Wireless rechargeable sensor networks (WRSNs) have overcome the energy limitation bottleneck through wireless power transfer (WPT) technology. Traditional research has primarily focused on enhancing charging efficiency, while the critical issue of location privacy security arising from wireless charging has received scant attention. Additionally, sensors are vulnerable to detection and harm by malicious attackers, posing a significant threat to network integrity. In this paper, we propose two attack schemes, termed Least Squares Method (LSM) attack model and Centroid Method (CM) attack model for compromising sensor location privacy by exploiting charging power information and mobile charger behaviors. To counter such threats, we develop a scheme aimed at maximizing the node location privacy protection capabilities of the network. We propose a theoretical analysis to exploit the features of the proposed scheme. Finally, extensive test-bed experiments and simulations have been conducted to validate the effectiveness of our algorithms. The results demonstrate that our algorithms can protect at least 78% of the nodes without significantly compromising their survival rate.

Most hyperspectral image (HSI) classification methods assume that all classes in the test set are present during training. However, in real-world applications, acquiring labeled training samples is challenging. As a result, it is difficult for the training dataset to cover all possible land cover types, leading to the generalized zero-shot learning (GZSL) problem. Recently, vision-language models (VLMs) have provided rich semantic priors for land cover classes, offering promising potential for GZSL. However, two fundamental gaps hinder their application to HSI classification: the task paradigm gap, arising from the difference between image-level VLMs and the pixel-level HSI classification task; and the knowledge gap, due to the inconsistency between VLM features and HSI spectral–spatial representations. To bridge both gaps, a novel framework leveraging VLM semantic priors for GZSL in HSI classification is proposed, primarily using pseudo-labeling technique to provide knowledge for unseen classes. Specifically, a pseudo-label generation and enhancement module enables a paradigm transition from image-level understanding to pixel-level classification by incorporating HSI’s spatial information. A pseudo-label correction module then refines noisy labels using spectral cues to address the knowledge gap. Finally, a global learning strategy integrates pseudo-label distillation, supervised learning, and feature regularization to classify seen classes while enabling generalization to unseen ones. Experiments on benchmark HSI datasets demonstrate the proposed method’s superiority in generalized zero-shot classification. This work highlights the potential of VLMs in advancing HSI classification in practical applications.

The target classification of synthetic aperture radar (SAR) is an important technique of SAR image processing. Recently, many deep convolutional neural network (CNN)-based methods have been proposed for SAR target classification. However, feature extraction abilities of these CNN-based methods are insufficient. On the other hand, attention-based methods (e.g., swin Transformer) have the advantage of capturing the local-global features of images. Moreover, there always exists inherently noise in SAR images influenced by the process of emitted pulses, which hinders the improvement of accuracy for SAR target classification. To solve the both problems, this study explores a swin Transformer with improved blind-spot network (STr-BS) to alleviate the bad influence caused by speckle noise in SAR image and enhance the classification result. Specifically, the denoising process in STr-BS designs an improved blind-spot network in unsupervised setting without requiring the clean SAR images as input. Then, the outputs of the improved blind-spot network are as the input of the swin Transformer for the subsequent local-global feature extraction. The proposed STr-BS is tested on two public datasets (MSTAR and OpenSARShip), and the experimental results demonstrate the effectiveness of the proposed methods in comparison to other state-of-the-art approaches.

Transformer has been widely used in hyperspectral image (HSI) classification tasks because of its ability to capture long-range dependencies. However, most Transformer-based classification methods lack the extraction of local information or do not combine spatial and spectral information well, resulting in insufficient extraction of features. To address these issues, in this study, a dual-branch masked Transformer (Dual-MTr) model is proposed. Masked Transformer (MTr) is used to pretrain vision transformer (ViT) by reconstruction of both masked spatial image and spectral spectrum, which embeds the local bias by the process of recovering from localized patches to the global original input. Different tokenization methods are used for different types of input data. Patch embedding with overlapping regions is used for 2-D spatial data and group embedding is used for 1-D spectral data. Supervised learning has been added to the pretraining process to enhance strong discriminability. Then, the dual-branch structure is proposed to combine the spatial and spectral features. To strengthen the connection between the two branches better, Kullback-Leibler (KL) divergence is used to measure the differences between the classification results of the two branches, and the loss resulting from the computed differences is incorporated into the training process. Experimental results from two hyperspectral datasets demonstrate the effectiveness of the proposed method compared to other methods.

