一种结合语义分割模型和图割的街景影像变化检测方法

由于街景影像具有地物尺度多样化、地物界限不明确、地物光谱信息复杂等问题，造成应用统计方法、机器学习等方法对复杂度高的街景影像变化检测性能欠佳. 因此提出一种结合语义分割模型和图割（GC）的街景影像变化检测方法. 该方法首先采用Camvid数据集训练DeeplabV3+网络得到的迁移学习模型对两个时期的街景影像进行语义分割；然后采用GC方法实现消除天空和植被等对街景影像的影响；接着采用变化向量分析（CVA）获取差异影像，最后对差异影像进行二值化和精度评价. 研究结果表明，提出的方法总体精度优于大津法（OTSU）、K均值法、Segnet网络迁移学习模型方法和DeeplabV3+网络迁移学习模型方法，是一种可行的街景影像变化检测方法. 

A street view image change detection method combining semantic segmentation model and graph cuts

Due to the problems of the diversity of the scale,the unclear boundary and the complex spectral information of the ground objects,the performance of the statistical method and machine learning method for the change detection of the high complexity street view images is poor.Therefore,a street view images change detection method combining semantic segmentation model and graph cuts(GC)is proposed in this paper.Firstly,the DeeplabV3+semantic segmentation model combined with migration learning is used to pre-train the Camvid data set to obtain the pre-trained model in this method;Then a small number of annotated samples from the data set of this paper were used to Fine Tune the pre-training model,which was respectively used for semantic segmentation of street view images in two periods.Then GC method is used to remove the sky,roads,vegetation and other factors,which is impacting on the street view.Finally,change vector analysis(CVA)is used to obtain the difference images,and binarization and accuracy evaluation were carried out for the difference images.The results show that the overall accuracy of the proposed method is better than the Otsu method(OTSU),K-means method,Segnet network migration learning method and DeeplabV3+network migration learning method,it is a feasible method for detecting changes in street view images.