This letter introduces a nonlinear measure of independence between random variables for remote sensing supervised feature selection. The so-called Hilbert-Schmidt independence criterion (HSIC) is a ...kernel method for evaluating statistical dependence and it is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is easy to compute and has good theoretical and practical properties. Rather than using this estimate for maximizing the dependence between the selected features and the class labels, we propose the more sensitive criterion of minimizing the associated HSIC p -value. Results in multispectral, hyperspectral, and SAR data feature selection for classification show the good performance of the proposed approach.
This paper studies the performance and applicability of a novel kernel partial least squares (KPLS) algorithm for nonlinear feature extraction in the context of remote sensing applications. The ...so-called kernel orthonormalized PLS algorithm with reduced complexity (rKOPLS) has the following two core parts: (1) a kernel version of OPLS (called KOPLS) and (2) a sparse approximation for large-scale data sets, which ultimately leads to the rKOPLS algorithm. The method is theoretically analyzed in terms of computational burden and memory requirements and is tested in common remote sensing applications: multi- and hyperspectral image classification and biophysical parameter estimation problems. The proposed method largely outperforms the traditional (linear) PLS algorithm and demonstrates good capabilities in terms of expressive power of the extracted nonlinear features, accuracy, and scalability as compared to the standard KPLS.
This paper presents a methodology for cloud screening of multispectral images acquired with the Medium Resolution Imaging Spectrometer (MERIS) instrument on-board the Environmental Satellite ...(ENVISAT). The method yields both a discrete cloud mask and a cloud-abundance product from MERIS level-1b data on a per-pixel basis. The cloud-screening method relies on the extraction of meaningful physical features (e.g., brightness and whiteness), which are combined with atmospheric-absorption features at specific MERIS-band locations (oxygen and water-vapor absorptions) to increase the cloud-detection accuracy. All these features are inputs to an unsupervised classification algorithm; the cloud-probability output is then combined with a spectral unmixing procedure to provide a cloud-abundance product instead of binary flags. The method is conceived to be robust and applicable to a broad range of actual situations with high variability of cloud types, presence of ground covers with bright and white spectra, and changing illumination conditions or observation geometry. The presented method has been shown to outperform the MERIS level-2 cloud flag in critical cloud-screening situations, such as over ice/snow covers and around cloud borders. The proposed modular methodology constitutes a general framework that can be applied to multispectral images acquired by spaceborne sensors working in the visible and near-infrared spectral range with proper spectral information to characterize atmospheric-oxygen and water-vapor absorptions.
This letter introduces a simple method for including invariances in support-vector-machine (SVM) remote sensing image classification. We design explicit invariant SVMs to deal with the particular ...characteristics of remote sensing images. The problem of including data invariances can be viewed as a problem of encoding prior knowledge, which translates into incorporating informative support vectors (SVs) that better describe the classification problem. The proposed method essentially generates new (synthetic) SVs from the obtained by training a standard SVM with the available labeled samples. Then, original and transformed SVs are used for training the virtual SVM introduced in this letter. We first incorporate invariances to rotations and reflections of image patches for improving contextual classification. Then, we include an invariance to object scale in patch-based classification. Finally, we focus on the challenging problem of including illumination invariances to deal with shadows in the images. Very good results are obtained when few labeled samples are available for classification. The obtained classifiers reveal enhanced sparsity and robustness. Interestingly, the methodology can be applied to any maximum-margin method, thus constituting a new research opportunity.
This letter proposes the kernel entropy component analysis for clustering remote sensing data. The method generates nonlinear features that reveal structure related to the Rényi entropy of the input ...space data set. Unlike other kernel feature-extraction methods, the top eigenvalues and eigenvectors of the kernel matrix are not necessarily chosen. Data are interestingly mapped with a distinct angular structure, which is exploited to derive a new angle-based spectral clustering algorithm based on the mapped data. An out-of-sample extension of the method is also presented to deal with test data. We focus on cloud screening from Medium Resolution Imaging Spectrometer images. Several images are considered to account for the high variability of the problem. Good results obtained show the suitability of the proposal.
This paper evaluates nonlinear retrieval methods to derive atmospheric properties from hyperspectral infrared sounding spectra, with emphasis on the retrieval of temperature, humidity, and ozone ...atmospheric profiles. We concentrate on the Infrared Atmospheric Sounding Interferometer (IASI) onboard the MetOp-A satellite data for the future Meteosat Third Generation Infrared Sounder (MTG-IRS). The methods proposed in this work are compared in terms of both accuracy and speed with the current MTG-IRS L2 processing concept, which processes MetOp-IASI and proxy MTG-IRS data. The official chain consists of a principal component extraction, typically referred to as empirical orthogonal functions (EOF) and a subsequent canonical linear regression. This research proposes the evaluation of some other methodological advances considering: 1) other linear feature extraction methods instead of EOF, such as partial least squares; and 2) the linear combination of nonlinear regression models in the form of committee of experts. The nonlinear regression models considered in this work are artificial neural networks and kernel ridge regression as nonparametric multioutput powerful regression tools. Results show that, in general, nonlinear models yield better results than linear retrieval for both MetOp-IASI and MTG-IRS synthetic and real data. Averaged gains throughout the column of +1.8 K and +2.2 K are obtained for temperature profile estimation from MetOp-IASI and IRS data, respectively. Similar gains are obtained for the estimation of dew point temperatures. In both variables, these improvements are more noticeable in lower atmospheric layers. The combination of models makes the retrieval more robust, improves the accuracy, and decreases the estimated bias. The nonlinear statistical approach is successfully compared to optimal estimation (OE) in terms of accuracy, bias and computational cost. These results confirm the potential of statistical nonlinear inversion techniques for the retrieval of atmospheric profiles.
