Deep neural networks (DNNs) have achieved remarkable progress in remote sensing image (RSI) analysis, yet their vulnerability to subtle adversarial perturbations poses a critical threat to safety-critical applications such as environmental monitoring. While black-box attacks have garnered attention for their practicality, existing methods face a dilemma in RSI scenarios: restricted attacks often result in compromised image quality and limited stealthiness, whereas unrestricted attacks risk degrading transferability. To address this challenge, this paper proposes the latent-space adversarial diffusion attack framework (LADA), which focuses on balancing stealthiness and transferability in adversarial attacks against RSI models. LADA employs a pre-trained diffusion model to map high-resolution RSIs into a low-dimensional latent space, enabling semantic-level perturbation optimization while avoiding pixel-wise explicit noise. Additionally, text prompts are automatically generated using a large multimodal model to guide adversarial sample synthesis, ensuring semantic consistency. To enhance perturbation search efficiency in the latent space, a hybrid strategy combining multi-scale sampling and covariance matrix adaptation evolution strategy is introduced. Extensive experiments demonstrate that LADA achieves superior performance across multiple RSI datasets, model architectures, and defense mechanisms. This paper establishes a benchmark for high-stealthiness and high-transferability adversarial attacks, advancing the secure deployment of DNNs in remote sensing applications.

We consider the black-box adversarial attack problem in the field of remote sensing images (RSIs) to reveal the vulnerabilities of various deep neural networks (DNNs), including classification and semantic segmentation models. Existing adversarial attack methods typically focus solely on maximizing attack success rates (ASRs) under given perturbation constraints, neglecting the differences between adversarial samples and clean images. We propose a batchwise amplitude spectrum normalization (BAMPN) method, which is a plug-and-play and transfer-based black-box attack strategy. Using Fourier transform, we convert RSIs from the spatial domain into the frequency domain to obtain the amplitude spectrum, which is normalized within the batch. Moreover, we use a moving average strategy to retain the historical amplitudes of the batch, enhancing input diversity. By mixing the low-level statistical features of RSIs, BAMPN reduces the differences between adversarial samples and clean images while improving attack transferability. In addition, BAMPN is applicable not only to RSIs classification tasks but also directly to semantic segmentation tasks, as it preserves the spatial semantic structure of RSIs. We conduct extensive experiments using 20 DNN models and four benchmark RSIs’ datasets, comparing our method against 14 state-of-the-art (SOTA) approaches. The results demonstrate that BAMPN achieves superior attack performance while ensuring a promising similarity between adversarial and clean samples.

In the remote sensing community, heterogeneous images captured by various observation platforms usually exhibit different visual appearances and statistical distribution characteristics, making it difficult to detect land surface changes through direct comparison. Most existing change detection (CD) methods aim to extract change information by transforming heterogeneous images into a common image domain or high dimensional feature space for change analysis, neglecting the deep mining of image domain-invariant features. To overcome this challenge, a novel unsupervised cross-domain difference representation learning (CDRL) framework is proposed for heterogeneous CD, including an image translation network and a CD network. First, the image translation network combines within-domain self-reconstruction and cross-domain image translation with CD constraints to enable joint optimization, thereby constructing a style-independent, content-comparable feature space for obtaining difference information between heterogeneous images. Subsequently, patch-based samples with reliable labels are selected by analyzing the difference information through thresholding. On this basis, a simple yet efficient CD network is established by partially reusing the content feature extraction module and incorporating the difference information fusion module. This design enables the network to effectively learn the semantics of both changed and unchanged pixels, thus accurately identifying the ground changes. Extensive experimental results on five different types of datasets demonstrate the effectiveness of the proposed method, showing remarkable improvements over state-of-the-art approaches in terms of accuracy and efficacy. The code and dataset are available at https://github.com/OMEGA-RS/CDRL.

