In many real world of remote sensing applications

one may be interested in identifying if the samples belong to one class from a remote sensing imagery. It is always expensive in terms of time and manpower to collect an exhaustive training sample set. Therefore

it is useful to design a classifier

by which a user needs only to collect training samples of the objective class for identifying if a pixel belongs to the class of interest or not. This is the technique of partially supervised classification. In this paper

we present an algorithm called Weighted Unlabeled Sample Support Vector Machine （WUS-SVM）

based on which a new partially supervised classification method is proposed. In this method

a certain amount of unlabeled samples is first randomly selected from the test set and labeled as other classes with different weights. Second

a primary classifier is defined by WUS-SVM

in which a hyperplane is constructed to separate training samples of objective class and other classes with low weighted error and large margin width. Third

the primary classifier is used to determine the class of unlabeled samples. Thus all training samples are labeled and again they are used as training samples of support vector machines to obtain a final classifier

which is used to classify other test samples. Experimental results with both simulated and real data sets show that the proposed method is very effective.