Change Detection Based on the Combination of Improved SegNet Neural Network and Morphology

Abstract

Through the analysis of satellite remote sensing image data, the identification of newly added buildings in the same area can be realized to judge the use of land. The identification of newly added buildings based on remote sensing images, involving image object extraction, semantic segmentation and change detection. The difficulty is not only to identify the changes of remote sensing images in different periods, but also to identify the newly added buildings with the original buildings. Both of the recognition effect and the detection precision of the traditional method based on mathematical modeling need to be improved. SegNet neural network is a kind of deep convolution neural network. It shows good performance in dealing with the task of semantic segmentation of single image, but it is directly applied to building change detection with low accuracy. The simulation results show that the improved SegNet neural network method improves the accuracy of the quantitative evaluation index F1 score by 8.6% compared with the conventional SegNet network in the newly added building detection effect in the same area in 2015 and 2017. In addition, the situation that the change detection result will produce a large number of noise, a combination of improved SegNet network and image morphological method is adopted to eliminate the noise and reduce the misjudgment. The simulation results show that the F1 index increased further by 1.4% on the basis of 8.6%.