In this paper,a technique based on image pyramid and Bayes rule for reducing noise effects in unsupervised change detection is proposed.By using Gaussian pyramid to process two multitemporal images respectively,two im...In this paper,a technique based on image pyramid and Bayes rule for reducing noise effects in unsupervised change detection is proposed.By using Gaussian pyramid to process two multitemporal images respectively,two image pyramids are constructed.The difference pyramid images are obtained by point-by-point subtraction between the same level images of the two image pyramids.By resizing all difference pyramid images to the size of the original multitemporal image and then making product operator among them,a map being similar to the difference image is obtained.The difference image is generated by point-by-point subtraction between the two multitemporal images directly.At last,the Bayes rule is used to distinguish the changed pixels.Both synthetic and real data sets are used to evaluate the performance of the proposed technique.Experimental results show that the map from the proposed technique is more robust to noise than the difference image.展开更多
基金the National Basic Research Program(973) of China (No. 2006CB701303)the National High Technology Research and Development Program(863) of China (No. 2006AA12Z105)
文摘In this paper,a technique based on image pyramid and Bayes rule for reducing noise effects in unsupervised change detection is proposed.By using Gaussian pyramid to process two multitemporal images respectively,two image pyramids are constructed.The difference pyramid images are obtained by point-by-point subtraction between the same level images of the two image pyramids.By resizing all difference pyramid images to the size of the original multitemporal image and then making product operator among them,a map being similar to the difference image is obtained.The difference image is generated by point-by-point subtraction between the two multitemporal images directly.At last,the Bayes rule is used to distinguish the changed pixels.Both synthetic and real data sets are used to evaluate the performance of the proposed technique.Experimental results show that the map from the proposed technique is more robust to noise than the difference image.