The application of remote sensing (RS) for building change detection (BCD) is indispensable for assessing shifts in land use and surface dynamics. Nevertheless, off-the-shelf deep learning-based BCD methods often suffer from incomplete change boundaries and pseudo changes, due to the insufficient utilization of difference information from bitemporal remote sensing images. To address these issues, we propose a difference-aware multiscale feature aggregation network (DMFANet) aimed at investigating more representative forms of change representation in BCD tasks. To facilitate comprehensive bitemporal feature alignment and differencing, the feature modulation module (FMM) is designed, which focuses on developing semantically robust and contextually enriched pyramidal feature representations by channel-spatial modulation. Subsequently, the cross-domain difference enhancement module (CDEM) is introduced to accurately locate changed building areas with intricate details by capturing multi-perspective difference information through the construction of different domain dependencies. Moreover, we propose a multi-scale context aggregation module (MCAM) to effectively adapt to building scale variations by aggregating multiscale difference features under the guidance of contextual information while mitigating the interference of redundant difference information. The empirical outcomes firmly confirm the superiority of our stream-lined network over nine cutting-edge approaches on the LEVIR-CD, WHU-CD, and SYSU-CD benchmarks, excelling both in terms of accuracy and efficiency. Code and pretrained models are accessible at https://github.com/SallyRonionGit/DMFANet.

Federated learning (FL) enables collaborative model training across massively distributed edge devices, such as Internet of Things (IoT) nodes. However, resource constraints impose a major challenge, as there exists a trade-off between maximizing learning accuracy and minimizing communication overhead between the resource-limited devices. In this paper, we present a device selection approach for heterogeneous FL systems based on multi-objective optimization and knowledge transfer. We formulate the resource constraint in federated optimization as a multi-objective problem, and obtain Pareto-optimal solutions balancing resource efficiency and test accuracy. Additionally, we introduce an innovative knowledge transfer mechanism that propagates the globally optimal models obtained during multi-objective optimization to subsequent FL tasks, further expediting convergence. The multi-objective formulation and knowledge transfer provide new insights into efficient and robust federated learning for resource-constrained IoT applications. We conduct extensive experiments on real-world datasets. Results demonstrate that our method achieves up to 11% higher accuracy than state-of-the-art methods, while effectively mitigating resource constraints. Impact Statement–Federated learning is an efficient algorithm that enables everything to be interconnected without sharing data. However, resource constraint is the main challenge for federated optimization problems. Although many works have proposed various solutions from different perspectives, these methods cannot simultaneously minimize the communication resource cost while ensuring algorithm performance. We propose an automatic device selection algorithm for federated systems based on multi-objective optimization and knowledge transfer. This work not only reduces the global resource usage rate of federated learning, but also enables it to converge quickly.

Change detection from synthetic aperture radar (SAR) imagery is a significant yet challenging task due to the intrinsic speckle noise and different object appearances in SAR images. Most deep learning-based techniques rely on sufficient labeled samples as supervision for training and lack the ability to identify subtle changes. In this letter, we propose an unsupervised neighborhood difference-based self-supervised network (NDSNet) to detect small changed areas in SAR images. Specially, an antinoise neighborhood differencing operator is designed to generate an initial difference image for segmentation into changed, uncertain, and unchanged classes. Then, a simple pseudo-Siamese network consisting of an encoder and a predictor is established to extract discriminative features from raw data based on contrastive learning, which takes unchanged paired image patches as input and is trained in a self-supervised learning (SSL) manner. On this basis, the encoder followed by two fully connected layers constitutes a classification network, which is trained by the samples belonging to changed and unchanged classes. Finally, the uncertain samples are assigned changed or unchanged labels by feeding them into the network, thus obtaining a binary change map. Experimental results on three SAR datasets confirm the superiority and robustness of the proposed method, achieving an overall accuracy of up to 99.8% in detecting small changed regions. Our codes are available athttps://github.com/OMEGA-RS/NDSNet.

