Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images

By Yakoub Bazi, Farid Melgani. IEEE Transactions on Geoscience and Remote Sensing, 2006

456 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/5b284ad0479a4ed16929c43f0e8f1a4bb3a06fd9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Atmospheric Science

In Media Technology

Citations in the corpus

Implementation of machine-learning classification in remote sensing: an applied review (2018, methodology): “For example, Bazi and Melgani (2006) presented a genetic optimization framework for optimizing the SVM classi ﬁ cation of hyperspectral data that allowed for full automation.”
Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines (2015, background): “In the context of machine learning, othermethodologies exist to solve problems related to model selection/parameter optimisation such as grid search, genetic algorithms or random search, which can be used to automatize this process (Bazi and Melgani, 2006; Bergstra and Bengio, 2012).”
Hyperspectral Image Classification With Independent Component Discriminant Analysis (2011, background): “SVMs, in particular, have been widely investigated recently in the remote sensing community [24], [25], [29], [30].”
Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization (2015, methodology): “For example, in [3], Bazi and Melgani proposed a support vector machine (SVM) classification system that allows for detecting the most distinctive features and estimating the SVM parameters by using a GA.”
A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification (2009, methodology): “A genetic algorithmwas proposed by Bazi and Melgani (2006) to select an optimal subset of features for a successive SVM classification.”
Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture (2012, methodology): “More recent methods are wrappers which include the use of non-parametric algorithms like classification trees, neural networks and support vector machines (Bazi and Melgani, 2006; De Stefano et al., 2007; Del Frate et al., 2005; Pal and Foody, 2010; Yu et al., 2002).”
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders (2013, methodology): “PSO is an evolutionary computation method, similar to genetic algorithms [44] and is a population-based search technique that utilizes the concept of social sharing of information.”
Multiclass and Binary SVM Classification: Implications for Training and Classification Users (2008, methodology): “SVM classifications may be more accurate than the widely used alternatives such as classification by maximum likelihood, decision tree, and neural network-based approaches [1], [2].”
Support vector machines in remote sensing: A review (2011, unknown): “Bazi and Melgani (2006) investigated the most appropriate feature subspace and model selection based on a genetic optimization framework using three feature selection methods including steepest ascent, recursive feature elimination technique, and the radius margin bound minimization method.”
Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization (2008, methodology): “D object recognition [12], biomedical imaging [13], image compression [14], and remote sensing [15], [16].”
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles (2008, methodology): “…contain many copies of the same information and a feature extraction step may ﬁnally be needed to ensure correct classiﬁcation on every data set, which is conﬁrmed by the experiments (Usefulness of features reduction for the classiﬁcation of remote sensing data with SVM was also assessed in [40]).”