This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines (SVMs) First, we propose a theoretical discussion and experimental analysis aimed at understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces Then, we assess the effectiveness of SVMs with respect to conventional feature-reduction-based approaches and their performances in hypersubspaces of various dimensionalities To sustain such an analysis, the performances of SVMs are compared with those of two other nonparametric classifiers (ie, radial basis function neural networks and the K-nearest neighbor classifier) Finally, we study the potentially critical issue of applying binary SVMs to multiclass problems in hyperspectral data In particular, four different multiclass strategies are analyzed and compared: the one-against-all, the one-against-one, and two hierarchical tree-based strategies Different performance indicators have been used to support our experimental studies in a detailed and accurate way, ie, the classification accuracy, the computational time, the stability to parameter setting, and the complexity of the multiclass architecture The results obtained on a real Airborne Visible/Infrared Imaging Spectroradiometer hyperspectral dataset allow to conclude that, whatever the multiclass strategy adopted, SVMs are a valid and effective alternative to conventional pattern recognition approaches (feature-reduction procedures combined with a classification method) for the classification of hyperspectral remote sensing data

/pdf/classification-of-hyperspectral-remote-sensing-images-with-51xjutm0y6.pdf

Classification of hyperspectral remote sensing images with support vector machines

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私の computer 環境

The aim of this paper is twofold. First, we present a thorough experimental study to show the superiority of the generalization capability of the support vector machine (SVM) approach in the automatic classification of electrocardiogram (ECG) beats. Second, we propose a novel classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the basis of ECG data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In particular, they were organized so as to test the sensitivity of the SVM classifier and that of two reference classifiers used for comparison, i.e., the k-nearest neighbor (kNN) classifier and the radial basis function (RBF) neural network classifier, with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the SVM approach as compared to traditional classifiers, and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. On an average, over three experiments making use of a different total number of training beats (250, 500, and 750, respectively), the PSO-SVM yielded an overall accuracy of 89.72% on 40438 test beats selected from 20 patient records against 85.98%, 83.70%, and 82.34% for the SVM, the kNN, and the RBF classifiers, respectively.

Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization

Recent remote sensing literature has shown that support vector machine (SVM) methods generally outperform traditional statistical and neural methods in classification problems involving hyperspectral images. However, there are still open issues that, if suitably addressed, could allow further improvement of their performances in terms of classification accuracy. Two especially critical issues are: 1) the determination of the most appropriate feature subspace where to carry out the classification task and 2) model selection. In this paper, these two issues are addressed through a classification system that optimizes the SVM classifier accuracy for this kind of imagery. This system is based on a genetic optimization framework formulated in such a way as to detect the best discriminative features without requiring the a priori setting of their number by the user and to estimate the best SVM parameters (i.e., regularization and kernel parameters) in a completely automatic way. For these purposes, it exploits fitness criteria intrinsically related to the generalization capabilities of SVM classifiers. In particular, two criteria are explored, namely: 1) the simple support vector count and 2) the radius margin bound. The effectiveness of the proposed classification system in general and of these two criteria in particular is assessed both by simulated and real experiments. In addition, a comparison with classification approaches based on three different feature selection methods is reported, i.e., the steepest ascent (SA) algorithm and two other methods explicitly developed for SVM classifiers, namely: 1) the recursive feature elimination technique and 2) the radius margin bound minimization method

Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images

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.

