Specific emitter identification (SEI) is a crucial task in various applications such as wireless communications and radar systems. The low-pass nature of vanilla Transformers hinders the extraction of high-frequency fingerprint features, resulting in poor SEI performance. Moreover, the introduction of additional high-frequency sensing structures can increase the computational efficiency of the already computationally intensive Transformer. To address these issues, we propose a high-frequency enhanced and low-complexity Transformer named HET. The framework integrates a multihead low-complexity self-attention (MLSA) module, a high-frequency enhanced connection, and a multihead high-frequency enhanced low-complexity self-attention (MESA) module. The MLSA module reduces the computational complexity by key and value mapping. The MESA and high-frequency enhanced connection module capture high-frequency information by reconstructing the low-frequency and high-frequency components of the features. We construct three HET variants, namely, $\text {HET}_{n}$ , $\text {HET}_{u}$ , and $\text {HET}_{m}$ , based on different enhancement methods and positions using $\text {MESA}_{n}$ , $\text {MESA}_{u}$ , and $\text {MESA}_{m}$ , respectively. Extensive experiments are conducted on the XSRP, ADS-B, and Wi-Fi datasets to evaluate the proposed models, demonstrating their competitive accuracy and faster throughput compared with popular methods. Theoretical proofs of high-frequency suppression and frequency response results confirm that the proposed framework has more gain for high-frequency information in SEI. Code is available at: https://github.com/zhailei-zl/HETmodel.

Sequence distance computation is a critical and fundamental task in many fields, such as bioinformatics, and time series analysis. Traditional functions for computing the distance between sequences are often based on dynamic programming to find a globally optimal alignment, which has quadratic complexity and is difficult to parallelize, thus limiting their application in large-scale datasets with long sequences. To solve this problem, various fields have designed some specialized models to approximate these distance functions inspired by deep representation learning, i.e., projecting the sequence into a geometric embedding space through an embedding function, so that the distance between sequences can be approximated by the distance in the high-dimensional embedding space, thereby reducing the quadratic complexity to linear. However, we note that even though the element types in sequence and distance functions are different across various fields, the core problem that needs to be solved remains the same. In this paper, we attempt to unify the sequence distance computation approximation from various fields and propose GnesDA. Specifically, we first unify the input representation of sequences in which the element type is the symbol and numeric values. We then encode the sequence using a convolutional block and a Transformer block sequentially, which can effectively capture local patterns and long dependencies respectively. Extensive experiments on four distance functions as well as four large-scale real-world datasets demonstrate that GnesDA achieves state-of-the-art in terms of both versatility and effectiveness. For the task of similarity retrieval, GnesDA can improve the edit distance, NW distance, DTW, and EDR by an average of 10.55%, 6.67%, 4.51%, and 12.00% on all metrics.

Cross-scene hyperspectral image (HSI) classification faces spectral-spatial feature distribution shifts resulting from cross-domain heterogeneity, which has become a critical challenge that urgently needs resolution in the field of remote sensing intelligent interpretation. To address this distribution shift, the mainstream approach is Domain Generalization (DG). However, existing HSI DG methods primarily focus on inter-class separability, while paying relatively less attention to cross-domain transferability. To overcome the limitation that existing methods mainly focus on inter-class separability, this study proposes a cross-scene HSI classification network, termed DPSKDnet. By synergistically employing dynamic perturbation-based destylization and self-knowledge distillation modeling mechanisms, DPSKDnet constructs domain-invariant representations with strong generalization capabilities in the feature space. Specifically, this study first builds a generator based on dynamic perturbation destylization to mine source domain (SD) invariant features and generate extended domain (ED) samples. Subsequently, a Fourier Augmentation Module is utilized to optimize the frequency domain representations of the SD, ED, and their combination-generated intermediate domain, obtaining frequency-enhanced representations. To effectively improve the model’s ability to capture domain-invariant features, a sample pair distillation loss is devised. This loss, informed by multi-domain mixed data input, guides the discriminator in online self-supervised learning. The overall accuracy of this method on Loukia, Houston2018, and Pavia Center increased by 0.48%, 0.81%, and 1.5%, respectively, compared to state-of-the-art methods. The code is available on the website: https://github.com/Yuhang-Hong/TGRS_DPSKDnet.

