The limited availability of annotated training data significantly constrains the classification accuracy of hyperspectral image (HSI) and LiDAR fusion approaches. Although contrastive learning has emerged as a potential solution, current implementations frequently neglect the critical class imbalance issues during unlabeled sample selection. To address the issue, we introduce a novel semi-supervised balanced contrastive learning (SemiBaCon) framework for multi-modal remote sensing image classification. First, we propose a superpixel-based balanced sampling (SPBS) mechanism that fundamentally addresses class imbalance through intelligent pseudo-label generation. By segmenting the HSI data into homogeneous superpixels and implementing intra-region label propagation, the method ensures statistically balanced pseudo-label selection across categories, effectively overcoming the bias introduced by conventional random sampling strategies. Second, our architecture integrates a dual-stream encoder combining convolutional neural networks (CNNs) with Transformers, enabling hierarchical feature extraction from spectral-spatial characteristics of HSI and elevation patterns of LiDAR. This design facilitates the construction of multi-modal positive sample pairs, achieving enhanced representation learning through inter-modal consistency constraints. Third, we develop a pseudo-label guided contrastive learning (PLCL) paradigm that synergistically combines pseudo-label confidence with feature similarity metrics, which effectively reduces intra-class variance and improves decision boundaries in the latent space. Comprehensive evaluations on three benchmark datasets demonstrate the framework’s superior performance compared to the state-of-the-art methods.

Deep learning techniques have recently achieved significant success in semantic segmentation for Earth observation tasks using single-modality data within a specific region. However, such optimal conditions are seldom found in real-world applications, leading to subpar performance and limited generalization of existing models when faced with multimodal cross-domain scenarios. To tackle this issue, we introduce a novel segmentation network for remote sensing imagery (RSI) called MC-Seg, which incorporates multilevel deep cross-fusion and adversarial domain adaptation. The MC-Seg framework begins by performing multilevel deep cross-fusion of multimodal data through a complete feature extraction module and three fusion levels: feature, modal, and layer. This approach ensures the thorough utilization of features from various modalities. Following this, the framework integrates a local-global adversarial domain adaptation module to minimize the domain gap, effectively aligning the source and target domains despite the differences in RSI data representations. Experimental validation on the C2Seg dataset shows that our proposed method outperforms existing state-of-the-art techniques.

Recent advances in hybrid architectures combining convolutional neural networks (CNNs) and Transformers have demonstrated significant potential in infrared small target detection. However, existing methods suffer from limited interaction depth, leading to weak cross-layer feature coordination. To address this, we propose the DIFTransNet, a dual-branch interactive fusion network that implements hierarchical mutual refinement between CNN and Transformer pathways. Specifically, the DIFTransNet incorporates an interactive fusion module (IFM) at each hybrid encoding stage that bridges local details and global semantics through shared latent space projection and cross-attention mechanisms, enabling aligned deep fusion. In the Transformer branch, we introduce the hierarchical decoupled sparse attention (HDSA) with resolution-progressive window partitioning, which preserves dim targets through dense local sampling while suppressing noise via cross-region sparse correlations. Moreover, rethinking the information gap in conventional skip connections between shallow and deep layers, we propose a reorganized skip augmentation module (RSAM). It introduces a cross-level bidirectional fusion strategy, where the closed-loop architecture enables dual compensation between spatial details and semantic contexts. The experimental results on the NUDT-SIRST, SIRST, and IRSTD-1K datasets demonstrate that the proposed DIFTransNet surpasses current state-of-the-art methods in accurate detection of infrared small targets.

Hyperspectral image (HSI) classification has been extensively studied in the context of Earth observation. However, its application in Mars exploration remains limited. Although convolutional neural networks (CNNs) have proven effective in HSI processing, their local receptive fields hinder their ability to capture long-range features. Transformers excel in global modeling and perform well in HSI classification (HSIC), but they often neglect the effective representation of local spectral and spatial features and tend to be more complex. To address these challenges, we propose a mixed CNN-transformer network for Mars HSI classification with graph contrastive learning to enhance classification performance. Specifically, we introduce an information-enhanced attention module (IEAM) designed to aggregate attention features from multiple perspectives. Additionally, we develop a lightweight dual-branch CNN-transformer (LDCT) network that efficiently extracts both local and global spectral-spatial features with lower complexity. To improve the discrimination of inter-class features, we apply graph contrastive learning to the topological structure of labeled samples. Furthermore, we annotated three Mars HSI datasets, referred to as HyMars, to validate the effectiveness of our proposed mixed CNN–“transformer network for Mars HSIC with graph contrastive learning (MCTGCL). Comprehensive experimental results across different amounts of labeled samples consistently demonstrate the superiority of the method. The source code is available athttps://github.com/B-Xi/TGRS_2025_MCTGCL.

Extracting rock objects from the surface of celestial bodies in deep space exploration environments is crucial for self-service path planning, navigation of detectors, and regional information evaluation. Most existing image saemantic segmentation frameworks decrease the spatial resolution of the feature maps as networks deepen, resulting in limitations in detecting small targets and the inability to accurately segment boundary regions. In this letter, we propose a novel semantic segmentation network based on U-Net network and Transformer for deep space rock images, referred to as Diamond-Unet. This model integrates overcomplete and undercomplete branches and incorporates a global-local feature extraction (GLFE) module based on Transformer and CNN technologies to effectively capture discriminative information. Furthermore, an innovative feature cross-fusion path (FCFP) is introduced to enhance information exchange between the dual-branch networks, enabling the capture of both fine-grained details and coarse-grained semantics in the full-scale image segmentation architecture. Experimental results demonstrate that the Diamond-Unet achievesMIoUscores of 79.32% and 93.43% on two public datasets, which are superior to the compared methods.

