Multiple Heterogeneous P-DCNNs Ensemble With Stacking Algorithm: A Novel Recognition Method of Space Target ISAR Images Under the Condition of Small Sample Set

In this paper, a novel method of multiple heterogeneous pre-trained deep convolutional neural network models (P-DCNN) ensemble with stacking algorithm is proposed, which can realize automatic recognition of space targets in inverse synthetic aperture radar(ISAR) images with high accuracy under the condition of the small sample set. In this method, transfer learning (TL) is introduced into the recognition of space targets in ISAR images for the first time, and the automatic recognition of space target ISAR images under a small sample set is realized. Besides, the stacking algorithm is used to realize the ensemble of multiple heterogeneous P-DCNNs, which effectively overcomes the limitations of the single weights fine-tuned P-DCNN (FP-DCNN), such as weak robustness and difficulty in classification accuracy. Firstly, the space target ISAR image data set after despeckling and standardization is divided into specific parts, and the training set of each part is augmented based on ISAR image transformation such as contrast adjustment, small-angle rotation, azimuth scaling, and range scaling. Then, multiple heterogeneous P-DCNNs are taken as the base learners in the first layer of the stacking ensemble learning framework (SELF), and fine-tuning training is carried out for each heterogeneous P-DCNN by using the augmented ISAR image dataset. Thus, the meta-features of ISAR images of space targets with stronger generalization are proposed. Furthermore, the XGBoost classifier is used as the meta-learner in the second layer of SELF, and the extracted meta-features of training data are used to train the meta-learner. Finally, the trained meta-learner is used to realize the automatic recognition of space targets in ISAR images. The experiment results show that the stacking algorithm can effectively realize the ensemble of multiple heterogeneous P-DCNNs, and the classification performance of the SELF is better than any single FP-DCNN.