As a fundamental and critical task in remote sensing image processing， image registration has consistently been a hot research topic among scholars both domestically and internationally， holding significant theoretical and practical value. This task aims to geometrically align two or more images of the same scene that were acquired at different times， by different sensors， or from different viewpoints， thereby providing a reliable data foundation for subsequent applications such as image mosaicking， image fusion， object detection， change detection， and guidance matching. However， remote sensing images often face significant challenges such as geometric distortion， nonlinear radiometric differences， complex object occlusion， and various types of noise interference. These factors interact with one another， making it difficult to accurately extract and match corresponding features between images. This severely hinders the achievement of high-precision， robust registration and poses greater challenges to the adaptability and robustness of existing registration methods in complex scenes. This paper systematically reviews remote sensing image registration algorithms. First， it outlines the basic concepts and technical framework of remote sensing image registration. Second， existing registration methods are categorized into two major groups for systematic analysis： knowledge-driven expert design methods and data-driven deep learning methods. Knowledge-driven， expert-designed methods rely on manually designed features and transformation models. Through explicit feature extraction and iterative optimization， they achieve interpretable registration in traditional scenarios. These methods can be further subdivided into template-based registration methods， feature-based registration methods， and hybrid registration methods. Template-based registration methods select a template window on the reference image， use its center as the point to be matched， and search for matches on the target image based on similarity criteria to identify corresponding points. These points are then used to solve for geometric transformation parameters， thereby completing remote sensing image registration. Feature-based registration methods identify and effectively describe salient features in remote sensing images. These features are used to establish correspondences between them and， subsequently， to construct geometric transformation relationships between the images. Hybrid registration methods leverage the advantages of multiple techniques to perform remote sensing image registration， thereby compensating for the shortcomings of single-method approaches. Most of these methods integrate various types of image information， such as gradient and phase information， to improve upon classical methods—such as scale-invariant feature transformation—in either the spatial domain or the transform domain， or to enhance registration accuracy by integrating multiple registration strategies. However， knowledge-driven， expert-designed methods suffer from issues such as heavy reliance on specialized knowledge， poor adaptability to complex scenes， and significant limitations in feature selection， making it difficult to meet the registration demands of large-scale， diverse remote sensing images. In recent years， with the continuous advancement of deep learning technology， numerous deep learning network architectures have become increasingly prevalent in the field of image processing， presenting new opportunities for remote sensing image registration methods. Consequently， data-driven deep learning registration methods have emerged. These methods can be further categorized into ensemble learning methods， end-to-end learning methods， style transfer methods， and large-model-driven methods. Ensemble learning methods integrate deep learning networks into template-based or feature-based registration frameworks to generate more robust and efficient feature representations or similarity metrics， thereby facilitating subsequent registration. End-to-end learning methods utilize deep learning networks to perform end-to-end learning from image pair inputs to target outputs， employing joint optimization to accomplish feature extraction， matching， or direct prediction of geometric transformation parameters between images. Style transfer methods use deep learning models， such as generative adversarial networks， to perform style transfer on remote sensing images， significantly reducing radiometric or textural differences between multimodal remote sensing images and creating more consistent image characteristics for subsequent registration operations. Large-model-driven methods leverage the powerful feature representation capabilities and cross-modal generalization abilities of pre-trained large models， providing a novel solution path for addressing challenges such as complex geometric distortions and nonlinear radiometric differences in remote sensing images. By learning directly from the data， these methods can solve image matching and registration problems more efficiently. They utilize deep learning networks to automatically learn feature or mapping relationships between image pairs， extract high-level features for registration， or directly predict transformation models end-to-end， thereby effectively addressing complex scenarios such as significant geometric distortion and nonlinear radiometric differences. Furthermore， this paper systematically reviews and analyzes datasets for multimodal image registration tasks. By summarizing representative datasets in the field of multimodal registration and focusing on their modal composition， data scale， spatial resolution， covered scenarios， and applicability， it provides an effective reference for researchers to select appropriate datasets for registration tasks， thereby facilitating systematic analysis and fair comparison of different methods under unified conditions in future research. Finally， based on the aforementioned review of methods in the field of remote sensing image registration， this paper outlines future development trends in the field.

