Automatic Selection of Classified Samples with the Help of Previous Land Cover Data

The combination of geographical knowledge and image calibration has long been the principal means of both the traditional visual interpretation and computer automatic classification in remote sensing mapping.Traditional visual interpretation could use the geographic knowledge well because of the artificial participation.However,it goes with the shortcomings that visual interpretation needs a lot of labor and is less efficient.In addition,the computer classification has not applied geographic knowledge in a proper way.Studies have shown that samples as the carrier of geographic knowledge can integrate geographic knowledge into the classification process to some extent.Meanwhile,unsupervised clustering can significantly improve the efficiency of sample selection and solve the problem of scarcity of samples in order to meet the requirement of distribution and purity.These studies provide a basic foundation for integration of geographic knowledge with computer classification.This paper presents an automatic sample selecting method which integrates image clustering with the aid of previous land cover data.The samples were selected automatically based on the TM images by the method mentioned above and used to classify the image later by the maximum likelihood classifier.We also classified the image using the manual samples by the maximum likelihood classifier in order to compare the classified results produced by these two kinds of samples.The test results indicated that the proposed method achieved an overall accuracy of 84.18% and a kappa coefficient of 0.8066 in seven categories,including water body,forest land,orchard and urban construction land.The method proposed in this paper is more efficient than the way of samples selected manually and provides better classification results.