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Polarimetric SAR feature selection using a genetic algorithm

In English

Författare

Summary, in English

One of the main applications of polarimetric synthetic aperture radar (POLSAR) images is terrain classification. In this study, an algorithm is presented to extract optimized features of POLSAR images that are required for classification. The proposed algorithm involves three main steps: (i) feature extraction using decomposition algorithms, including both coherent and incoherent decomposition algorithms; (ii) feature selection using a combination of a genetic algorithm (GA) and an artificial neural network (ANN); and (iii) image classification using the neural network. The algorithm is applied to a data set composed of different land cover elements, such as manmade objects, oceans, forests, and vegetation. The classification results obtained by the GA-based feature selection method exhibit the highest accuracy. The best features from the extracted features were identified and used in the classification based on the proposed algorithm.

Publiceringsår

2011

Språk

Engelska

Sidor

27-36

Publikation/Tidskrift/Serie

Canadian Journal of Remote Sensing

Volym

37

Issue

1

Dokumenttyp

Artikel i tidskrift

Förlag

Taylor & Francis

Ämne

  • Physical Geography

Nyckelord

  • Remote sensing
  • SAR
  • Genetic algorithm (GA)
  • Artificial Intelligence (AI)

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1712-7971