Few-Shot Learning (FSL) methods have made significant advancements in natural optical image recognition. These methods rely on fine-tuning models that have already been extensively trained on large-scale datasets. However, pre-trained knowledge can mislead subsequent recognition processes. In the case of Synthetic Aperture Radar (SAR) FSL tasks, the pretraining stage usually uses self-supervised techniques to mitigate the issue of limited data, which can worsen the misguided judgment resulting from pre-trained knowledge. In this paper, a new framework, named Causal Meta-Learning (CML), is proposed to tackle this issue. Firstly, the FSL task is modeled as a causal graph from a causal inference perspective to clearly identify the bias introduced by pre-trained knowledge. Secondly, two distinct backdoor interventions are designed: intra-task and extra-task adjustments, to sever the direct linkage between pretrained knowledge and feature representations. Finally, a dedicated dataset, named mini-MSTAR, is reconstructed based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset to evaluate our framework. It's important to note that the contributions of CML are independent of existing meta-learning-based FSL methods, enabling CML to enhance all of them. Experiments conducted on mini-MSTAR demonstrate the improved recognition capabilities of several baseline models in 3 -way 1 -shot and 3 -way 5 -shot scenarios.

Deep learning has been widely used in the field of radar image classification because of its powerful feature extraction capabilities. In the land-sea clutter classification of sky-wave over-the-horizon radar (OTHR), deep learning methods perform poorly due to the radar receiver noise and the ionosphere. Addressing this challenge, a dual attention aided residual neural networks (DAAResNet) is proposed for OTHR land-sea classification. Leveraging prior knowledge that landsea clutter features predominantly cluster around the 0 Hz frequency, two attention mechanisms are introduced. Firstly, a channel attention module (CAM) is proposed, which directs the network’s focus towards critical channels. Secondly, a frequency attention module (FAM) is proposed, which directs attention towards pivotal frequencies. The classification performance of DAAResNet is validated on the original dataset and the scarce dataset. Experimental results show that DAAResNet outperforms state-of-the-art methods.

This paper presents recursive two-filter smoothing (TFS) in the criterion of maximizing the correntropy (MC) instead of minimizing the mean square error, to pursue robustness for outlier rejections caused by non-Gaussian noises and obtain high-precision state estimate, which is motivated by non-cooperative target backtracking. Here, non-cooperative target tracking often needs to consider non-Gaussian noises. The MC-based recursive TFS (abbreviated as MRTFS) is put forward, where both the forward and backward filters are performed independently and recursively in the criterion of MC. Meanwhile, an MC-based fusion rule is further designed to obtain the final smoothed estimate by fusing the forward filtered estimate and backward predicted estimate step by step, in order to improve estimation accuracy. A target backtracking example with non-Gaussian noises is simulated to show the advantage of estimation accuracy of the proposed MRTFS over Kalman filter/smoothers, MC-based Kalman filter/Rauch-Tung-Striebel smoother, in terms of different kernel bandwidths and levels of process noises.

In clustering process, fuzzy partition performs better than hard partition when the boundaries between clusters are vague. Whereas, traditional fuzzy clustering algorithms produce less interpretable results, limiting their application in security, privacy, and ethics fields. To that end, this paper proposes an interpretable fuzzy clustering algorithm—fuzzy decision tree-based clustering which combines the flexibility of fuzzy partition with the interpretability of the decision tree. We constructed an unsupervised multi-way fuzzy decision tree to achieve the interpretability of clustering, in which each cluster is determined by one or several paths from the root to leaf nodes. The proposed algorithm comprises three main modules: feature and cutting point-selection, node fuzzy splitting, and cluster merging. The first two modules are repeated to generate an initial unsupervised decision tree, and the final module is designed to combine similar leaf nodes to form the final compact clustering model. Our algorithm optimizes an internal clustering validation metric to automatically determine the number of clusters without their initial positions. The synthetic and benchmark datasets were used to test the performance of the proposed algorithm. Furthermore, we provided two examples demonstrating its interest in solving practical problems.

OWA operator is an effective aggregation method in multi-criteria decision making problem. However, in some multi-criteria decision making cases, the criteria satisfactions have some uncertainty, for instance, which is a set of interval values at a series of different levels. For multi-criteria decision making problem, it is necessary to aggregate criteria satisfactions. But the linear ordering of criteria satisfactions is unknown at a specific level in these cases. Therefore, OWA operator cannot be applied to aggregate the satisfactions directly. In this paper, a new method, named as the Interval Value Exceedance Method (IVEM), is proposed. By using the proposed method, the domination relationship of criteria satisfactions for each level can be obtained. Then OWA operator can be used to aggregate these satisfactions based on the domination relationship, even if the linear ordering of satisfaction is unknown.