Deep learning (DL) have recently achieved outstanding performance in change detection of multitemporal images. However, most existing DL-based change detection methods still suffer from the problem of insufficient labeled training samples. To overcome this limitation, an unsupervised superpixel-guided self-supervised learning network (S3Net) is proposed for detecting changes occurred on the land surface. By performing principal component analysis on two input images, a triple-channel pseudo-color image containing the main information of both images is first generated, which is used for superpixel segmentation to produce homogeneous image objects. Then, a siamese network composing of two identical subnetworks with shared weight based on transfer learning is trained for pretext task in a self-supervised learning way, aiming to obtain multiscale object-level spatial feature difference images. On this basis, a high-quality difference image is generated by incorporating the pixel-level and object-level difference information using a simple weighted fusion strategy, which can be analyzed by thresholding to produce the final binary change map. The experimental results on four real-world datasets from different sensors show that the proposed approach can obtain superior performance in comparison with several state-of-the-art change detection methods, which further demonstrates its effectiveness and practicability. We make our data and code publicly available (https://github.com/OMEGA-RS/S3Net_CD).

With the increasing popularity of deep learning, most recent developments of change detection (CD) approaches have taken advantage of deep learning techniques to improve the detection performance. However, it is usually necessary to elaborately design the network architecture and train the model with a large amount of labeled data, which are difficult to obtain in practice. To overcome these limitations, this letter proposed an unsupervised CD framework for high-resolution remote sensing images integrating transfer learning-based bilinear convolutional neural networks (BCNNs) and object-based change analysis. A difference image is first generated, which is used for the subsequent preclassification and superpixel segmentation. Then, two sets of superpixel samples with reliable labels derived from the bitemporal remote sensing images are input into two pretrained CNNs to extract representative features, respectively. On this basis, the matrix outer product is utilized to generate the combined bilinear features, which are input into the softmax classifier to discriminate the change and no-change information and thus obtaining the final change map by feeding all sample data into the well-trained model. The experimental results on three real data sets demonstrate the effectiveness and superiority of the proposed method over several existing CD approaches.

Hyperspectral images (HSIs) contain rich spectral signatures that reveal more image details and, thus, enable the detection of less noticeable changes on the ground. However, HSI-based change detection (CD) is susceptible to a large amount of irrelevant or noisy spectral and spatial information due to massive spectral bands. To address these issues, we propose a novel spectral and spatial attention network (S2AN) for HSI-based CD, which is capable to suppress CD-irrelevant spectral and spatial information via adaptive spectral and spatial attention mechanisms. S2AN takes as input the image patch from the difference map between two HSIs and outputs the status of change for the patch. Specifically, S2AN is composed of several repeated attention blocks, each of which contains the spectral attention (SpeA) module for directly calculating the attention score for each input channel, the Gaussian spatial attention (GSpaA) module that first constructs an adaptive Gaussian distribution and then samples it to derive the attention scores for each spatial position, and the convolutional feature extraction (CFE) module for extracting features from the attention-weighted input. It is worth mentioning that, in addition to the advantage of the attention, GSpaA also reduces the sensitivity of patch size for patch-based methods. To effectively train S2AN when facing insufficient labeled data, a semisupervised strategy that combines supervised and unsupervised methods to augment labeled training data is proposed. Experiments on several HSI datasets in comparison to existing methods show the superiority of S2AN.