ObjectiveSynthetic Aperture Radar （SAR） to optical image translation （SAR-to-Optical， S2O） is essential for full-time and all-weather earth observation. Optical images provide rich texture information and intuitive visual representations， which are important to a wide range of downstream tasks such as land-cover classification and urban monitoring. However， optical sensors are highly vulnerable to clouds， fog， and illumination changes， limiting continuous data acquisition. In contrast， SAR sensors operate in the microwave spectrum and can acquire images under all-weather and all-time conditions. Consequently， S2O translation can significantly enhance the interpretability of SAR data and facilitate multimodal remote sensing applications. Despite recent progress in cross-modal image translation， achieving high-quality S2O translation remains challenging. First， SAR images inevitably contain multiplicative speckle noise that is strongly coupled with structural information， and together with the substantial semantic gap caused by fundamentally distinct imaging mechanisms between SAR and optical sensors， this leads to insufficient cross-modal feature fusion and representation capability. Second， although diffusion models have recently demonstrated impressive performance in image generation tasks， their high computational overhead and inefficient conditional fusion mechanisms limit their applicability for real-time processing on resource-limited remote sensing platforms. To address these issues， we propose a Spatial-Frequency Strongly-Sparse Guided Diffusion Model （SFSG-Diff） for efficient and high-quality SAR-to-optical image translation.MethodThe proposed SFSG-Diff framework follows the standard diffusion generation paradigm， consisting of a forward diffusion process and a reverse denoising process. In the forward process， Gaussian noise is progressively added to a clean optical image until the data distribution approaches pure noise. During the reverse process， a conditional denoising network iteratively removes the noise to reconstruct the target optical image. To improve both generation quality and computational efficiency， SFSG-Diff introduces spatial-frequency guided feature extraction and a strongly sparse conditional fusion mechanism. To effectively suppress SAR speckle noise and enhance structural representation， a Multi-scale Spatial-Frequency Denoising Encoder （MDE） is designed to extract robust conditional features from the input SAR image. The encoder adopts a dual-branch architecture composed of a spatial feature extraction branch and a frequency feature extraction branch. The spatial branch focuses on capturing local textures and contextual information through multi-scale convolutional operations， while the frequency branch transforms SAR images into the frequency domain to capture global structural information that is less sensitive to speckle noise. Since speckle noise is mainly concentrated in high-frequency components， whereas meaningful structural information is typically distributed in low and mid-frequency regions， the spatial-frequency joint representation enables effective separation of noise and structural features. The outputs of both branches are fused across multiple scales to produce robust conditional representations that guide the diffusion model during image generation. To further improve conditional guidance efficiency， we introduce a lightweight Strong-Sparse Semantic Fusion （SIF） pattern. Instead of densely integrating conditional features into all feature channels， the SIF module performs channel-wise selection and adaptive attention-based modulation to identify and fuse the most informative feature components. This sparse guidance mechanism not only reduces computational overhead but also improves cross-modal semantic alignment by emphasizing the most relevant structural cues. Furthermore， to enhance training stability and generation quality， we adopt a two-stage training strategy， leveraging a joint loss， including a simplified mean squared error loss， perceptual loss， focus frequency loss， and adversarial loss.ResultTo evaluate the performance of the proposed framework， extensive experiments are conducted on three publicly available datasets， including SEN1-2， QXS-SAROPT， and WHU-OPT-SAR. The experimental results demonstrate that SFSG-Diff consistently outperforms several state-of-the-art GAN-based and diffusion-based image translation models. Quantitative evaluation results demonstrate that SFSG-Diff