The methods of change detection based on high resolution images have important applications in land use/land change

water quality change

forest resource monitoring and military target detection. In this paper

we introduce a new object-oriented change detection method for two high-resolution remote sensing images

which combines neighborhood correlation images (NCI) and the minimum-redundancy-maximum-relevance feature selection (mRMR) together. Content of main experiments:In this paper

study area is located in Shunyi district

Beijing. We choose two rapideye images as experimental data and both of them are without any cloudy covering them. Comparisons with reviewed researches:These two image data both contain abundant kinds of object types while the number of object types for traditional change detection are relative small. We design three comparative experiments to verify the effectiveness of this method: 1) compare the results of using mRMR feature selection versus without using mRMR feature selection; 2) compare the results of using NCI and mRMR feature selection together versus only using NCI; 3) compare the results of using NCI and mRMR feature selection together versus only using mRMR feature selection. The results show that the effect of change detection combining NCI and the mRMR feature selection is better than only using NCI or only using mRMR feature selection

and is more superior than neither NCI nor mRMR was introduced. Meanings:The most innovative contents about this paper is the method about how to deal with features

involving how to condense features and how to deal with high dimension features. That means we can use much more knowledge of data mining and machine learning to deal with remote sensing change detection. However

we need use more high resolution images to test this method. In theory

effectiveness of this method for change detection does not be affected by using different high resolution remote sensing data sources; However

the reality needs to be further verified by experiments.