Hyperspectral unmixing including endmember extration and abundance estimation has been investigated successively in recent years due to increasingly hyperspectral processing requirements. As one type of decision after solution paradigm, multiobjective endmember estimation method is able to obtain a set of Pareto optimal solutions, thus providing a wealth of information to determine the most representative endmembers. In addition, multiobjective optimization methods also have the characteristics of flexible modeling, excellent global convergence and commendable adaptability, etc. However, the evolutionary algorithms designed for this kind of method generally use little spatial-spectral information of the hyperspectral image. In this paper, we delve into the memetic strategy by exploiting the topological structure of hyperspectral data in the high-dimensional space to establish a vertex-directed multiobjective endmember estimation method, termed VD-MoEE. According to the frequently used linear mixture model, endmembers of hyperspectral images are generally distributed at the vertices of hyperspectral data manifold. Therefore, we design a vertex-directed local search operator to guide the search direction of the individuals in evolutionary algorithms. Experimental results on synthetic as well as real data sets demonstrated that the proposed VD-MOEE is able to achieve appealing performance in terms of the solution selecting and accuracy in comparison with several classic and state-of-the-art endmember estimation methods.

Effectively highlighting multiple changes in the earth surface from multi-temporal remote sensing images is a meaningful but challenging task. In order to reduce costs and ensure the performance, it is advisable to employ a semisupervised strategy to achieve this goal. As a discriminative joint classification task, semisupervised change detection aims to extract useful and discriminative features from a large amount of unlabeled data in addition to limited labeled samples. The discriminator of a well-trained generative adversarial network (GAN) is just right for this. Therefore, in this letter, we proposed a semisupervised GAN-based multiple change detection framework for multi-spectral images. First, the GAN is trained by all data without any prior information. Then, we combine two identical trained discriminators to construct a dual-pipeline joint classifier. Finally, the classifier is fine-tuned by a very small amount of labeled data to detect multiple changes. The superior performance of the proposed model over both real multi-spectral data sets demonstrates its robustness and effectiveness.

Hyperspectral imagery is always composed of mixed pixels because of the limited spatial resolution of a sensor and the macroscopic/microscopic mixture of distinct substances. The linear mixing model (LMM) is proven to be simple and effective in extensive literature when the macroscopic mixture dominates the mixing process. But when the photons undergo multiple reflections before reaching the sensor, the LMM becomes invalid. In this circumstance, the bilinear mixture model (Bi-LMM), which considers secondary reflections with a bilinear term, is a viable alternative. However, the bilinear term in most existing Bi-LMMs is constructed based on the pre-estimated endmembers, and thus, most Bi-LMMs focus mainly on the abundance estimation. This may lead to inaccurate estimation of endmembers and abundances for a given hyperspectral image. In this article, we propose a multiobjective endmember extraction (Bi-MoEE) method within the bilinear mixture paradigm, which considers each secondary reflection as a virtual endmember. Then, Bi-MoEE selects real and virtual endmembers from an extended spectral library consisting of a standard spectral library and their virtual products. By imposing some intuitive constraints, the solution space is greatly reduced, and the multipoint crossover and restricted bit-flip mutation operators are specially designed. Finally, Bi-MoEE can efficiently obtain a set of tradeoff solutions by minimizing the unmixing residuals and the number of selected endmembers, and automatically determine the optimal solution with multiobjective decision-making techniques. Compared with some advanced endmember extraction methods, the proposed Bi-MoEE does not need to know the number of real endmembers. In addition, the time efficiency of Bi-MoEE is mainly related to the image size and the algorithmic parameters, and has little to do with the size of spectral library, thus facilitating the practical implementation of Bi-MoEE with regard to the oversized spectral library. The experiments on synthetic and real data sets demonstrated the excellent performance of Bi-MoEE.

The rapid development of remote sensing technology has enabled the acquisition of very high spatial resolution (VHR) multitemporal images in Earth observation. However, how to effectively exploit these existing data to accurately monitor land surface changes is still a challenging task. In this article, we propose an unsupervised scale-driven change detection (CD) framework for VHR images by jointly analyzing the spatial-spectral change information, which combines the advantages of deep feature learning and multiscale decision fusion. First, a well pretrained deep fully convolutional network (FCN) is used to automatically extract the deep spatial context information from the acquired images. Then, the uncertainty analysis incorporating the deep spatial feature and the image spectral feature is implemented to generate a pseudobinary change map. On this basis, it is easy to choose suitable samples to train an excellent support vector machine (SVM) classifier, thus detecting changes occurred on the ground. In addition, the multiscale superpixel segmentation technique is introduced to make full use of the spatial structural information, which takes an image-object as the basic analysis unit. Finally, a robust binary change map with high detection precision can be achieved by merging the CD results obtained at different scales. The impressive experimental results on four real data sets demonstrate the effectiveness and flexibility of the proposed framework.