consistently outperforms existing state-of-the-art methods across multiple evaluation metrics. On the SEN1-2 dataset， the proposed SFSG-Diff achieves a PSNR improvement of 0.77 dB over the best competing method， while the Structural Similarity Index （SSIM） improves by 17.8%. In addition， the perceptual quality metrics show substantial improvements， with LPIPS and Fréchet Inception Distance （FID） reduced by 8.0% and 17.6%， respectively. These results indicate that the proposed model not only improves reconstruction fidelity but also produces images with higher perceptual realism. Similar performance gains are observed on the QXS-SAROPT and WHU-OPT-SAR datasets， which include more complex urban structures and higher spatial resolution imagery. The proposed SFSG-Diff demonstrates strong robustness across diverse scenes and maintains consistent advantages in both structural similarity and perceptual quality. Visual comparisons further confirm the superiority of SFSG-Diff. Compared with existing methods， the SFSG-Diff generates optical images with clearer structural boundaries， more realistic textures， and fewer noise artifacts. In particular， the model exhibits improved performance in challenging regions such as dense urban areas and vegetation-rich scenes. In addition to generation quality， the computational efficiency of the proposed framework is also evaluated. Owing to the lightweight SIF pattern and the two-stage training strategy， SFSG-Diff achieves significantly faster inference compared with conventional diffusion models. The average inference time per image is approximately 0.21 seconds， which represents a 69.1% reduction in computation time compared with representative diffusion-based baselines.ConclusionWe presents SFSG-Diff， a novel framework for one-step S2O translation， which addresses key challenges in cross-modal image generation by leveraging spatial-frequency joint feature modeling and lightweight Strong-Sparse conditional guidance. The MDE effectively suppresses speckle noise and enhances structural feature representation， while the SIF pattern improves cross-modal alignment and reduces computational complexity. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method significantly outperforms existing approaches in both quantitative metrics and visual quality. Moreover， the framework achieves substantial improvements in inference efficiency， making it more suitable for practical remote sensing applications with limited computational resources. Overall， the proposed SFSG-Diff framework provides an effective solution for robust and efficient SAR-to-optical image translation and offers new insights into the integration of spatial-frequency modeling and sparse conditional guidance within diffusion-based generative models.

ObjectiveVisual saliency prediction aims to simulate the human visual attention mechanism by estimating the spatial probability distribution of gaze points in complex scenes. This task is fundamental to various downstream applications， including autonomous driving， robot navigation， and image compression. While Convolutional Neural Networks （CNNs） have long been the backbone of saliency modeling due to their proficiency in local feature extraction and multi-scale representation， they are inherently limited by their local receptive fields. This restriction often leads to "fixation fragmentation" and background false alarms in cluttered environments. Conversely， Transformer-based models have introduced global dependency modeling via self-attention； however， their quadratic computational complexity becomes a significant bottleneck when processing high-resolution saliency maps. Recently， State Space Models （SSMs）， specifically Mamba， have emerged as a promising alternative， offering linear-time complexity while maintaining long-range context. Nevertheless， standard Mamba architectures are designed for 1D sequences， which inherently disrupts the 2D spatial topological continuity of images. Furthermore， during long-range state propagation， background noise is often indiscriminately amplified alongside salient features. Additionally， the continuous bilinear upsampling typically employed in decoding stages causes a "structural over-smoothing" effect， which diminishes the peak energy of salient regions and blurs boundaries. To address these multi-faceted challenges， this study proposes a novel visual saliency prediction network named Spatio-Spectral Uncertainty-Gated Mamba Network （S²UG-Mamba）. The goal is to achieve an