Multi-stage Classification Algorithm Based on Multi-feature Tightly Coupled for Aerial Image

A multi-stage classification algorithm based on multi-feature tightly coupled for aerial image is proposed.Firstly,the sample images are transformed from RGB space to HSV space.Color moment feature and Gabor texture feature are extracted and PCA is used to reduce the feature dimensionality of Gabor texture.Secondly,tightly coupled matrix is gotten by multiplying the two characteristics and tightly coupled vector is generated.PCA is used again to reduce the feature dimensionality of tightly coupled vector.Then a multi-stage classifier is constructed by five probabilistic neural network classifiers.Lastly,images of Xi'an Qing Zhen Village with different time and scale are selected by Google Earth software.Grassland,lakes,trees,houses and land from the images are selected as training samples and test samples.And these samples are used for multi-stage classifier training and testing.Experimental results show that proposed method has higher classification accuracy compared with single feature classification method and the method of multi-feature loosely coupled.