A method of auto classification based on object oriented unsupervised classification

In order to achieve auto-classification for high resolution remote sensing image without any prior knowledge and further improve the efficiency and accuracy of automated classification,a new method of object-oriented unsupervised classification was presented in this paper.The detailed steps are as follows: First,the image was segmented into a series of segments which were composed of a cluster of contiguous and homogeneous pixels.Second,the object features of the segments were obtained(including spectral features,texture features and so on) through extracting features from the segments.And then by using the object feature we can cluster the segments using the Mahalanobis distance.Finally,through post-classification processing(including merging classes;adjusting misclassification),we can get the final classification results.The method was tested with an experiment and the result shows that the new proposed method could make use of more feature of image and reduce the number of cluster objects,so that both accuracy and efficiency of the auto classification have been much improved.