/pdf/remote-sensing-of-the-environment-4yfxjaaswe.pdf

Remote sensing of the environment

We propose an independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

Measuring statistical dependence with Hilbert-Schmidt norms

A particle swarm optimization (PSO)-based system is proposed to select bands and determine the optimal number of bands to be selected simultaneously, which is near-automatic with only a few data-independent parameters. The proposed system includes two particle swarms, i.e., the outer one for estimating the optimal number of bands and the inner one for the corresponding band selection. To avoid employing an actual classifier within PSO so as to greatly reduce computational cost, criterion functions that can gauge class separability are preferred; specifically, minimum estimated abundance covariance (MEAC) and Jeffreys-Matusita (JM) distance are adopted in this research. The experimental results show that the 2PSO-based algorithm outperforms the popular sequential forward selection (SFS) method and PSO with one particle swarm in band selection.

Optimized Hyperspectral Band Selection Using Particle Swarm Optimization

An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges

Electric vehicle state of charge estimation: Nonlinear correlation and fuzzy support vector machine

Unsupervised classification approaches, also known as “clustering algorithms”, can be considered a solution to problems associated with the supervised classification of remotely sensed image data. The most important of these problems with respect to statistical classification algorithms is the lack of enough high quality training data and high dimensionality of hyperspectral data. In this paper, an improved clustering framework is developed and evaluated as a resolution to these problems. The proposed method enhances the Fuzzy C-Means (FCM) algorithm by using the Support Vector Domain Description (SVDD). The proposed algorithm operates in a similar manner as the FCM for the clustering and labeling of data vectors. However, for estimation of the cluster centers, the SVDD encircles the corresponding members and estimates the center of a containing sphere. By doing so, the effects of noise and outliers on the cluster centers are reduced, and more specifically, higher classification accuracy can be obtained. In spite of this advantage, there are two sets of parameters, namely, the SVDD's and FCM's parameters, both of which affect the performance of the proposed algorithm. Accordingly, the effects of these parameters and their optimum values have been evaluated as well. The evaluations of the results of experiments show that the proposed algorithm, due to the use of the SVDD algorithm, is more efficient than other clustering algorithms.

An Improved FCM Algorithm Based on the SVDD for Unsupervised Hyperspectral Data Classification

This paper presents multiple kernel learning (MKL) in the context of remote sensing (RS) image classification problems by illustrating main characteristics of different MKL algorithms and analyzing their properties in RS domain. A categorization of different MKL algorithms is initially introduced, and some promising MKL algorithms for each category are presented. In particular, MKL algorithms presented only in machine learning are introduced in RS. Then, the investigated MKL algorithms are theoretically compared in terms of their: 1) computational complexities; 2) accuracy with different qualities of kernels; and 3) accuracy with different numbers of kernels. After the theoretical comparison, experimental analyses are carried out to compare different MKL algorithms in terms of: 1) model selection and 2) feature fusion problems. On the basis of the theoretical and experimental analyses of MKL algorithms, some guidelines for a proper selection of the MKL algorithms are derived.

Multiple Kernel Learning for Remote Sensing Image Classification

The quality of surface waters plays a key role in the sustainability of ecological systems. Measuring water quality parameters (WQPs) is of high importance in the management of surface water resources. In this paper, contemporary-developed regression analysis was proposed to estimate the hard-to-measure parameters from those that can be measured easily. To this end, we proposed a novel modification of support vector regression (SVR), known as multiple-kernel support vector regression (MKSVR) algorithm. The MKSVR learns an optimal data representation for regression analysis by either linear or nonlinear combination of some precomputed kernels. For solving the optimization problem of the MKSVR, the particle swarm optimization (PSO) algorithm was used. The proposed algorithm was assessed using WQPs taken from Karun River, Iran. MKSVR was used to estimate chemical oxygen demand (COD) and biochemical oxygen demand (BOD) using nine WQPs as the input variables, namely electrical conductivity, sodium, calcium, magnesium, phosphate, nitrite, nitrate nitrogen, turbidity, and pH. The results of the proposed MKSVR were compared with those obtained using the SVR and Random Forest regression (RFR). The results showed that the MKSVR algorithm (correlation coefficient [R] = 0.8 and root mean squared error [RMSE] = 4.76 mg/l) increased the accuracy level of BOD prediction when compared with SVR (R = 0.68 and RMSE = 5.15 mg/l) and RFR (R = 0.77 and RMSE = 5.15 mg/l). In the case of COD estimation, the performance of a developed support vector machine (SVM) technique was satisfying. Overall, the use of MKSVR along with the PSO algorithm could demonstrate the superiority of the newly developed SVM technique for the WQPs estimation in the natural streams.

A Novel Multiple-Kernel Support Vector Regression Algorithm for Estimation of Water Quality Parameters

Multiple Kernel Learning (MKL) algorithms have recently demonstrated their effectiveness for classifying the data with numerous features. These algorithms aim at learning an optimal composite kernel through combining the basis kernels constructed from different features. Despite their satisfactory results, MKL algorithms assume that the basis kernels are a priori computed. Moreover, they adopt complex optimization methods to train the combination of the basis kernels, which are usually hard to solve and can only handle the binary classification problems. In this paper, a novel MKL framework was introduced in order to address all these issues. This framework optimizes a data-dependent kernel evaluation measure in order to learn both the basis kernels and their combination. The kernel evaluation measure should be able to estimate the goodness of the composite kernel for a multiclass classification problem. In this paper, we defined such a measure based on the similarity between the composite kernel and an ideal kernel. To this end, three different kernel-based similarity measures, namely kernel alignment (KA), centered kernel alignment (CKA), and Hilbert-Schmidt independence criterion (HSIC), were presented. For solving the optimization problem of the proposed MKL framework, we used the metaheuristic optimization algorithms, which in addition to being accurate algorithms can be easily implemented. The performance of the proposed framework was evaluated by classifying the features extracted from two multispectral and hyperspectral datasets. The results showed that this framework outperformed the other state-of-the-art MKL algorithms in terms of both classification accuracy and the computational time.

Saeid Niazmardi

Papers

An Improved FCM Algorithm Based on the SVDD for Unsupervised Hyperspectral Data Classification

Multiple Kernel Learning for Remote Sensing Image Classification

A Novel Multiple-Kernel Support Vector Regression Algorithm for Estimation of Water Quality Parameters

A Novel Multiple Kernel Learning Framework for Multiple Feature Classification

Histogram-based spatio-temporal feature classification of vegetation indices time-series for crop mapping.