optimal synergy between global context modeling， noise suppression， and high-frequency structural restoration within a linear computational framework.MethodThe proposed S²UG-Mamba follows a "backbone extraction， encoder enhancement， multi-scale decoding， frequency modulation， and prediction" pipeline. We employ an ImageNet-pretrained ConvNeXt-Tiny as the backbone to extract multi-level features. In the encoder enhancement phase， we introduce the Uncertainty-Aware State Space Enhancement Module （UA-SSM）. To preserve 2D spatial continuity， UA-SSM utilizes a bidirectional orthogonal serpentine scanning strategy， which unfolds features in both horizontal and vertical directions to capture omnidirectional contextual interactions. To mitigate the propagation of background noise， we design a dual-view uncertainty proxy within UA-SSM. This proxy consists of a spatial variance component （capturing local texture fluctuations in 3x3 windows） and a channel variance component （measuring semantic activation disagreements）. These proxies are fused to generate a pixel-level confidence gate G， which dynamically modulates the Mamba input sequence， thereby suppressing responses in high-uncertainty background regions.In the decoding stage， to counteract the smoothing effects of upsampling， we propose the Semantic-Guided Dynamic Frequency Modulation （SDFM） module. Unlike conventional static filters， SDFM employs a joint logical routing mechanism that combines deep semantic priors from the encoder with local content features from the decoder. This routing mechanism dynamically synthesizes a set of learnable complex frequency filters. The feature maps are transformed into the frequency domain via a real Fast Fourier Transform （rFFT） after StarReLU activation， multiplied by the adaptive filters， and then mapped back to the spatial domain using an inverse rFFT. This residual frequency compensation explicitly restores high-frequency structural details and tightens the energy concentration of fixation peaks. The entire network is optimized using a joint loss function of Kullback-Leibler （KL） divergence and Linear Correlation Coefficient （CC）.ResultExtensive experiments were conducted on five benchmark datasets： SALICON， MIT300， MIT1003， CAT2000， and TORONTO. On the SALICON test set （LSUN'17）， S²UG-Mamba achieved state-of-the-art performance， reducing the KL divergence to 0.176 while reaching a CC of 0.918 and an NSS of 2.007. On the challenging MIT300 blind test set， our model yielded a CC of 0.829 and a KL of 0.370， outperforming the advanced GSGNet by 2.2% and 9.8%， respectively. To evaluate cross-domain generalization， we performed zero-shot testing on the MIT1003 dataset using weights trained solely on SALICON； S²UG-Mamba attained an NSS of 2.386 and a CC of 0.6849， demonstrating robust learning of universal saliency priors. Ablation studies confirmed the efficacy of each component： the introduction of UA-SSM improved the NSS from 1.9627 to 2.0152， while SDFM significantly reduced the KL divergence. The complexity and efficiency analysis demonstrates that our model achieves favorable performance in terms of computational cost and GPU memory consumption. Meanwhile， it maintains comparable parameter scale and inference speed with state-of-the-art methods， fully validating the efficient modeling capability of the Mamba architecture. Qualitative visualizations further illustrated that S²UG-Mamba effectively suppresses non-salient background clutter in densely textured scenes and produces sharper， more compact salient boundaries compared to existing methods.ConclusionThis study presents S²UG-Mamba， a linear-complexity network that synergistically integrates uncertainty-aware state space modeling and semantic-guided frequency modulation for visual saliency prediction. By employing orthogonal scanning and a dual-view uncertainty gating mechanism， the encoder efficiently captures long-range dependencies while adaptively suppressing noise. Simultaneously， the SDFM leverages high-level semantic guidance to perform adaptive frequency filtering， effectively restoring structural details lost during spatial upsampling. The experimental results across multiple benchmarks validate that S²UG-Mamba provides a superior balance between prediction accuracy， cross-domain robustness， and computational efficiency. Future research will explore hardware-friendly parallel 2D scanning algorithms and low-annotation learning paradigms to further enhance the model’s practical deployment potential.

