recursively in a coarse-to-fine fashion resulting in a tree, performing incremental hierarchical discriminating regression (IHDR). Embedded in the tree is a hierarchical probability distribution model used to prune unlikely cases. A sample size dependent negative-log-likelihood (NLL) metric is introduced to deal with large sample-size cases, small sample-size cases, and unbalanced sample-size cases, measured among different internal nodes of the IHDR algorithm. We report the experimental results of the proposed algorithm for an OCR classification problem and an image orientation classification problem. Received: November 20, 2001 / Accepted: May 10, 2002     

Hyperspectral image classification based on robust discriminative extraction of multiple spectral-spatial features

In hyperspectral image (HSI) processing, the inclusion of both spectral and spatial features, e.g. morphological features, shape features, has shown great success in classification of hyperspectral data. Nevertheless, there exist two main issues to address: (1) The multiple features are often treated equally and thus the complementary information among them is neglected. (2) The features are often degraded by a mixture of various kinds of noise, leading to the classification accuracy decreased. In order to address these issues, a novel robust discriminative multiple features extraction (RDMFE) method for HSI classification is proposed. The proposed RDMFE aims to project the multiple features into a common low-rank subspace, where the specific contributions of different types of features are sufficiently exploited. With low-rank constraint, RDMFE is able to uncover the intrinsic low-dimensional subspace structure of the original data. In order to make the projected features more discriminative, we make the learned representations optimal for classification. With intrinsic information preserving and discrimination capabilities, the learned projection matrix works well in HSI classification tasks. Experimental results on three real hyperspectral datasets confirm the effectiveness of the proposed method.     

Closed-loop evolutionary multiobjective optimization

Artificial evolution has been used for more than 50 years as a method of optimization in engineering, operations research and computational intelligence. In closed-loop evolution (a term used by the statistician, George Box) or, equivalently, evolutionary experimentation (Ingo Rechenberg?s terminology), the ?phenotypes? are evaluated in the real world by conducting a physical experiment, whilst selection and breeding is simulated. Well-known early work on artificial evolution?design engineering problems in fluid dynamics, and chemical plant process optimization? was carried out in this experimental mode. More recently, the closed-loop approach has been successfully used in much evolvable hardware and evolutionary robotics research, and in some microbiology and biochemistry applications. In this article, several further new targets for closed-loop evolutionary and multiobjective optimization are considered. Four case studies from my own collaborative work are described: (i) instrument optimization in analytical biochemistry; (ii) finding effective drug combinations in vitro; (iii) onchip synthetic biomolecule design; and (iv) improving chocolate production processes. Accurate simulation in these applications is not possible due to complexity or a lack of adequate analytical models. In these and other applications discussed, optimizing experimentally brings with it several challenges: noise; nuisance factors; ephemeral resource constraints; expensive evaluations, and evaluations that must be done in (large) batches. Evolutionary algorithms (EAs) are largely equal to these vagaries, whilst modern multiobjective EAs also enable tradeoffs among conflicting optimization goals to be explored. Nevertheless, principles from other disciplines, such as statistics, Design of Experiments, machine learning and global optimization are also relevant to aspects of the closed-loop problem, and may inspire futher development of multiobjective EAs.     

Simultaneous linear and anti-windup controller synthesis using multiobjective convex optimization

We present a method for the synthesis of a control law for input constrained linear systems that incorporates both a traditional linear output-feedback controller as well as a static anti-windup compensator. Unlike traditional two-step anti-windup controller designs in which the linear controller and anti-windup compensator are designed sequentially, our method synthesizes all controller parameters simultaneously. This one-step design retains the anti-windup structure, thus providing structurally ‘a priori’ compensation for saturation. We derive sufficient conditions for guaranteeing global quadratic stability and for satisfying multiple, possibly conflicting, performance objectives on the constrained and unconstrained closed-loop dynamics. The resulting synthesis problem is recast as an optimization over linear matrix inequalities (LMIs). We demonstrate the proposed method on a benchmark problem.     

Target-matching test problem for multiobjective topology optimization using genetic algorithms

This paper describes the multiobjective topology optimization of continuum structures solved as a discrete optimization problem using a multiobjective genetic algorithm (GA) with proficient constraint handling. Crucial to the effectiveness of the methodology is the use of a morphological geometry representation that defines valid structural geometries that are inherently free from checkerboard patterns, disconnected segments, or poor connectivity. A graph- theoretic chromosome encoding, together with compatible reproduction operators, helps facilitate the transmission of topological/shape characteristics across generations in the evolutionary process. A multicriterion target-matching problem developed here is a novel test problem, where a predefined target geometry is the known optimum solution, and the good results obtained in solving this problem provide a convincing demonstration and a quantitative measure of how close to the true optimum the solutions achieved by GA methods can be. The methodology is then used to successfully design a path-generating compliant mechanism by solving a multicriterion structural topology optimization problem.     

Integrated multiobjective optimization and a priori preferences using genetic algorithms

One of the tasks of decision-making support systems is to develop methods that help the designer select a solution among a set of actions, e.g. by constructing a function expressing his/her preferences over a set of potential solutions. In this paper, a new method to solve multiobjective optimization (MOO) problems is developed in which the user’s information about his/her preferences is taken into account within the search process. Preference functions are built that reflect the decision-maker’s (DM) interests and use meaningful parameters for each objective. The preference functions convert these objective preferences into numbers. Next, a single objective is automatically built and no weight selection is performed. Problems found due to the multimodality nature of a generated single cost index are managed with Genetic Algorithms (GAs). Three examples are given to illustrate the effectiveness of the method.     

Hyperspectral image classification based on joint spectrum of spatial space and spectral space

Multimedia Tools and Applications - This paper presents a novel feature extraction model that incorporates local histogram in spatial space and pixel spectrum in spectral space, with the goal of...