With the increasing availability and openness of remote sensing (RS) data collected from diverse sensors, there has been a growing interest in multimodal RS data classification. Nowadays, in the area of deep learning, there is a paradigm shift with the rise of foundation models, which are trained on large-scale datasets and are adaptable to a wide range of downstream tasks. In this study, the potential and effectiveness of foundation models for multimodal RS data classification is investigated. The training datasets of foundation models and multimodal RS datasets are quite different, and therefore, it is difficult to use a pretrained foundation model for multimodal RS data classification directly. To mitigate this difficulty, this article proposes a foundation model adaptation (FMA) framework for multimodal RS data classification without fine-tuning the parameters. Specifically, two learnable modules, i.e., cross-spatial interaction module and cross-channel interaction module, are proposed to add to the foundation model for extracting multimodal-specific representations. The cross-spatial and cross-channel interaction modules extract the characteristics of unimodal features along the spatial dimension and channel dimension, respectively. To effectively tackle the disparities among various RS modalities, an alignment approach (FMA2) is further explored based on the FMA. The FMA2 describes dependencies between different modalities by establishing a coupling score function, which can further enhance classification performance. To demonstrate the effectiveness and superiority of the FMA framework, comprehensive experiments are conducted on three multimodal RS datasets, showing improvement over the advanced multimodal RS data classification image methods.

Recently, deep learning models have dominated hyperspectral image (HSI) classification. Nowadays, deep learning is undergoing a paradigm shift with the rise of transformer-based foundation models. In this study, the potential of transformer-based foundation models, including the vision foundation model (VFM) and language foundation model (LFM), for HSI classification are investigated. First, to improve the performance of traditional HSI classification tasks, a spectral-spatial VFM-based transformer (SS-VFMT) is proposed, which inserts spectral-spatial information into the pretrained foundation transformer. Specifically, a given pretrained transformer receives HSI patch tokens for long-range feature extraction benefiting from the prelearned weights. Meanwhile, two enhancement modules, i.e., spatial and spectral enhancement modules (SpaEMs$\backslash $SpeEMs), utilize spectral and spatial information for steering the behavior of the transformer. Besides, an additional patch relationship distillation strategy is designed for SS-VFMT to exploit the pretrained knowledge better, leading to the proposed SS-VFMT-D. Second, based on SS-VFMT, to address a new HSI classification task, i.e., generalized zero-shot classification, a spectral-spatial vision-language-based transformer (SS-VLFMT) is proposed. This task is to recognize novel classes not seen during training, which is more meaningful as the real world is usually open. The SS-VLFMT leverages SS-VFMT to extract spectral-spatial features and corresponding hash codes while integrating a pretrained language model to extract text features from class names. Experimental results on HSI datasets reveal that the proposed methods are competitive compared to the state-of-the-art methods. Moreover, the foundation model-based methods open a new window for HSI classification tasks, especially for HSI zero-shot classification.

Several fundamental remote sensing (RS) image processing tasks, including classification, segmentation, and detection, have been set to serve for manifold applications. In the RS community, the individual tasks have been studied separately for many years. However, the specialized models were only capable of a single task. They lacked the adaptability for generalizing to the other tasks. Moreover, Transformer exhibits a powerful generalization capacity because it has the property of dynamic feature weighting. Hence, there is a large potential of a uniform Transformer to learn multiple tasks simultaneously, i.e., multi-task learning (MTL). An MTL Transformer can combine knowledge from different tasks by sharing a uniform network. In this study, a general-purpose Transformer, which simultaneously processes the three tasks, is investigated for RS MTL. To build a Transformer capable of the three tasks, an MTL framework named RSCoTr is proposed. The framework uses a shared encoder to extract multi-scale features efficiently and three task-specific decoders to obtain different results. Moreover, a flexible training procedure named co-training is proposed. The MTL model is trained with multiple general data sets annotated for individual tasks. The co-training is as easy as training a specialized model for a single task. It can be developed into different learning strategies to meet various requirements. The proposed RSCoTr is trained jointly with various strategies on three challenging data sets of the three tasks. And the results demonstrate that the proposed MTL method achieves state-of-the-art performance in comparison with other competitive approaches. Code will be available at https://github.com/Li-Qingyun/RSCoTr.

In recent years, deep learning models have been widely used for hyperspectral image (HSI) classification and most of existing deep learning-based methods merely focused on high classification accuracy. However, in real applications, classification with low uncertainty matters as much as accurate classification. Unfortunately, existing methods fail to consider uncertainty. To tackle this challenge, for the first time, Bayesian deep learning (BDL) is investigated to analyze the model uncertainty for HSI classification. Specifically, first, at the feature extraction stage, an HSI classification framework based on BDL, which contains two Bayesian Gabor layers and a global pooling layer (i.e., BDL-G2), is proposed. In BDL-G2, parameters in Gabor layers are sampled from the Gaussian distribution. The proposed BDL-G2not only provides the uncertainty estimation, but also strengthens the structure characteristic (i.e., texture) of HSI. Second, to model the uncertainty at the final classification stage, BDL-G2is combined with a Bayesian fully-connected layer (i.e., BDL-G2-BFL), where the parameters’ distribution is adjusted adaptively. In the proposed BDL-G2-BFL, the uncertainty at feature extraction and classification stages are both captured, and a whole uncertainty estimation framework is established. Experimental results on the three public HSI datasets demonstrates the superiority in both accuracy and uncertainty. The proposed Bayesian deep learning-based methods pioneer a new direction and provide useful inspiration and experience for practical applications.