We propose the use of support vector machines (SVMs) for automatic hyperspectral data classification and knowledge discovery. In the first stage of the study, we use SVMs for crop classification and ...analyze their performance in terms of efficiency and robustness, as compared to extensively used neural and fuzzy methods. Efficiency is assessed by evaluating accuracy and statistical differences in several scenes. Robustness is analyzed in terms of: (1) suitability to working conditions when a feature selection stage is not possible and (2) performance when different levels of Gaussian noise are introduced at their inputs. In the second stage of this work, we analyze the distribution of the support vectors (SVs) and perform sensitivity analysis on the best classifier in order to analyze the significance of the input spectral bands. For classification purposes, six hyperspectral images acquired with the 128-band HyMAP spectrometer during the DAISEX-1999 campaign are used. Six crop classes were labeled for each image. A reduced set of labeled samples is used to train the models, and the entire images are used to assess their performance. Several conclusions are drawn: (1) SVMs yield better outcomes than neural networks regarding accuracy, simplicity, and robustness; (2) training neural and neurofuzzy models is unfeasible when working with high-dimensional input spaces and great amounts of training data; (3) SVMs perform similarly for different training subsets with varying input dimension, which indicates that noisy bands are successfully detected; and (4) a valuable ranking of bands through sensitivity analysis is achieved.
Data from current medium-spatial-resolution imaging spectroradiometers are used for land-cover mapping and land-cover change detection at regional to global scales. However, few landscapes are ...homogeneous at these scales, and this creates the so-called mixed-pixel problem. In this context, this study explores the use of the linear spectral mixture model to extract subpixel land-cover composition from medium-spatial-resolution data. In particular, a time series of MEdium Resolution Imaging Spectrometer (MERIS) full-resolution (FR; pixel size of 300 m) images acquired over The Netherlands is used to illustrate this study. The Netherlands was selected because of the following: 1) the fragmentation of its landscapes and 2) the availability of a high-spatial-resolution land-cover data set (LGN5) which can be used as a reference. The question then is to what extent a multitemporal unmixing of MERIS FR data delivers land-cover information comparable with the one provided by the LGN5. To this end, fully constrained linear spectral unmixing is applied to each individual MERIS image and to the multitemporal composite. The unmixing results are validated at both subpixel and per-pixel scales and at two thematic aggregation levels (12 and 4 land-cover classes). The obtained results indicate that the described unmixing approach yields moderate results for the 12-class case and good results for the 4-class case. These results might be explained by MERIS preprocessing steps, gridding effects, vegetation phenophases, and spectral class separability.
► Feature selection algorithm based on support vector machines and bootstrap methods. ► Proposed FS algorithm outperforms the recursive feature elimination method. ► VF detector performance improves ...with the reduced feature set.
Early detection of ventricular fibrillation (VF) is crucial for the success of the defibrillation therapy in automatic devices. A high number of detectors have been proposed based on temporal, spectral, and time–frequency parameters extracted from the surface electrocardiogram (ECG), showing always a limited performance. The combination ECG parameters on different domain (time, frequency, and time–frequency) using machine learning algorithms has been used to improve detection efficiency. However, the potential utilization of a wide number of parameters benefiting machine learning schemes has raised the need of efficient feature selection (FS) procedures. In this study, we propose a novel FS algorithm based on support vector machines (SVM) classifiers and bootstrap resampling (BR) techniques. We define a backward FS procedure that relies on evaluating changes in SVM performance when removing features from the input space. This evaluation is achieved according to a nonparametric statistic based on BR. After simulation studies, we benchmark the performance of our FS algorithm in AHA and MIT-BIH ECG databases. Our results show that the proposed FS algorithm outperforms the recursive feature elimination method in synthetic examples, and that the VF detector performance improves with the reduced feature set.
A Support Vector Machine MUSIC Algorithm El Gonnouni, A.; Martinez-Ramon, Manel; Rojo-Alvarez, J. L. ...
IEEE transactions on antennas and propagation,
10/2012, Letnik:
60, Številka:
10
Journal Article
Recenzirano
This paper introduces a new Support Vector Machine (SVM) formulation for the direction of arrival (DOA) estimation problem. We establish a theoretical relationship between the Minimum Variance ...Distortionless Response (MVDR) and the MUltiple SIgnal Characterization (MUSIC) methods. This leads naturally to the derivation of an SVM-MUSIC algorithm, which combines the benefits of subspace methods with those of SVM. Spatially smoothed versions and a recursive form of the algorithms exhibit good performance against coherent signals. We test the method's performance in scenarios with noncoherent and coherent signals, and in small-sample size-situations obtaining an improved performance in comparison with existing standard approaches.
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