Change Detection Method for High Resolution Remote Sensing Images Using Random Forest

Abstract

Studies based on object-based image analysis (OBIA) representing the paradigm shift in remote sensing image change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. This paper presents a novel RF OBIA method for high resolution remote sensing image CD that makes full use of the advantages of RF and OBIA. Firstly, the entropy rate segmentation algorithm is used to segment the image for the purpose of measuring the homogeneity of super-pixels. Then the optimal image segmentation result is obtained from the evaluation index of the optimal super-pixel number. Afterwards, the spectral features and Gabor features of each super-pixelareextracted and used as feature datasets for the training of RF model. On the basis of the initial pixel-level CD result, the changed and unchanged samples are automatically selected and used to build the classifier model in order to get the final object-level CD result. Experimental results on Quickbird, IKONOS and SPOT-5 multi-spectral images show that the proposed method out performs the compared methods in the accuracy of CD. (Less)