A novel approach for feature extraction from a gamma-ray energy spectrum based on image descriptor transferring for radionuclide identification

This study proposes a novel feature extraction approach for radionuclide identification to increase the precision of identification of the gamma-ray energy spectrum set. For easier utilization of the information contained in the spectra, the vectors of the gamma-ray energy spectra from Euclidean space, which are fingerprints of the different types of radionuclides, were mapped to matrices in the Banach space. Subsequently, to make the spectra in matrix form easier to apply to image-based deep learning frameworks, the matrices of the gamma-ray energy spectra were mapped to images in the RGB color space. A deep convolutional neural network (DCNN) model was constructed and trained on the ImageNet dataset. The mapped gamma-ray energy spectrum images were applied as inputs to the DCNN model, and the corresponding outputs of the convolution layers and fully connected layers were transferred as descriptors of the images to construct a new classification model for radionuclide identification. The transferred image descriptors consist of global and local features, where the activation vectors of fully connected layers are global features, and activations from convolution layers are local features. A series of comparative experiments between the transferred image descriptors, peak information, features extracted by the histogram of the oriented gradients (HOG), and scale-invariant feature transform (SIFT) using both synthetic and measured data were applied to 11 classical classifiers. The results demonstrate that although the gamma-ray energy spectrum images are completely unfamiliar to the DCNN model and have not been used in the pre-training process, the transferred image descriptors achieved good classification results. The global features have strong semantic information, which achieves an average accuracy of 92.76% and 94.86% on the synthetic dataset and measured dataset, respectively. The results of the statistical comparison of features demonstrate that the proposed approach outperforms the peak-searching-based method, HOG, and SIFT on the synthetic and measured datasets.