Land use/land cover classification with multimodal data has attracted increasing attention. For hyperspectral images (HSIs) and light detection and ranging (LiDAR) data, the combination of them can make the classification more accurate and robust. However, how to effectively utilize their respective strengths and integrate them with the classification task is still a challenging problem. In this article, a multilevel attention dynamic-scale network (MADNet) is proposed. First, in the feature extraction stage, the two modalities are divided into two branches with different scales, which are then fed into the convolutional neural networks (CNNs) to learn shallow features. Then, considering the characteristics of the HSI, a spectral angle attention module (SAAM) with low-level attention is designed to highlight surrounding pixels that have similar spectra to the central pixel of the patch. After that, a dynamic-scale selection module (DSSM) is proposed to screen an appropriate scale for the patches by pixel similarity analysis. Next, combining the Transformer and the CNN, a global-local cross-attention module (GLCAM) is devised to investigate the fused deep-level multimodal features. Distinct from the vanilla Transformer, the GLCAM deploys a distance-weight operator to decrease the redundancies at long distances and effectively reduce misclassifications. Extensive experiments on three paired HSI and LiDAR datasets demonstrate that the proposed MADNet has certain advantages over the existing methods.

Gathering extensive labeled data during Mars missions is costly and unrealistic, especially considering the complex and unpredictable Martian environment where new and unfamiliar scenes may emerge. Traditional Mars scene classification (MSC) methods depend heavily on large amounts of labeled data, which makes it impractical to recognize previously unseen scene classes without the necessary labeled examples. In addition, the significant computational demands and parameter requirements of modern models also pose challenges for their integration into resource-constrained systems used in Mars exploration. To address these issues, we propose a zero-shot MSC (ZSMSC) framework, which is able to categorize unseen Martian image scenes without the prior acquisition of vast visual examples. Specifically, the framework combines lightweight model design with knowledge distillation (KD) techniques, known as KDMSC, to streamline complex zero-shot learning (ZSL) models. It employs a KD loss that captures essential knowledge through the training of the teacher model from scratch, thereby improving the zero-shot classification performance of the student model. Consequently, the lightweight student model is tailored for deployment on devices with limited resources while fulfilling the requirements of the ZSMSC tasks. Moreover, to support the ZSMSC initiative, we developed a dataset named ZSMars to further advance this field. Experimental results indicate that our model excels in the ZSMSC tasks while maintaining low computational complexity and storage requirements.

Inspired by the recent progress of multimodal fusion in a variety of computer vision tasks, this letter aims to propose a two-stream fusion crater detection network (TFCDNet). Toward this end, near-infrared (IR) images and digital elevation maps (DEMs) in the feature domain are appropriately fused to boost the performance of crater detection (CD). The proposed TFCDNet includes a powerful feature-coding module that can effectively extract and fuse multimodal features. The comprehensively conducted experiments on both optical-DEM paired lunar crater detection dataset (ODPLCD) and Mars day CD (MDCD) datasets reveal that the proposed TFCDNet is capable of being more competitive than the state of the arts. As a result, this work is anticipated to spark some new thinking in CD. Relevant data in this letter can be downloaded from the websitehttps://doi.org/10.57760/sciencedb.o00009.00312.

Identifying the surface topographic changes accurately plays a vital role in the task of planetary exploration. In this study, a lightweight mobile vision transformer-based planetary image change detection (MViT-PCD) was proposed for monitoring dynamic surface changes using bitemporal images. The mobile vision transformer (MobileViT) was first introduced to make the most of the spatial information available. Subsequently, a multiscale feature differentiation and fusion (MFDF) block was adopted to improve the distinguishability of multilevel contextual information. Moreover, the strategy of information maximization (IM) was integrated to refine the model performance on the heterogeneous dataset. Then, experiments were conducted on the public Martian datasets. Compared with other state-of-the-art (SOTA) methods, the MViT-PCD provides favorable performance, with the highest accuracy of 97.2% and 82.9%, respectively, under the speed of 43.4 frame per second (FPS). Code is available athttps://github.com/lynn1023-max/MViTPCD.

In recent years, Distributed Autonomous Robotic System (DARS) becomes a major concern in developing robots of many projects. The use of DARS to accomplish tasks within a complex environment with limited resources requires strategies that can leverage the inherent redundancies within these systems. In fact, different DARS strategies produce differents DARS architectures, and the practical level of DARS design is reflected by reliability of DARS architectures. However, there are still some challenges, which contain communication, control, perception and energy management, in reliability evaluation of DARS architectures design. Therefore, considering these challenges, reliability evaluation model study of DARS architectures is needed in engineering. In order to evaluate reliability of DARS architectures systematically, and guide DARS architectures design effectively, this paper provides a reliability evaluation model of DARS architectures. There are five factors in this model, such as communication ability, adaptation ability, information integration, task allocation, and destination achievement. This model will evaluate these factors separately and provide a combined evaluation result together. Additionally, this paper explains a case to demonstrate how this model could be used in projects. The case indicates that the model could take advantage of reliability factors together, and gives a reasonable reliability evaluation in engineering. In addition, this paper makes a discussion in model improvement for different architectures reliability evaluation. Finally, the advantages of this model and the future work will be summarized.