Biological sequence nearest neighbor search plays a fundamental role in bioinformatics. To alleviate the pain of quadratic complexity for conventional distance computation, neural distance embeddings, which project sequences into geometric space, have been recognized as a promising paradigm. To maintain the distance order between sequences, these models all deploy triplet loss and use intuitive methods to select a subset of triplets for training from a vast selection space. However, we observed that such training often enables models to distinguish only a fraction of distance orders, leaving others unrecognized. Moreover, naively selecting more triplets for training under the state-of-the-art network not only adds costs but also hampers model performance. In this paper, we introduce Bio-kNN: a kNN search framework for biological sequences. It includes a systematic triplet selection method and a multi-head network, enhancing the discernment of all distance orders without increasing training expenses. Initially, we propose a clustering-based approach to partition all triplets into several clusters with similar properties, and then select triplets from these clusters using an innovative strategy. Meanwhile, we noticed that simultaneously training different types of triplets in the same network cannot achieve the expected performance, thus we propose a multi-head network to tackle this. Our network employs a convolutional neural network(CNN) to extract local features shared by all clusters, and then learns a multi-layer perception(MLP) head for each cluster separately. Besides, we treat CNN as a special head, thereby integrating crucial local features which are neglected in previous models into our model for similarity recognition. Extensive experiments show that our Bio-kNN significantly outperforms the state-of-the-art methods on two large-scale datasets without increasing the training cost.

Deep learning methods have achieved outstanding performance in various signal tasks. However, due to degraded signals in real electromagnetic environment, it is crucial to seek methods that can improve the representation of signal features. In this paper, a Singular Value decomposition-based Attention, SVA is proposed to explore structure of signal data for adaptively enhancing intrinsic feature. Using a deep neural network as a base model, SVA performs feature semantic subspace learning through a decomposition layer and combines it with an attention layer to achieve adaptive enhancement of signal features. Moreover, we consider the gradient explosion problem brought by SVA and optimize SVA to improve the stability of training. Extensive experimental results demon-strate that applying SVA to a generalized classification model can significantly improve its ability in representations, making its recognition performance competitive with, or even better than, the state-of-the-art task-specific models.

Similarity search is a fundamental but expensive operator in querying trajectory data, due to its quadratic complexity of distance computation. To mitigate the computational burden for long trajectories, neural networks have been widely employed for similarity learning and each trajectory is encoded as a high-dimensional vector for similarity search with linear complexity. Given the sequential nature of trajectory data, previous efforts have been primarily devoted to the utilization of RNNs or Transformers. In this paper, we argue that the common practice of treating trajectory as sequential data results in excessive attention to capturing long-term global dependency between two sequences. Instead, our investigation reveals the pivotal role of local similarity, prompting a revisit of simple CNNs for trajectory similarity learning. We introduce ConvTraj, incorporating both 1D and 2D convolutions to capture sequential and geo-distribution features of trajectories, respectively. In addition, we conduct a series of theoretical analyses to justify the effectiveness of ConvTraj. Experimental results on four real-world large-scale datasets demonstrate that ConvTraj achieves state-of-the-art accuracy in trajectory similarity search. Owing to the simple network structure of ConvTraj, the training and inference speed on the Porto dataset with 1.6 million trajectories are increased by at least 240x and 2.16x, respectively.

Heterogeneous object-level change detection (CD) aims to detect the state of the objects and whether they have changed from multitemporal multimodal data. In this article, a new cross-modality interactive change detector (CICD) is proposed for object-level CD from multitemporal optical and synthetic aperture radar (SAR) images. The CICD consists of a backbone, cross-modal interactive module (CIM), neck, and head. CIM is designed to work with features extracted from modalities by the backbone network, enabling it to identify more changes in objects. Moreover, to address data imbalances in change categories caused by variations in satellite revisit cycles and aircraft flight plans, we introduce a heterogeneous class balanced module (HCBM). An eliminate adversarial network (EAN) is constructed as the main component of the HCBM. It is used to eliminate objects to augment images in which objects appear in only one temporal instant, thus reducing imbalances in the dataset. Extensive experiments are conducted on the multimodal object-level change dataset (MOCD), and the results show that CICD can achieve state-of-the-art performance.

