An efficient and effective robust algorithm for the classification of Jakarta vegetation area

This paper discusses an efficient and effective robust algorithm applied to the classification of vegetation areas in the Jakarta Province. The input data is remote sensing data from the Landsat 7 Satellite. The classification process is guided over two steps, training and classification. The purpose of the training step is to determine the reference spectra of the vegetation area, and the purpose of the classification step is to classify Jakarta areas as either vegetation or nonvegetation. An efficient robust approach is used to classify the Jakarta area using the anomolous digital number resulting from a failed instrument. This paper discusses the application of an efficient and effective robust method to classify the remote sensing data with anomolous or inconsistent observations. The aim is to propose a new efficient subset robust approach - the subset minimum vector variance - to classify the vegetation area of Jakarta. The minimum vector variance (MVV) is a robust method having a minimum of the square of the length of a parallelotope diagonal.