A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions

Abstract

Feature extraction is the key step of Inverse Synthetic Aperture Radar (ISAR) image recognition. However, limited by the cost and conditions of ISAR image acquisition, it is relatively difficult to obtain large-scale sample data, which makes it difficult to obtain target deep features with good discriminability by using the currently popular deep learning method. In this paper, a new method for low-dimensional, strongly robust, and fast space target ISAR image recognition based on local and global structural feature fusion is proposed. This method performs the trace transformation along the longest axis of the ISAR image to generate the global trace feature of the space target ISAR image. By introducing the local structural feature, Local Binary Pattern (LBP), the complementary fusion of the global and local features is achieved, which makes up for the missing structural information of the trace feature and ensures the integrity of the ISAR image feature information. The representation of trace and LBP features in a low-dimensional mapping feature space is found by using the manifold learning method. Under the condition of maintaining the local neighborhood relationship in the original feature space, the effective fusion of trace and LBP features is achieved. So, in the practical application process, the target recognition accuracy is no longer affected by trace function, LBP feature block number selection, and other factors, realizing the high robustness of the algorithm. To verify the effectiveness of the proposed algorithm, an ISAR image database containing 1325 samples of 5 types of space targets is used for experiments. The results show that the classification accuracy of the 5 types of space targets can reach more than 99%, and the recognition accuracy is no longer affected by the trace feature and LBP feature selection, which has strong robustness. The proposed method provides a fast and effective high-precision model for space target feature extraction, which can give some references for solving the problem of space object efficient identification under the condition of small sample data.

Ship target recognition based on superstructure matching

“…Automatic Target Recognition (ATR) using ISAR images has grown in recent years [1][2][3][4][5]. Most of them trained classifiers for aircraft and space target recognition, which requires many samples.…”

Domain Adaptive Few-Shot Learning for ISAR Aircraft Recognition with Transferred Attention and Weighting Importance

“…However, the lack of labeled samples in the training process often results in over-fitting under the condition of small training sets. Recently, a fast space target ISAR image recognition based on local and global structural feature fusion has been proposed [15]. Further, transfer learning also provides a solution by taking the deep CNN model as a feature extractor or for fine-tuning the weight in the new task to address the problem that the samples in the target classification task are too small to effectively train the deep CNN model [16].…”

Meta-Learner-Based Stacking Network on Space Target Recognition for ISAR Images

“…However, the algorithm does not consider the influence of translation, rotation and scale change of ISAR images on the classification effect. Sang-Hong Park used the polar mapping classifier (PMC) for classification in the reference [21]. Transferring the ISAR image to the polar coordinate system can convert the rotation of the image into the angular direction in the polar coordinate system.…”