ObjectiveVideo anomaly detection （VAD） aims to automatically identify events that deviate from learned normal patterns in video sequences and has become a critical component of intelligent surveillance systems. It plays an important role in public security， intelligent transportation， industrial inspection， and urban management. With the rapid deployment of unmanned aerial vehicles （UAVs）， large-scale aerial video data have become increasingly available， creating new opportunities for anomaly detection. However， compared with conventional fixed-camera surveillance videos， UAV videos exhibit substantial viewpoint variations， severe object-scale changes， dynamic backgrounds， and complex motion patterns， which significantly increase the difficulty of anomaly detection. Although recent deep-learning-based methods have achieved promising performance， most existing approaches focus primarily on appearance modeling while insufficiently exploiting motion information， despite the fact that abnormal events are often characterized by unusual motion patterns such as sudden acceleration， abrupt direction changes， illegal crossings， or abnormal trajectories. Furthermore， reconstruction-based and prediction-based methods commonly rely on generative models to learn normal patterns and identify anomalies through reconstruction or prediction errors. These models often suffer from the over-generalization problem， where abnormal samples can still be reconstructed or predicted with relatively low errors， thereby reducing the discriminative capability between normal and abnormal events. Therefore， developing a unified framework that can effectively model both appearance and motion information while enhancing the representation of diverse normal patterns remains an important challenge for anomaly detection in both ground-based and aerial surveillance scenarios.MethodTo address the above issues， a dual-stream memory-enhanced network for ground-to-air video anomaly detection is proposed. The framework adopts a dual-encoder single-decoder architecture. First， a pretrained FlowNet2 optical-flow estimation network is employed to extract motion information from consecutive video frames. To prevent overfitting and reduce training complexity， the FlowNet2 parameters remain fixed throughout the training process. The original RGB frames and corresponding optical-flow maps are then fed into two independent encoders， namely an appearance encoder and a motion encoder， to extract multi-scale appearance features and motion features， respectively. To establish a stronger correlation between appearance and motion representations， a variance-attention appearance-motion fusion module is designed. Specifically， the spatial variance of motion features is computed to characterize the intensity of motion changes. Regions exhibiting large motion variance， such as sudden acceleration， abrupt direction changes， or unusual object movements， are assigned higher attention weights. These weights are subsequently used to enhance appearance features， enabling the network to focus on potentially anomalous regions while suppressing irrelevant background information. Through this mechanism， appearance and motion features can be deeply fused at multiple scales， resulting in more discriminative feature representations. To alleviate the over-generalization problem and improve the learning of diverse normal patterns， a memory-enhanced module with a dynamic update strategy is further introduced. The memory module consists of multiple learnable memory items that store representative normal feature prototypes. During the memory-reading process， query features extracted from high-level fused representations retrieve the most relevant memory items according to cosine similarity. The retrieved memory features are then combined with the original query features to generate memory-enhanced representations. During memory updating， a regularization score derived from prediction errors is employed to distinguish normal frames from potential abnormal