It is a challenging problem for pattern classification with few labeled instances. Transfer learning provides an efficient solution to improve the classification accuracy using some training knowledge in the related domain (called source domain). Nevertheless, the single transformation in one direction may be uncertain in some cases, and this is harmful for classification. So we propose a new classification method based on the fusion of data transformations in different directions between source domain and target domain. At first, the mapping of target in the source domain is estimated by K-nearest neighbor technique using some one-to-one instance pairs, and the estimated mapping instance (pattern) can be classified in the source domain according to the available training data. Then, the credibility of classification result is evaluated. If the credibility achieves the expected threshold, the classification result is directly output. Otherwise, it indicates that the transformation may be not very reliable, and the labeled instances in source domain will be transferred to target domain for the classification of target. The two versions of classification results will be fused with different weights based on evidential reasoning, and the weighting factors are optimized using the available training instances. By doing this, we can efficiently reduce the uncertainty of transformation and improve the classification accuracy. Some real data sets from UCI have been employed to validate the effectiveness of the proposed by comparing with other related methods.

In the complex pattern classification problem, the fusion of multiple classification results produced by different attributes is able to efficiently improve the accuracy. Evidence theory is good at representing and combining the uncertain information, and it is employed here. Each attribute (set) can be considered as one source of evidence (information). In some applications, the observation of target attributes can be costly, and some unreliable information sources may harm the fusion result. Therefore, we want to use as few as possible sources of information with high quality to achieve the admissible classification accuracy. So we propose a new fusion method based on the adaptive selection of the information sources for pattern classification. For each pattern, the attribute (set) producing the highest accuracy among the various ones will be chosen to classify the pattern at first. If the reliability of classification result, which is evaluated by the K-nearest neighbors (K-NN) technique using training data, cannot satisfy the request, the next attribute source will be chosen according to its classification performance on the selected neighborhoods of the object. In the fusion, the classification results corresponding to different attributes are assigned different weights because of their different classification abilities, and the weighted evidence combination method is adopted to produce the best possible classification performance. Several real data sets from UCI have been used for the evaluation of the proposed method by comparison with other related fusion methods, and it shows that our new method can produce higher accuracy with smaller number of information sources than the other fusion methods which are directly used to combine all the sources of information.

In the complex pattern classification problem, the reliability of classifier output for the patterns located at different regions of the data set may be different. In order to efficiently improve the classification accuracy, we propose a new method to correct the original classifier output using the local knowledge of the classifier performance in different regions. The training data set can be divided into some small clusters corresponding to different regions. The prior knowledge of the classifier performance on each cluster is characterized by a confusion matrix representing the conditional probability of the pattern belonging to one class but committed to another class by the classifier. The matrix associated with each cluster is learnt by minimizing an error criteria using training data, which is assigned different weights to achieve the highest possible accuracy. If the classification accuracy of the training data in one cluster can be improved according to the corrected classification results, the associated confusion matrix becomes valid. Otherwise, the confusion matrix is invalid and patterns in this cluster cannot be modified any more. For each object, if it lies in the cluster with valid confusion matrix, its classification result will be corrected by the matrix before making the class decision. The above correction process can be regarded as the fusion of local and global information. Several experiments are given to test the performance of the proposed method using real data sets, and it shows that the new method is able to efficiently improve the classification accuracy compared with other related methods.

A new change detection method for heterogeneous remote sensing images (i.e. SAR & optics) has been proposed via pixel transformation. It is difficult to directly compare the pixels from heterogeneous images for detecting changes. We propose to transfer the pixels in different images to a common feature space for convenience of comparison. For each pixel in the 1stimage, it will be transferred to the 2ndfeature space associated with the 2ndimage according to the given unchanged pixel pairs. In fact, this transformation is done assuming that the pixel is not affected by the events. Then the difference value between the estimation of transferred pixel and the actual one in the same location of the 2ndimage can be calculated. The bigger difference value, the higher possibility of change happening. We can similarly do the opposite transformation from the 2ndimage to the 1stimage, and one more difference value is obtained in the 1stfeature space. Change occurrences will be detected using Fuzzy C-means clustering method based on the sum of two difference values. The flood detection in the SAR and optical images is given in the experiments, and it shows that the proposed method is able to efficiently detect changes.