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.
Highlights
Inverse Synthetic Aperture Radar (ISAR) is all-day, all-weather, long-range, high-resolution two-dimensional imaging equipment, which plays an essential role in civil and military fields [1]
Considering that trace features belong to global structural features, in order to make up for lost trace feature information and ensure high classification accuracy, this paper further proposes to introduce local structural features and enhance trace features by complementary fusion of global and local features
When extracting the trace features of an image, the traditional method usually uses diametric functional P and circus functional C to calculate the result of the trace transform, thereby generating a small dimensional feature (1 × 1) [22, 23] that is invariant to rotation, translation, and scaling
Summary
ISAR is all-day, all-weather, long-range, high-resolution two-dimensional imaging equipment, which plays an essential role in civil and military fields [1]. Lee et al proposed to extract the small dimensions and highdiscriminant features of ISAR images by trace transformation, effectively overcoming the impact of spatial distribution changes of ISAR images on classification accuracy This method in practical application is still affected by many factors, such as trace functions, target types, ISAR imaging conditions, and noise types. Too many blocks will lead to high feature dimensions and low computational efficiency, and too few blocks will make the target background and noise dominant in the statistical characteristics of LBP features, affecting the classification accuracy To solve this problem, this paper further proposes to use the manifold learning method to fuse trace features and LBP features.
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