frames. Only features associated with normal frames are allowed to update memory items， thereby preventing abnormal patterns from contaminating the memory bank. This strategy enables the memory module to continuously learn diverse normal behaviors while preserving its discriminative capability. In the decoding stage， multi-scale fused features are delivered to the decoder through skip connections， allowing low-level spatial details and high-level semantic information to be jointly utilized for future-frame prediction. Furthermore， efficient channel attention modules are embedded after the first three upsampling layers to adaptively recalibrate channel-wise feature responses and enhance prediction quality with negligible computational overhead. The entire network is optimized using a joint loss function composed of prediction loss， optical-flow loss， feature compactness loss， and feature separation loss. These losses collaboratively encourage accurate future-frame prediction， motion consistency preservation， compact intra-class representations， and discriminative memory-item distributions.ResultsExtensive experiments were conducted on three benchmark ground-surveillance datasets， namely UCSD Ped2， CUHK Avenue， and ShanghaiTech， as well as the Drone-Anomaly UAV dataset. Experimental results demonstrate that the proposed method achieves state-of-the-art or highly competitive performance across different scenarios. On the UCSD Ped2 dataset， the proposed method achieved an AUC of 98.8%， outperforming representative methods such as MemAE （94.1%）， MNAD （97.0%）， and AMMC （96.6%）. On the CUHK Avenue dataset， an AUC of 89.1% was obtained， slightly surpassing DEDDnet （89.0%） and exceeding most recent prediction-based approaches. On the challenging ShanghaiTech dataset， which contains more than 270，000 training frames， multiple scenes， and diverse anomaly categories， the proposed method achieved an AUC of 74.7%， demonstrating strong robustness under complex environments. To further evaluate the generalization capability in aerial surveillance scenarios， experiments were conducted on the Drone-Anomaly dataset， which contains multiple UAV-captured inspection and traffic-monitoring scenes. The proposed method achieved 94.76% AUC with an EER of 0.09% on Railway Inspection and 91.41% AUC with an EER of 0.10% on Farmland Inspection， significantly outperforming Frame-Pred， MNAD， ANDT， MKD， and SSPCAB. In addition to detection accuracy， computational efficiency was evaluated. The proposed model contains only 20.39M parameters and achieves an inference speed of 62.66 FPS on an NVIDIA RTX4090 GPU， outperforming several existing methods in terms of both efficiency and accuracy. Even when the computational cost of FlowNet2 is included， the end-to-end processing speed remains approximately 39.76 FPS， satisfying the requirements of most real-time surveillance applications. Ablation studies further validate the effectiveness of the proposed components. Additional loss-function ablations confirmed that the combination of prediction loss， optical-flow loss， compactness loss， and separation loss yields the best detection performance.ConclusionThis paper presents a dual-stream memory-enhanced framework for video anomaly detection in both ground-based and UAV surveillance scenarios. By jointly modeling appearance and motion information through a variance-attention fusion mechanism and learning diverse normal patterns via a dynamically updated memory module， the proposed method effectively alleviates the over-generalization problem and improves anomaly discrimination in complex dynamic environments. Extensive experiments on multiple benchmark datasets demonstrate its robustness， generalization capability， computational efficiency， and real-time performance. The proposed framework provides a practical and effective solution for intelligent anomaly detection across diverse surveillance applications and offers a promising foundation for future research on unified ground-to-air video understanding systems.

