NDLI: Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images

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Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping

Retrieval of leaf fuel moisture contents from hyperspectral indices developed from dehydration experiments

Classification of Tundra Vegetation in the Krkonoše Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

Spectral-Spatial Classification of Hyperspectral Imagery Based on Stacked Sparse Autoencoder and Random Forest

Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data

A lossless coding scheme for maps using binary wavelet transform

Sensitivity analysis of a scattering-based nonlocal means Despeckling Algorithm

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Spectral–spatial feature extraction using orthogonal linear discriminant analysis for classification of hyperspectral data

3Cat-1 project: a multi-payload CubeSat for scientific experiments and technology demonstrators

Object-based water body extraction model using Sentinel-2 satellite imagery

Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images

Adequacy of pseudo-direct georeferencing of terrestrial laser scanning data for coastal landscape surveying against indirect georeferencing

Decision fusion for hyperspectral image classification based on multiple features and locality-preserving analysis

Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

Towards improved land use mapping of irrigated croplands: performance assessment of different image classification algorithms and approaches

Level set incorporated with an improved MRF model for unsupervised change detection for satellite images

Combining nadir and oblique UAV imagery to reconstruct quarry topography: methodology and feasibility analysis

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Detection of ventilation corridors using a spatio-temporal approach aided by remote sensing data

Mapping land use with using Rotation Forest algorithm from UAV images

Preliminary results from active landslide monitoring using multidisciplinary surveys

Biophysical parameters mapping within the SPOT-5 Take 5 initiative

Preliminary considerations about costs and potential market of remote sensing from UAV in the Italian viticulture context

A novel method for urban land cover mapping based on new vegetation indices and texture-spectral information from fused visible and hyperspectral thermal infrared airborne data

A remote sensing-based discrimination of high- and low-potential mineralization for porphyry copper deposits; a case study from Dehaj–Sarduiyeh copper belt, SE Iran

Traffic data acquirement by unmanned aerial vehicle

GuidosToolbox: universal digital image object analysis

Hyperspectral anomaly detection based on spectral–spatial background joint sparse representation

Sea-ice area variability and trends in Arctic sectors of different morphology, 1996–2015

Comparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest

The geospatial modelling of carbon sequestration in Oluwa Forest, Ondo State, Nigeria

A decision-based multi-sensor classification system using thermal hyperspectral and visible data in urban area

Improvements of storm surge forecasting in the Gulf of Venice exploiting the potential of satellite data: the ESA DUE eSurge-Venice project

Inter-comparison of SMOS and AMSR-E soil moisture products during flood years (2010–2011) over Pakistan

Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases

Satellite SAR interferometric techniques in support to emergency mapping

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Land cover classification in Romanian Carpathians and Subcarpathians using multi-date Sentinel-2 remote sensing imagery

Detection of gravity waves in Meteosat imagery by grating cell operators

A new land-cover match-based change detection for hyperspectral imagery

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Composite indicator for monitoring of Norway spruce stand decline

Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery

Assessment of inter-annual forest production variations in Italy by the use of remote-sensing and ancillary data

Phenological analysis of the savanna–forest transition from 1981 to 2006 from Côte d’Ivoire to Benin with NDVI NOAA time series

The role of Remote Sensing in land degradation assessments: opportunities and challenges

Applicability of NDVI temporal database for western Himalaya forest mapping using Fuzzy-based PCM classifier

Land use/land cover change detection combining automatic processing and visual interpretation

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Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images

Content Provider	Taylor & Francis Online
Author	Zagajewski, Bogdan Raczko, Edwin
Abstract	Knowledge of tree species composition in a forest is an important topic in forest management. Accurate tree species maps allow for much more detailed and in-depth analysis of biophysical forest variables. The paper presents a comparison of three classification algorithms: support vector machines (SVM), random forest (RF) and artificial neural networks (ANN) for tree species classification using airborne hyperspectral data from the Airborne Prism EXperiment sensor. The aim of this paper is to evaluate the three nonparametric classification algorithms (SVM, RF and ANN) in an attempt to classify the five most common tree species of the Szklarska Poręba area: spruce (Picea alba L. Karst), larch (Larix decidua Mill.), alder (Alnus Mill), beech (Fagus sylvatica L.) and birch (Betula pendula Roth). To avoid human introduced biases a 0.632 bootstrap procedure was used during evaluation of each compared classifier. Of all compared classification results, ANN achieved the highest median overall classification accuracy (77%) followed by SVM with 68% and RF with 62%. Analysis of the stability of results concluded that RF and SVM had the lowest variance of overall accuracy and kappa coefficient (12 percentage points) while ANN had 15 percentage points variance in results.
Starting Page	144
Ending Page	154
Page Count	11
File Format	PDF HTM / HTML
DOI	10.1080/22797254.2017.1299557
Journal	European Journal of Remote Sensing
Volume Number	50
Issue Number	1
Language	English
Publisher	Taylor & Francis
Publisher Date	2017-03-09
Access Restriction	Open
Subject Keyword	Support vector machines Random forest Artificial neural networks Hyperspectral data Classification
Content Type	Text
Resource Type	Article

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