Border feature detection and adaptation algorithm and its application in remote sensing
3rd International Conference on Recent Advances in Space Technologies, İstanbul, Türkiye, 14 - 16 Haziran 2007, ss.341-342, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/rast.2007.4284008
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.341-342
- İstanbul Kültür Üniversitesi Adresli: Hayır
Özet
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.