Cross-domain few-shot object detection （CD-FSOD） in aerial remote sensing aims to detect target objects when the target domain contains only a few annotated instances and differs substantially from the source domain. This setting is important for land resource investigation， disaster monitoring， traffic supervision， maritime and airport surveillance， emergency response， and military reconnaissance， because many operational remote sensing systems must be deployed in new regions， seasons， sensors， or platforms before large-scale annotation is available. Conventional object detectors usually assume abundant labeled data and a relatively stable training-test distribution. In contrast， aerial images are affected by platform altitude， satellite or unmanned aerial vehicle imaging geometry， optical or synthetic aperture radar （SAR） sensing mechanisms， spatial resolution， illumination， weather， seasonal background， geographical scene composition， and category occurrence frequency. These factors change target scale， texture， orientation， density， and contextual appearance at the same time. Consequently， a detector trained on a well-annotated source domain may suffer from severe classification confusion， localization drift， false alarms， and missed detections after being transferred to a new target domain. This paper provides a systematic survey of CD-FSOD for aerial remote sensing scenes. First， the task definition and research boundary are clarified. CD-FSOD is distinguished from classic object detection， few-shot object detection （FSOD）， cross-domain object detection， and general remote sensing object detection. Classic object detection emphasizes classification and localization under roughly independent and identically distributed data. FSOD focuses on new-category detection with limited examples but often assumes that the base and novel domains are close. Cross-domain object detection addresses domain shift but usually does not impose an extreme target-domain annotation constraint. Remote sensing object detection describes the application scenario and its characteristic difficulties， including small objects， dense object distributions， arbitrary orientations， and complex backgrounds. CD-FSOD couples the last two difficulties： the target domain is both data-scarce and distributionally shifted. Second， existing methods are organized into five technical routes. Transfer and domain-alignment methods， including domain-adaptive Faster region-based convolutional neural network （DA Faster R-CNN）， strong-weak distribution alignment （SWDA）， spatial attention pyramid network （SAPNet）， and adaptive teacher frameworks， reduce the discrepancy between source and target features by image-level， feature-level， instance-level， or teacher-student constraints. Meta-learning and metric-learning methods， including meta region-based convolutional neural network （Meta R-CNN）， attention region proposal network （Attention-RPN）， few-shot object detection via contrastive proposal encoding （FSCE）， Detect Everything with Vision Transformer （DE-ViT）， and CD-ViTO， improve the use of support samples through episodic learning， class prototypes， relation modeling， and contrastive representation learning. Generative augmentation methods， such as hallucination-based feature synthesis， multi-perspective data augmentation （MPAD）， AeroGen， Control Copy-Paste， and Domain-RAG， expand the target-domain sample distribution by generating features， foreground instances， target layouts， or domain-consistent backgrounds. Vision-language alignment methods， including RegionCLIP， PromptDet， Grounding DINO， and rich-text cross-domain multimodal FSOD， introduce category names， attributes， scene descriptions， and professional textual semantics to compensate for insufficient visual examples. Large-model methods exploit visual foundation models， detection transformer （DETR） variants， and multimodal large models such as Rex-Omni to provide broader open-vocabulary recognition and stronger initial representations. Third， the paper summarizes datasets， task protocols， and evaluation metrics. The reviewed benchmarks include multi-domain few-shot object detection （MoFSOD）， CD-FSOD， object detection in optical remote sensing images （DIOR）， dataset for object detection in aerial images （DOTA）， xView， vehicle detection in aerial imagery （VEDAI）， and the Northwestern Polytechnical University very-high-resolution 10-class dataset （NWPU VHR-10）. Their differences involve image number， category number， bounding-box type， scene coverage， object scale， and whether the protocol stresses a balanced K-shot instance setting. Common metrics include precision （P）， recall （R）， average precision （AP）， mean average precision （mAP）， AP50， AP75， mAP@50：95， novel-class mAP， and intersection over union （IoU）. Instead of listing formulae for routine metrics， this survey explains their meaning and discusses how they should be used to evaluate target-domain generalization， shot sensitivity， robustness across domains， and localization quality for small dense objects. Fourth， this review identifies several open problems. Complex domain shift should be modeled at global-scene， local-object， and category-conditional levels rather than by a single overall feature alignment. Few-shot robustness requires support-sample quality assessment， stable prototype learning， multi-scale representation， uncertainty estimation， and joint optimization of classification and localization. Current datasets and evaluation protocols remain partly disconnected from real remote sensing applications， where categories are long-tailed， annotations are incomplete， sensors are heterogeneous， and deployment constraints are strict. Multisource adaptation across optical， infrared， multispectral， and SAR data is still underexplored. Foundation models and multimodal large models offer stronger semantic priors， but they also introduce high computational cost， insufficient remote sensing domain knowledge， and limited performance on small， dense， rotated， or sensor-specific targets. To improve reuse and citation of the survey materials， a curated performance summary of representative algorithms under the aerial remote sensing CD-FSOD setting is provided in the manuscript and will be maintained through the open resource page：https：//github.com/Farenweh/CD-FSOD-Links（Github）orhttps：//gitee.com/cby1241385936/cd-fsod-links（Gitee）. Overall， CD-FSOD in aerial remote sensing is moving from isolated fine-tuning or domain-alignment strategies toward integrated frameworks that combine domain adaptation， few-shot discrimination， data generation， vision-language semantics， and foundation-model transfer. In terms of practical use， the paper also emphasizes that CD-FSOD should not be evaluated only by a single accuracy score obtained under a fixed split. A more informative protocol should report the stability of each method under different shots， different target domains， different object scales， and different sensor conditions， because an algorithm that performs well on one aerial dataset may fail when the dominant background， ground sampling distance， or object density changes. The survey therefore recommends that future benchmark construction record the source of domain shift explicitly and separate the effects of category novelty， image style， spatial resolution， and annotation scarcity. Such analysis is particularly necessary for aerial remote sensing， where missed small targets and inaccurate localization may be more damaging than a modest decrease in overall mAP. Future progress depends on remote-sensing-specific pretraining resources， reliable benchmark construction， efficient adaptation for high-resolution imagery， and stronger integration between precise detectors and general-purpose foundation models.