Deep learning models (algorithms) have demonstrated their superior performance in interpreting Earth science and remote sensing data. However, adversarial examples generated with perturbations imperceptible to humans could render deep learning algorithms ineffective. This significant vulnerability of deep learning models, thus, inspires the exploration of defense methods resistible to adversarial examples. Although numerous countermeasures against adversarial examples have been proposed, the design of a universally applicable defense method across multiple scenarios still remains to be explored. In this study, we propose an effective denoising diffusion-based defense restore network (D3R-Net) based on the denoising diffusion model from the perspective of adversarial restoration, which transforms the adversarial examples into clean samples. Utilizing a highly effective denoising diffusion probabilistic model (DDPM), our D3R-Net transforms input adversarial examples into a state of noise, where diverse forms of adversarial noise transition into Gaussian noise. Subsequently, it captures semantic information through a series of iterative denoising steps. The pixel distribution of adversarial examples is restored in the proposed network to match the original distribution, enabling the classifier to identify adversarial examples correctly. Furthermore, we introduce a combined filtering module to preserve the semantic information of the original image, thereby further enhancing the defensive performance. Instead of modifying the model structure or excluding suspected samples, the proposed method restores the adversarial examples, making it simple yet effective and applicable to a broader range of scenarios. Extensive experiments are conducted on four benchmark datasets, and the results demonstrate that D3R-Net has significant defense capabilities against known and unknown attacks. Our source code is available athttps://github.com/SIM-xidian/D3R-Net.

In this paper, an Adaptive Fusion Transformer (AFT) is proposed for unsupervised pixel-level fusion of visible and infrared images. Different from the existing convolutional networks, transformer is adopted to model the relationship of multi-modality images and explore cross-modal interactions in AFT. The encoder of AFT uses a Multi-Head Self-attention (MSA) module and Feed Forward (FF) network for feature extraction. Then, a Multi-head Self-Fusion (MSF) module is designed for the adaptive perceptual fusion of the features. By sequentially stacking the MSF, MSA, and FF, a fusion decoder is constructed to gradually locate complementary features for recovering informative images. In addition, a structure-preserving loss is defined to enhance the visual quality of fused images. Extensive experiments are conducted on several datasets to compare our proposed AFT method with 21 popular approaches. The results show that AFT has state-of-the-art performance in both quantitative metrics and visual perception.

In this paper, we study a new variant of trajectory similarity search from the context of continuous query processing. Given a moving object from$s$to$d$, following a reference route$T_{r}$, we monitor the trajectory similarity between the reference route and the current partial route at each timestamp for online detour detection. We consider deviation calculation in both Euclidean space and road networks. Furthermore, we propose efficient incremental processing strategies to facilitate continuous query processing for moving objects. Our experiments are conducted on multiple real datasets and the experimental results verify the efficiency of our query processing algorithms.

In this paper, we study a new variant of trajectory similarity search from the context of continuous query processing. Given a moving object from$s$to$d$, following a reference route$T_r$, we monitor the trajectory similarity between the reference route and the current partial route at each timestamp for online detour detection. Since existing trajectory distance measures fail to adequately capture the deviation between a partial route and a complete route, we propose a partial trajectory similarity measure to bridge the gap. In particular, we enumerate all the possible routes extended from the partial route to reach the destination$d$and calculate their minimum distance to$T_r$. We consider deviation calculation in both euclidean space and road networks. In euclidean space, we can directly infer the optimal future path with the minimum trajectory distance. In road networks, we propose an efficient expansion algorithm with a suite of pruning rules. Furthermore, we propose efficient incremental processing strategies to facilitate continuous query processing for moving objects. Our experiments are conducted on multiple real datasets and the experimental results verify the efficiency of our query processing algorithms.