Support vector machines in remote sensing: A review

By Giorgos Mountrakis, Jungho Im, Caesar Ogole. 2011

3,131 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/154507a7222c60380dd895d85171722548fbc81f

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Citations in the corpus

Hyperspectral image noise reduction based on rank-1 tensor decomposition (2013, methodology): “The support vector machine (SVM) algorithm (Mountrakis et al., 2011) was used as the classifier, and the number of training and test pixels used in the classification of the HYDICE data set are shown in Table 4.”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, methodology): “SVM is a statistical learning algorithm (Vapnik et al. 1997) which is an important method to estimate forest biophysical parameters using remote sensing data (Mountrakis et al. 2011; Marabel and Alvarez-Taboada 2013).”
Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers (2014, background): “In addition, Mountrakis, Im, and Ogole (2011) note that SVM reduces classification error on test data points without a prior assumption about their distribution.”
Analyzing High Resolution Topography for Advancing the Understanding of Mass and Energy Transfer Through Landscapes: A Review (2015, background): “…(Li et al., 2013; Garcia-Gutierrez et al., 2014), machine learning (Zhao et al., 2008, 2011; Gleason and Im, 2012b,a), and support vector machines (Mountrakis et al., 2011; Zhao et al., 2011; Brodu and Lague, 2012) also show great promise to represent discrete fea-735 tures within complex and…”
Estimating winter wheat (Triticum aestivum) LAI and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the PROSAIL model (2015, methodology): “…regressions provide a more robust model to generate inverse biophysical variables even with limited training data, but the major setback is the choice of kernels; some kernel functions may not provide optimal SVM configuration for remote-sensing applications (Mountrakis, Im, and Ogole 2011).”
Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model (2017, background): “Previous studies have indicated that support vector machines (SVMs) can perform well in solving high-dimensional and nonlinear classi ﬁ cation problems (Mountrakis et al .”
Implementation of machine-learning classification in remote sensing: an applied review (2018, methodology): “This includes a number of excellent review articles for speci ﬁ c methods, such as Mountrakis, Im, and Ogole (2011) review of support vector machines (SVMs), and Belgiu and Dr ă gu ţ (2016) article about Random Forests (RFs), as well as articles reviewing the process of classi ﬁ cation and its…”
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data (2019, positive): “Support vector machines (SVM) has been shown to outperform other classi ﬁ ers due to its overall high capacity to generalize complex features (Shao and Lunetta 2012; Mountrakis, Jungho, and Ogole 2011).”