Support vector machines in remote sensing: A review
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Summary
This paper reviews remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology, and hopes that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.
- Type
- review
- Published
- 2011-05-01
- Cited by
- 3,162
- References
- 152
- OpenAlex
- https://openalex.org/W2063907334
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2357415
Keywords
Support vector machine, Computer science, Implementation, Field (mathematics), Machine learning
References
- Classification using ASTER data and SVM algorithms;: The case study of Beer Sheva, Israel
- A MISR cloud-type classifier using reduced Support Vector Machines
- Capacity control in linear classifiers for pattern recognition
- Estimation of Dependences Based on Empirical Data
- Learning classifiers for science event detection in remote sensing imagery
- A tutorial on support vector regression
- Rule extraction from support vector machines: A review
- Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification
- Towards 3D map generation from digital aerial images
- Adaptive mapped least squares SVM-based smooth fitting method for DSM generation of LIDAR data
- Use of a dark object concept and support vector machines to automate forest cover change analysis
- Semisupervised PSO-SVM Regression for Biophysical Parameter Estimation
- An unsupervised method of classifying remotely sensed images using Kohonen self‐organizing maps and agglomerative hierarchical clustering methods
- Support vector machines for optimal classification and spectral unmixing
- Road centreline extraction from high‐resolution imagery based on multiscale structural features and support vector machines
- An automatic method for burn scar mapping using support vector machines
- Introduction to Linear Regression Analysis (3rd ed.)
- Using the one-dimensional S-transform as a discrimination tool in classification of hyperspectral images
- A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data
- Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas
Cited by
- Curvilinear Structures Segmentation and Tracking in Interventional Imaging
- Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models.
- A Rapid Response Electrochemical Biosensor for Detecting Thc In Saliva
- Active learning for semantic labelling of airborne LIDAR data
- Mapping eastern redcedar (Juniperus Virginiana L.) and quantifying its biomass in Riley County, Kansas
- Exploitation de séries temporelles d'images satellites à haute résolution spatiale pour le suivi des prairies en milieu agricole
- Ecological indicators, historical land use, and invasive species detection in the lower Iowa River floodplain
- Hyperspectral Remote Sensing of Crop CanopyChlorophyll and Nitrogen: The Relative Importance of Growth Stages
- Ensemble classifiers for land cover mapping
- Remote Sensing of Urbanization and Environmental Impacts
- Multi-Fidelity Construction of Explicit Boundaries: Application to Aeroelasticity
- Hyperspectral benthic mapping from underwater robotic platforms
- Support vector machine with adaptive composite kernel for hyperspectral image classification
- A spatial–temporal contextual Markovian kernel method for multi-temporal land cover mapping
- Multispectral and digital elevation model information fusion for classification and change detection in earth observation
- Improving the performance of extreme learning machine for hyperspectral image classification
- Retrieval of grassland plant coverage on the Tibetan Plateau based on a multi-scale, multi-sensor and multi-method approach.
- Evaluating the capability of machine-learning algorithms and object-oriented classification techniques using hyperspectral remote sensing for the discrimination of Australian native forest species in south-eastern Australia
- Radar and Optical Data Fusion for Object Based Urban Land Cover Mapping
- Application of spatial features to classification, segmentation and sharpening in remotely sensed images
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