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Image Classification

  • Courage KamusokoEmail author
Chapter
  • 841 Downloads
Part of the Springer Geography book series (SPRINGERGEOGR)

Abstract

Image classification refers to the procedure of assigning each pixel in an image to a particular land use/cover class of interest. The purpose of this chapter is to perform supervised classification using single date and multidate Landsat 5 TM imagery and machine learning methods. Five machine learning methods such as k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), single Decision Trees (DT), Support Vector Machines (SVM) and Random Forests (RF) machine learning classifiers will be used for image classification. The tutorial exercises show that multidate Landsat 5 imagery and the RF method provide relatively good results.

Keywords

Supervised classification k-nearest neighbors Artificial neural networks Decision trees Support vector machines Random forests 

Supplementary material

468277_1_En_4_MOESM1_ESM.zip (35.5 mb)
Supplementary material 1 (ZIP 36370 kb)

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Asia Air Survey Co., Ltd.KawasakiJapan

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