Border feature detection and adaptation algorithm and its application in remote sensing


Kasapoglu N. G. , Ersoy M. K.

3rd International Conference on Recent Advances in Space Technologies, İstanbul, Turkey, 14 - 16 June 2007, pp.341-342 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/rast.2007.4284008
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.341-342

Abstract

Defining decision region borders properly is a major task of classification algorithms. In this paper, the border feature detection and adaptation (BFDA) algorithm is introduced for this purpose. The BFDA is a novel classification scheme, especially useful for the classification of remote sensing images. The method exploits the powerful discrimination capability of the 1-Nearest Neighborhood (1-NN) method with the border feature vectors. The first part of the algorithm consists of generating border feature vectors using class centers and misclassified training vectors. With this approach, a manageable number of border feature vectors are obtained. The second part of the algorithm involves the adaptation of the border feature vectors with a technique similar to the learning vector quantization (LVQ) algorithm. The performance of the BFDA was compared with other classification algorithms including support vector machines (SVMs) and several statistical classification techniques.