Aurora Image Classification and Retrieval Method Based on Deep Hashing Algorithm

It is of great significance to classify and retrieve the vast amount of aurora data with various forms and complex changes for the further study of the physical mechanism of the geomagnetic field and spatial information. In this paper, an end-to-end deep hashing algorithm for aurora image classification and retrieval is proposed based on the good performance of CNN in image feature extraction and the fact that hash coding can meet the retrieval time requirment of large-scale image retrieval. Firstly, Spatial Pyramidal Pooling(SPP) and Power Mean Transformtion(PMT) are embedded in Convolutional Neural Network (CNN) to extract multi-scale region information in the image. Secondly, a Hash layer is added between the fully connected layer to Mean Average Precision(MAP) the high-dimensional semantic information that can best represent the image into a compact binary Hash code, and the hamming distance is used to measure the similarity between the image pairs in the low-dimensional space. Finally, a multi-task learning mechanism is introduced to design the loss fuction by making full use of similarity informtion between the image label information and the image pairs. The loss of classification layer and Hash layer are combined as the optimization objective, so that a better semantic similarity between Hash code can be maintained, and the retrieval performance can be effectively improved. The results show that the proposed method outperforms the state-of-art retrieval algorithms on aurora dataset and CIFAR-10 datasets, and it can also be used in aurora image classification effectively.