Airborne spectral imaging， including hyperspectral （HSI） and multispectral （MSI） data from UAVs and manned aircraft， has become a cornerstone of modern remote sensing. Compared to satellites， airborne platforms offer ultra-high spatial resolution， operational flexibility， short revisit cycles， and customizable sensors， making them indispensable for precision agriculture， environmental monitoring， military reconnaissance， urban planning， geological exploration， and cultural heritage preservation. However， airborne spectral data present unique challenges： high dimensionality （especially for HSI with hundreds of bands）， strong spectral-spatial correlations， complex noise， geometric distortions from platform motion， variable illumination， and the mixed-pixel problem. Traditional methods （e.g.， SVM， random forests， physical models） struggle to fully exploit these data due to limited feature representation， the curse of dimensionality， and poor generalization across sensors or flight conditions.This paper systematically reviews intelligent processing techniques for airborne spectral images， focusing on deep learning and advanced machine learning. We first highlight the complementary roles of HSI and MSI： HSI provides rich continuous spectra for fine material identification and sub-pixel analysis， while MSI offers higher signal-to-noise ratio， lower data volume， and better real-time performance. Their synergistic use， often in a layered strategy （MSI-based coarse detection followed by HSI-based fine identification）， is a key trend. We then detail the preprocessing pipeline—radiometric calibration， atmospheric correction， geometric registration， denoising， and resolution enhancement—noting the shift from purely physical models to hybrid data-driven and physics-informed deep learning. The core of the review covers six intelligent processing tasks. For spectral-spatial feature extraction， we trace the evolution from 2D/3D CNNs and hybrid CNN-Transformer models to state-space models like Mamba， which achieve linear complexity while capturing long-range spectral dependencies， balancing accuracy and efficiency for real-time scenarios. For classification and segmentation， we discuss supervised， semi-supervised， few-shot， and self-supervised methods （e.g.， HybridSN， Spectral-Former， masked spectral modeling）； for MSI， lightweight networks （MobileNet， EfficientNet） and transfer learning dominate， with cross-domain few-shot learning addressing label scarcity. For target detection， HSI exploits subtle spectral differences for camouflaged targets and chemical agents using anomaly detection or Transformer-based detectors （e.g.， SpecDETR）， while MSI leverages YOLO and Faster R-CNN for real-time object detection； multimodal fusion （visible， thermal， spectral） further improves robustness. For change detection， Siamese networks， Transformers （BIT， ChangeFormer）， and Mamba hybrids are applied to multi-temporal images， with challenges in distinguishing real changes from illumination/atmospheric variations and incorporating physical constraints. For spectral unmixing， autoencoders learn nonlinear mixtures but lack physical constraints （non-negativity， sum-to-one， energy conservation）； recent efforts embed linear mixture or radiative transfer models into deep networks for better interpretability. For multi-source fusion， we cover heterogeneous fusion （HSI+LiDAR， HSI+SAR） using multimodal Transformers or GNNs， and spectral-spectral fusion （HSI+MSI） to generate high-resolution hyperspectral data， outlining four collaborative strategies： HSI-guided band selection， MSI-assisted dimensionality reduction， layered cascaded processing， and end-to-end joint modeling. A dedicated section addresses real-time processing and lightweight deployment on airborne edge devices （e.g.， NVIDIA Jetson）. We review model compression （pruning， quantization， knowledge distillation， neural architecture search） and evaluate backbones （MobileNet， HybridSN， Mamba， efficient Transformers） for onboard inference. Real-time HSI classification and MSI detection have been demonstrated， but real-time unmixing and change detection remain challenging. The paper surveys typical applications， emphasizing HSI/MSI complementarity： precision agriculture （early disease detection， nutrient/water stress， fruit counting）； environmental and disaster monitoring （oil spills， red tides， forest fires， air pollution， mining）； military reconnaissance （camouflage target detection， chemical agents）； urban and infrastructure （land cover classification， building change detection， traffic， infrastructure health）； geological exploration （mineral mapping， alteration zones， oil/gas microseepage）； and archaeology （subsurface relics， surface disease analysis）. We then identify five core challenges： scarcity of high-quality labeled data， insufficient model generalization， real-time constraints， lack of interpretability （black-box models ignoring physical mechanisms）， and difficulties in air-space-ground collaboration （heterogeneous data， limited bandwidth， cross-platform transfer）. Finally， we outline future directions： foundation models with self-supervised learning for spectral-spatial representation； lightweight edge intelligence via hardware-software co-design； physics-informed deep models embedding radiative transfer or mixture constraints； robust and trustworthy AI with adversarial defense and uncertainty quantification； and integrated air-space-ground collaborative observation systems for hierarchical， intelligent remote sensing. This review provides a systematic reference for researchers and engineers advancing airborne spectral image intelligent processing. The datasets mentioned in this paper have been compiled and are available at The datasets mentioned in this paper have been compiled and are available athttps：//github.com/zhaoxb2025/Airb-spe-Img.