Support vector machines for classification in remote sensing

By Mahesh Pal, Paul M. Mather. 2005

1,088 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/db68a79e59291b85e10300b79c43843b651aa195

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Citations in the corpus

Fusion of Support Vector Machines for Classification of Multisensor Data (2007, methodology): “Hence, the classical OAO approach seems appropriate here, which has been used successfully in several remote sensing studies [21], [22].”
The multispectral separability of Costa Rican rainforest types with support vector machines and Random Forest decision trees (2010, methodology): “…classification accuracy for all forest types using SVMs. SVM classifiers, detailed in §2, provide a robust mechanism to integrate field observations with spectral and spatial predictors for discriminating diverse land cover types (Huang et al. 2002, Pal 2005, Pal and Mather 2005, Boyd et al. 2006).”
Invasion of glossy privet (Ligustrum lucidum) and native forest loss in the Sierras Chicas of Córdoba, Argentina (2010, background): “The hyperplane that best discriminates two classes maximizes the distance between the hyperplane and the closest training samples (i.e., the support vectors, Burges 1998; Pal and Mather 2005 ).”
Land cover 2.0 (2018, positive): “…space relationships among classes, including arti ﬁ cial neural networks (ANN), support vector machines (SVM), and decision tree classi ﬁ ers, and have been shown to be more e ﬃ cient and accurate than parametric classi ﬁ ers (Pal and Mather 2005; Breiman et al. 1984; Mannan, Roy, and Ray 1998).”
A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, background): “…and Brodley, 1997; Mclver and Friedl, 2002; Jiang et al., 2012], random forests [Gislason et al., 2006], support vector machine [Gualtieri and Cromp, 1999; Huang et al., 2002; Pal and Mather, 2005; Marconcini et al., 2009], and genetic algorithms [Ishibuchi et al., 1994; Tseng et al., 2008].”
Implementation of machine-learning classification in remote sensing: an applied review (2018, methodology): “Machine-learning classi ﬁ cation has become a major focus of the remote-sensing literature (e.g. Pal and Mather 2003; 2005; Pal 2005; Mountrakis, Im, and Ogole 2011; Belgiu and Dr ă gu ţ 2016).”
Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images (2021, background): “The traditional RSI semantic segmentation algorithms are mostly machine learning approaches based on handcrafted features, such as support vector machines (SVMs), [7], [8], random forests (RFs), [9], and artificial neural networks (ANNs), [10].”
Support vector machines in remote sensing: A review (2011, neutral): “In a bid to compare SVMs against maximum likelihood and backpropagation artificial neural networks, Pal and Mather (2005) experimented on Landsat 7ETM + andhyperspectraldata.”
Measuring and modelling biodiversity from space (2008, background): “Contemporary approaches, such as those based on support vector machines (Pal and Mather, 2005) appear to offer many attractions, especially if resources for training the classifi er are limited (Foody et al.”
Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images (2006, methodology): “In [17], SVMs and two conventional classifiers (i.e., maximum likelihood and multilayer perceptron neural network classifiers) are compared experimentally based on two data sets acquired by the Landsat-7 Enhanced Thematic Mapper Plus and the hyperspectral Digital Airborne Imaging Spectrometer…”