In the field of synthetic aperture radar (SAR) target recognition, the recognition of aircraft targets has consistently presented a significant challenge. This is primarily due to the difficulty in obtaining SAR data of aircraft. In contrast, optical images of the target are easy to obtain. Consequently, the prevailing approach has been to translate target optical images into SAR images for target recognition. With the advancement of diffusion models, the quality and diversity of generated samples have continued to improve, rendering it possible to employ diffusion models for the translation of optical images to SAR. Additionally, the Schrödinger bridge constructs a transition between two arbitrary distributions, which can be utilized in the diffusion model to achieve optimal transport with two distributions. This paper proposes an innovative solution to achieve the translation from optical to SAR images by diffusion model based on the Schrödinger bridge. Concurrently, there is an unavoidable domain shift between the generated image and the measured SAR image. In order to narrow this domain shift, a domain adaptation method is employed. The experimental results demonstrate that the data generated by diffusion can effectively assist in the recognition of three types of aircraft targets, with an accuracy rate of 71.11%.
With the rapid development of artificial neural network (ANN), the field of synthetic aperture radar (SAR) target recognition has witnessed significant progress. However, due to the poor interpretability and ease of being affected by speckle noise, it brings challenges to ANN for SAR target recognition. Spiking Neural Network (SNN) has emerged as the third-generation neural network architecture and presents promising prospects for various applications. This study aims to explore the performance of SNN in SAR target recognition. In our experiments, we achieved comparable performance to conventional neural networks by utilizing directly trained SNN. This indicates the effectiveness of SNN in coping with SAR target recognition tasks. Moreover, we investigated the impact of different spiking encoders on SAR target recognition. Specifically, we compared the performance of SNN using the Poisson encoder and utilizing the first layer of the SNN as an encoder. This comparison provides valuable insights into the optimal coding strategy for SNN-based SAR target recognition. Additionally, we examined the robustness of SNNs in the presence of strong speckle noise. Our findings demonstrate that SNN can maintain good performance under the influence of strong speckle noise. The outcomes of this research shed light on the potential of SNN as a powerful tool for SAR target recognition. Future studies can focus on exploring SNN’s applicability to SAR Interpretation.
Outliers and occlusions are important degradation in the real application of point matching. In this paper, a novel point matching algorithm based on the reference point pairs is proposed. In each iteration, it firstly eliminates the dubious matches to obtain the relatively accurate matching points (reference point pairs), and then calculates the shape contexts of the removed points with reference to them. After re-matching the removed points, the reference point pairs are combined to achieve better correspondences. Experiments on synthetic data validate the advantages of our method in comparison with some classical methods.
Image matching has always been a very important research areas in computer vision. The performance will directly affect the matching results. Among local descriptors, the Scale Invariant Feature Transform(SIFT) is a milestone in image matching, while HOG as an excellent descriptor is widely used in 2D object detection, but it seldom used as a descriptor for matching. In this article, we suppose to pool these algorithms and we use a simple modification of the Rotation- Invariant HOG(RI-HOG) to describe the feature domain detected by SIFT. The RI-HOG is Fourier analyzed in the polar/spherical coordinates. Later in our experiment, we test the performance of our method on a datasets. We are surprised to find that the method outperforms other descriptors in image matching in accuracy.
Automatic image registration is a vital yet challenging task, particularly for non-rigid deformation images which are more complicated and common in remote sensing images, such as distorted UAV (unmanned aerial vehicle) images or scanning imaging images caused by flutter. Traditional non-rigid image registration methods are based on the correctly matched corresponding landmarks, which usually needs artificial markers. It is a rather challenging task to locate the accurate position of the points and get accurate homonymy point sets. In this paper, we proposed an automatic non-rigid image registration algorithm which mainly consists of three steps: To begin with, we introduce an automatic feature point extraction method based on non-linear scale space and uniform distribution strategy to extract the points which are uniform distributed along the edge of the image. Next, we propose a hybrid point matching algorithm using DaLI (Deformation and Light Invariant) descriptor and local affine invariant geometric constraint based on triangulation which is constructed by K-nearest neighbor algorithm. Based on the accurate homonymy point sets, the two images are registrated by the model of TPS (Thin Plate Spline). Our method is demonstrated by three deliberately designed experiments. The first two experiments are designed to evaluate the distribution of point set and the correctly matching rate on synthetic data and real data respectively. The last experiment is designed on the non-rigid deformation remote sensing images and the three experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm compared with other traditional methods.
There are two problems when associating multiple targets in remote sensing images: Firstly, with low temporal
resolution observation, the target's kinematic state cannot be estimated accurately and the classical Kalman filtering
association algorithms are no more applicable. Secondly, the classical image feature-based target matching algorithms
cannot deal with the illegibility of multiple targets' correspondence, which don't take into account the uncertainty of
feature extraction. To resolve above problems, a novel multiple targets association method based on Multi-scale
Autoconvolution(MSA) features matching and global association cost optimization through simulated annealing (SA)
algorithm is proposed. Experiments with remote sensing images show the applicability of the method for multiple targets
association.
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