Robust support vector method for hyperspectral data classification and knowledge discovery

We present an algorithm for the application of support vector machine (SVM) learning to image compression. The algorithm combines SVMs with the discrete cosine transform (DCT). Unlike a classic radial basis function networks or multilayer perceptrons that require the topology of the network to be defined before training, an SVM selects the minimum number of training points, called support vectors, that ensure modeling of the data within the given level of accuracy (a.k.a. insensitivity zone /spl epsi/). It is this property that is exploited as the basis for an image compression algorithm. Here, the SVMs learning algorithm performs the compression in a spectral domain of DCT coefficients, i.e., the SVM approximates the DCT coefficients. The parameters of the SVM are stored in order to recover the image. Results demonstrate that even though there is an extra lossy step compared with the baseline JPEG algorithm, the new algorithm dramatically increases compression for a given image quality; conversely it increases image quality for a given compression ratio. The approach presented can be readily applied for other modeling schemes that are in a form of a sum of weighted basis functions.

http://www.support-vector.ws/Robinson-Kecman,%20ieee_trans_neural_2003_published.pdf

Combining support vector machine learning with the discrete cosine transform in image compression

There is provided a method of producing compressed data including the steps of receiving input data to be compressed (step 10), transforming said input data into a sum of basis functions multiplied by a corresponding set of coefficients, which may be a transformation to the frequency domain(step 12), forming a function or row of data from said set of coefficients (step 17), estimating the function using a learning process (step 21) and recording the resulting estimate (step 28) as the compressed data. Also provided is a method of estimating a function which may be used in the method of producing compressed data including inverting the function about a predetermined value (step 18) prior to using a learning process to estimate the function. Apparatus (1) for implementing the method and a software product to cause a processing means to implement the method are also claimed.

Method, apparatus and software for lossy data compression and function estimation

Methods exploring the application of neural networks to still image compression are detailed in both the spatial and frequency domains. In particular the sparse properties of Support Vector Machine (SVM) learning are exploited in the compression algorithms. A classic radial basis function (RBF) neural network requires that the topology of the network be defined before training. An SVM has the property that it will choose the minimum number of training points to use as centres of the Gaussian kernel functions. It is this property that is exploited as the basis for image compression algorithms presented in this thesis. Several novel algorithms are developed applying SVM learning to both directly model the colour surface and model transform coefficients after the surface has been transformed into the frequency domain. It is demonstrated that compression is more efficient in frequency space. The Discrete Cosine Transform (DCT) is used to transform the colour surface into the frequency domain. A counter-intuitive result is shown where mapping the DCT coefficients to a 1-dimensional function for SVM modelling produces better results than SVM modelling of the 2-dimensional transform surface. Results are presented in comparison to the JPEG image compression algorithm. In the frequency domain, results are superior to that of JPEG. For example, the quality of the ‘Lena’ image compressed 63:1 for JPEG is slightly worse quality than the same image compressed 192:1 with the RKi-1 algorithm presented in this thesis. Due to the commercial value of the algorithms detailed in this thesis, a patent has been filed.

The Application of Support Vector Machines to Compression of Digital Images

In this paper we present a novel algorithm exploiting sparse properties of support vector machines (SVM) with application to image compression. The algorithm combines SVM learning with the discrete cosine transform (DCT). Unlike a classic radial basis function (RBF) networks or multilayer perceptrons that require the topology of the network to be defined before training, an SVM selects the minimum number of training points, called support vectors, that ensure modelling of the data within the given level of accuracy (a.k.a. insensitivity zone /spl epsi/). It is this property that is exploited as the basis for an image compression algorithm. Here, the SVMs learning algorithm performs the compression in a spectral domain of DCT coefficients. Results demonstrate that even though there is an extra lossy step compared with the baseline JPEG algorithm, the new algorithm dramatically increases compression for a given image quality; conversely it increases image quality for a given compression ratio. The approach presented can be readily applied for other modelling schemes that are in a form of a sum of weighted basis functions.

https://www.researchgate.net/profile/Vojislav_Kecman/publication/4030260_Exploitation_of_sparse_properties_of_support_vector_machines_in_image_compression/links/0c96052971fd8ee90e000000.pdf

Jonathan Robinson

Papers

Combining support vector machine learning with the discrete cosine transform in image compression

Method, apparatus and software for lossy data compression and function estimation

The Application of Support Vector Machines to Compression of Digital Images

Exploitation of sparse properties of support vector machines in image compression