Due to the distinct statistical properties in cross-sensor images, change detection in heterogeneous images is much more challenging than in homogeneous images. In this letter, we adopt a conditional generative adversarial network (cGAN) to transform the heterogeneous synthetic aperture radar (SAR) and optical images into some space where their information has a more consistent representation, making the direct comparison feasible. Our proposed framework contains a cGAN-based translation network that aims to translate the optical image with the SAR image as a target, and an approximation network that approximates the SAR image to the translated one by reducing their pixelwise difference. The two networks are updated alternately and when they are both trained well, the two translated and approximated images can be considered as homogeneous, from which the final change map can be acquired by direct comparison. Theoretical analysis and experimental results demonstrate the effectiveness and robustness of the proposed framework.

In sparse hyperspectral unmixing, regularization methods inevitably suffer from the "decision ahead of solution" issue concerning the regularization parameter, which is not conducive to practical applications. To settle this issue, a two-phase multiobjective sparse unmixing (Tp-MoSU) approach has been proposed recently. However, Tp-MoSU has limited performance on high noise data and uses little spatial-contextual information in estimating abundances. To address the first problem, a tri-objective optimization model is established for each of the two phases to model mixed additive noise automatically. To address the second problem, a dual spatial exploiting objective is specially designed in the second phase to exploit similarity among adjacent pixels, which can improve the quality of estimated abundances. In addition, the memetic based evolutionary algorithms are elaborately modified for each of the two phases for better convergence. The experimental results on several representative data sets demonstrate that the proposed method performs better than Tp-MoSU in both of the two phases and completely better than some advanced regularization algorithms in abundance estimation under mixed additive noise.

With the rapid development of remote sensing technology, how to accurately detect changes that have occurred on the land surface has been a critical task, particularly when images come from different satellite sensors. In this letter, we propose an unsupervised change detection method for heterogeneous synthetic aperture radar (SAR) and optical images based on the logarithmic transformation feature learning framework. First, the logarithmic transformation is applied to the SAR image that aims to achieve similar statistical distribution properties as the optical image. Then, high-level feature representations can be learned from the transformed image pair via joint feature extraction, which are used to select reliable samples for training a neural network classifier. When it is trained well, a robust change map can be obtained, thus identifying changed regions accurately. The experimental results on three real heterogeneous data sets demonstrate the effectiveness and superiority of the proposed method compared with other existing state-of-the-art approaches.

With the rapid technological development of various satellite sensors, high-resolution remotely sensed imagery has been an important source of data for change detection in land cover transition. However, it is still a challenging problem to effectively exploit the available spectral information to highlight changes. In this paper, we present a novel change detection framework for high-resolution remote sensing images, which incorporates superpixel-based change feature extraction and hierarchical difference representation learning by neural networks. First, highly homogenous and compact image superpixels are generated using superpixel segmentation, which makes these image blocks adhere well to image boundaries. Second, the change features are extracted to represent the difference information using spectrum, texture, and spatial features between the corresponding superpixels. Third, motivated by the fact that deep neural network has the ability to learn from data sets that have few labeled data, we use it to learn the semantic difference between the changed and unchanged pixels. The labeled data can be selected from the bitemporal multispectral images via a preclassification map generated in advance. And then, a neural network is built to learn the difference and classify the uncertain samples into changed or unchanged ones. Finally, a robust and high-contrast change detection result can be obtained from the network. The experimental results on the real data sets demonstrate its effectiveness, feasibility, and superiority of the proposed technique.