Fine-grained target classification method based on deep clustering

To improve the recognition and classification ability of electro-optical system for dim and small targets, and reduce the dependence of algorithms on hardware platforms and data, an unsupervised classification method was proposed, namely the fine-grained classification method based on deep features clustering. Firstly, the targets were suggested by the extraction of shallow features such as contour, color and contrast. Then, after a super-resolution processing, a convolutional neural network was used to encode the deep features of the target. Furthermore, the principal component analysis based on attention mechanism was adopted to generate the characterization matrix. Finally, the clustering method was used to realize the fine-grained target classification. The experiments were set to verify the classification performance of the deep clustering method based on different neural networks on different data sets. The fine-grained classification performance based on ResNet-34 clustering method reached 92.71% on CIFAR-10 test set. The results show that the fine-grained target method based on deep clustering can achieve the same effect as the strong supervised learning method. In addition, the classification effect of different fine granularity can be realized according to the different numbers of cluster and selection of cluster grades.