Robust supervised image classifiers by spatial AdaBoost based on robust loss functions

Ryuei Nishii, Shinto Eguchi

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)

Abstract

Spatial AdaBoost, proposed by Nishii and Eguchi (2005), is a machine learning technique for contextual supervised image classification of land-cover categories of geostatistical data. The method classifies a pixel through a convex combination of a log posterior probability at the current pixel and averages of log posteriors in various neighborhoods of the pixel. Weights for the log posteriors are tuned by minimizing the empirical risk based on the exponential loss function. It is known that the method classifies test data very fast and shows a similar performance to the Markov-random-field-based classifier in many cases. However, it is also known that the classifier gives a poor result for some data when the exponential loss puts too big penalty for misclassified data. In this paper, we consider a robust Spatial boosting method by taking a robust loss function instead of the exponential loss. For example, the logit loss function gives a linear penalty for misclassified data approximately, and is robust. The Spatial boosting methods are applied to artificial multispectral images and benchmark data sets. It is shown that Spatial LogitBoost based on the logit loss can classify the benchmark data very well even though Spatial AdaBoost based on the exponential loss failed to classify the data.

Original languageEnglish
Article number59820D
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5982
DOIs
Publication statusPublished - Dec 1 2005
EventImage and Signal Processing for Remote Sensing XI - Bruges, Belgium
Duration: Sep 20 2005Sep 22 2005

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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