Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features Academic Article


  • Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (α

publication date

  • 2015/5/1


  • Antennas
  • Backpropagation algorithms
  • Classifiers
  • Image classification
  • Image texture
  • Infrared radiation
  • Learning algorithms
  • Learning systems
  • Maximum likelihood
  • NDVI
  • Neural networks
  • Remote sensing
  • Wetlands
  • artificial neural network
  • image classification
  • imagery
  • machine learning
  • marsh
  • national park
  • near infrared
  • remote sensing
  • sedge
  • swamp
  • texture
  • void
  • wetland

International Standard Serial Number (ISSN)

  • 0167-6369

number of pages

  • 1

start page

  • 262