Complex street scene change detection based on segnet network and migration learning
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Abstract:
The use of multi-temporal panoramic block images is of great significance for monitoring urban development and assisting government decision-making. However, due to the influence of solar rays, ground spectrum and shooting angle during the process of collecting data, it is difficult to obtain high precision by traditional methods. Complex neighborhood changes information. To this end, this paper proposes a method for detecting image change in panoramic blocks based on Segnet and migration learning. Firstly, the data set "TSUNAMI" is pre-trained and the training set is classified and merged. Then, the Segnet network is used to semantically segment the panoramic block image, and the semantic segmentation result is subjected to difference calculation to obtain the change result map and evaluate the accuracy. Experiments were carried out to select two groups of panoramic block images. The maximum likelihood method, the support vector machine method and the method proposed in this paper were used to compare the two groups of data. The accuracy of the first group was 65.1%, 72.1% and 81.4%, respectively. The accuracy of the second group was 66.5%, 70.6%, and 82.2%, respectively. The experimental results show that the proposed method has higher detection accuracy and can provide technical support for urban violation investigation, post-disaster reconstruction, and ancient cultural relics restoration.