Recently, due to the powerful capability at modeling the long-range relationships, Transformer-based methods have been widely explored in many research areas including hyperspectral image (HSI) classification. However, because of lots of trainable parameters and the lack of inductive bias, it is difficult to train a Transformer-based HSI classifier, especially when the number of training samples is limited. To address this issue, in this study, spectral-spatial masked Transformer (SS-MTr) is explored for HSI classification, which uses a two-stage training strategy. In the first stage, SS-MTr pre-trains a vanilla Transformer via reconstruction from masked HSI inputs, which embeds the local inductive bias into the Transformer. In the second stage, the well pre-trained Transformer is cooperated with a fully connected layer and then fine-tuned for the HSI classification. Furthermore, in order to incorporate discriminative feature learning into the SS-MTr, three SS-MTr-based methods, including contrastive SS-MTr (C-SS-MTr), supervised SS-MTr (S-SS-MTr), and supervised contrastive SS-MTr (SC-SS-MTr) are proposed by adding extra branches for specific tasks in parallel with the existing reconstruction task. Specifically, the proposed C-SS-MTr adds a contrastive loss which brings instance discriminability. Besides, the proposed S-SS-MTr builds an extra classification branch for embracing inter-class discriminability and intra-class similarity. Moreover, the proposed SC-SS-MTr combines C-SS-MTr and S-SS-MTr for better generalization. The proposed SS-MTr, C-SS-MTr, S-SS-MTr, and SC-SS-MTr are tested on three popular hyperspectral datasets (i.e., Indian Pines, Pavia University, and Houston). The obtained results reveal that the proposed models achieve competitive results compared with the state-of-the-art HSI classification methods. Code is available at https://github.com/mengduanjinghua/SS-MTr.

Recently, deep convolutional neural networks (CNNs) have achieved high classification accuracy of hyperspectral image (HSI). However, high accuracy is not the only goal of a good HSI classifier. In real-world applications, it is necessary to tell whether the classifier is certain about its classification result, which is critical for the safe usage. Unfortunately, most of existing models do not consider the issue. In this study, uncertainty is estimated and reduced to build a trustworthy HSI classifier. Firstly, since the output probabilities of softmax layer cannot represent the confidence scores, distance measurement scheme is used to measure the confidence scores. And then, a trustworthy HSI classifier, which reduces the predictive uncertainty in CNN (i.e., PU-CNN), is obtained by minimizing the distance to the correct centroid. Secondly, the fact that a training sample of HSI usually contains many pixel vectors that belong to different classes, which brings label uncertainty. Then, label uncertainty CNN (i.e., LU-CNN), which uses a classifier-consistent estimator to recover the multiple classes in each HSI sample, is proposed. LU-CNN computes loss over candidate label sets to find the optimal classes, which leads to a trustworthy HSI classifier. Finally, the combination of PU-CNN and LU-CNN (i.e., PL-CNN) is proposed to address predictive uncertainty and label uncertainty at the same time. Experimental results on the three popular hyperspectral datasets show that the proposed methods yield improvements in both accuracy and confidence. The proposed trustworthy classifier opens a new window for safe usage of HSI.

In recent years, deep convolutional neural networks (CNNs) have been widely used for hyperspectral image (HSI) classification. The powerful feature extraction capability and high classification performance of CNN highly depend on sufficient training samples. Unfortunately, it is not a common situation because collecting training samples is time-consuming and expensive. In this letter, in order to make the most of deep CNN with limited training samples, dual-path siamese CNN (Dual-SCNN) is proposed for HSI classification. Specifically, the proposed classification framework is a combination of extended morphological profiles, CNN, siamese network, and spectral-spatial feature fusion. In order to solve the problem of insufficiency in hard negative pairs during the training of a siamese network, adversarial training is combined with Dual-SCNN (Dual-SCNN-AT) for HSI classification. Moreover, a data augmentation method titled mixup is combined with Dual-SCNN and Dual-SCNN-AT to further improve the classification performance of HSI. The obtained results on widely used hyperspectral data sets reveal that the proposed methods provide the competitive results in terms of classification accuracy, especially with limited training samples.