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.
Border Feature Detection and Adaptation Algorithm and Its Application in Remote Sensing
2007-06-01
4142502 byte
Conference paper
Electronic Resource
English