This paper proposes an efficient unsupervised method for detecting relevant changes between two temporally different images of the same scene. A convolutional neural network (CNN) for semantic segmentation is implemented to extract compressed image features, as well as to classify the detected changes into the correct semantic classes. A difference image is created using the feature map information generated by the CNN, without explicitly training on target difference images. Thus, the proposed change detection method is unsupervised, and can be performed using any CNN model pre-trained for semantic segmentation.
翻译:本文建议了一种有效且不受监督的方法,用以探测同一场景两个时间上不同图像之间的相关变化。 实施了用于语义分解的进化神经网络(CNN),以提取压缩图像特征,并将所检测到的变化分类为正确的语义类。 使用CNN生成的地貌地图信息创建了差异图像,但没有对目标差异图像进行明确培训。 因此,拟议的变化检测方法不受监督,可以使用任何预先培训的CNN语义分解模型进行。