An assessment of support vector machines for land cover classification

By Chengquan Huang, L. S. Davis, John R. Townshend. 2002

469 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/d8b01abecedc2ea807dacad75be44d1f619cce4e

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In Media Technology

Citations in the corpus

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].”
Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? (2008, methodology): “This research confirms most of the findings published by Huang et al. (2002) with some notable differences.”
A hybrid SVM-FFA method for prediction of monthly mean global solar radiation (2015, methodology): “It has been proved that its applications show superior performance compared to prior developed methodologies such as neural network and other conventional statistical models (Vapnik et al. 1996; Joachims 1998; Collobert and Bengio 2000; Mukkamala et al. 2002; Huang et al. 2002; Sung andMukkamala 200”
Implementation of machine-learning classification in remote sensing: an applied review (2018, methodology): “Thus Huang, Davis, and Townshend (2002) concluded that the number of training samples needed may depend on the classi ﬁ cation algorithm, the number of input variables, and the size and spatial variability of the mapped area.”
Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach (2012, methodology): “SVM have been adapted to handle nonlinear boundaries between classes through the use of kernal functions; the non-linear kernals transform the training data into higher dimensional feature space where linear class separation is then possible (Huang et al., 2002).”
Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, background): “For example, SVMs have been reported to work better for text classification although this requires a lot of time for training [28].”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, unknown): “Case studies were assembled from 2002 to 2019, a period of 17 years, wherein the ﬁrst study using SVM dates back to 2002 [139] and 2005 for RFs [137].”
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): “The non-linear polynomial and radial basis kernels are the most commonly used functions on remotely sensed data (Huang, Davis, and Townshend 2002; Oommen et al. 2008).”
Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images (2017, background): “SVM is often reported to achieve better results than other classifiers (Ghosh et al., 2014; Huang, Davis, & Townshed, 2002; Sluiter & Pebesma, 2010).”
Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data (2010, background): “, in [5] and [6].”
Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020, methodology): “A small difference is found between the previous study and the present study on the accuracy levels of ANN, SAM, fuzzy ARTMAP and SVM [75,100,101].”
Support vector machines in remote sensing: A review (2011, unknown): “Moving towards medium resolution imagery (15–30 m pixel size), in one of the earlier investigations Huang et al. (2002) provided an accuracy evaluation of SVMs versus three other classifiers, namely a MLC, a three-layer (input, hidden and output) backpropagation neural network classifier (NN), and…”
Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping (2016, unknown): “For processing, we applied Support Vector Machine (SVM) classification, producing an overall class agreement of 74% ( Huang et al., 2004; Rabe et al., 2010).”
Support vector machine: A tool for mapping mineral prospectivity (2011, methodology): “The class in which each sample is identified to belong is labeled yi, which is equal to 1 for one class or is equal to 1 for the other class (i.e. yiA{ 1,1}) (Huang et al., 2002).”