Abstract: A method of enhancing an abnormal area of a ground-penetrating radar image based on hybrid-supervised learning includes the steps of: building a database including a real image set, a simulation image set and a simulation image label set; adopting a generative adversarial network; processing semi-supervised training and unsupervised training alternately to obtain a trained model, then inputting a real radar image with abnormal area that needs to be enhanced into the model and processing through the generative network to output an abnormal-area-enhanced image.

Abstract: A method of enhancing an abnormal area of a ground-penetrating radar image based on hybrid-supervised learning includes the steps of: building a database including a real image set, a simulation image set and a simulation image label set; adopting a generative adversarial network; processing semi-supervised training and unsupervised training alternately to obtain a trained model, then inputting a real radar image with abnormal area that needs to be enhanced into the model and processing through the generative network to output an abnormal-area-enhanced image.

Abstract: A method of locating a temperature anomalies of a distributed optical fiber includes the steps of: (a) generating a training dataset having training samples; (b) setting labels for training samples; (c) building a convolutional neural network composed of multi-layer convolutional networks and a fully connected layer, training to form a convolutional neural network model; (d) utilizing a fiber-optic temperature sensing system for measurement of testing object; (e) sending acquired data into the convolutional neural network model to obtain output features, then processing mapping and binarization; (f) offsetting the binary feature to obtain an offset feature and calculating a cosine similarity; and (g) obtaining a location of the abnormal temperature event by identifying the offset feature with a largest cosine similarity and identifying its location in the sequence P.