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Improving Image Classification

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

Abstract

Multidate imagery and satellite image derivatives such as vegetation and texture indices have been reported to improve image classification. However, the increase in additional predictor variables has also resulted in high data dimensionality and redundancy. Feature selection and extraction can be used to reduce high data dimensionality and minimize redundancy. The purpose of this chapter is to test whether feature selection can improve image classification. In this chapter, image classification will be performed using two different approaches. First, image classification is performed using the random forests (RF) classifier and multiple data sets (that consist of multidate Landsat 5 TM imagery, and vegetation and texture indices). Second, image classification is performed using the RF classifier with feature selection and multiple data sets. While the tutorial exercises indicate that feature selection did not improve image classification accuracy, it reduced the number of predictor variables.

Keywords

High data dimensionality Redundancy Random forests (RF) classifier Feature selection 

Supplementary material

468277_1_En_5_MOESM1_ESM.zip (109 mb)
Supplementary material 1 (ZIP 111657 kb)

References

  1. Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(1–4):131–156CrossRefGoogle Scholar
  2. Kuhn M, Johnson K (2016) Applied predictive modeling. SpringerGoogle Scholar
  3. Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5:1205–1224Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

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

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