A Class-Oriented Strategy for Features Extraction from Multidate ASTER Imagery

In this paper we propose a hybrid classification method, adopting the best features extraction strategy for each land cover class on multidate ASTER data. To enable an effective comparison among images, Multivariate Alteration Detection (MAD) transformation was applied in the pre-processing phase, because of its high level of automation and reliability in the enhancement of change information among different images. Consequently, different features identification procedures, both spectral and object-based, were implemented to overcome problems of misclassification among classes with similar spectral response. Lastly, a post-classification comparison was performed on multidate ASTER-derived land cover (LC) maps to evaluate the effects of change in the study area.

• Publication year

  2009

• Venue

  Remote Sensing

• Publication date

  2009-11-30

• Fields of study

  Computer Science, Environmental Science

• Identifiers
  DOI 10.3390/rs1041171
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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