Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks
This addresses the need for efficient change detection in remote sensing applications, but it is incremental as it adapts existing CNN techniques to an unsupervised setting.
The paper tackles the problem of detecting changes in satellite images without labeled data by using a convolutional neural network (CNN) for semantic segmentation to extract features and classify changes, resulting in an unsupervised method that can work with any pre-trained CNN model.
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.