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A Self-Prompt Calibration Network Based on Segment Anything Model 2 for High-Resolution Remote Sensing Image Segmentation 基于分段任意模型2的高分辨率遥感图像自提示标定网络
Yizhou Lan;Daoyuan Zheng;Xinge Zhao;Ke Shang;Feizhou Zhang
Remote sensing image segmentation is particularly difficult due to the coexistence of large-scale variations and fine-grained structures in very high-resolution imagery. Conventional CNN-based or transformer-based networks often struggle to capture global context while preserving boundary details, leading to degraded performance on small or thin objects. To address these challenges, we propose a self-prompt calibration network based on segment anything model 2 (SC-SAM). The SC-SAM achieves self-prompt by feeding mask prompts from a lightweight decoder into frozen prompt encoder. Output calibration is achieved through the proposed cross-probability guided calibration (CPGC) module, which employs cross-probability uncertainty as complementary guidance to refine final predictions via self-prompted outputs. Furthermore, to better preserve contextual and structural information across multiple scales, a scale-decoupled kernel mixture (SDKM) module is designed. Experimental results on the ISPRS Vaihingen and Potsdam dataset demonstrate that the proposed approach surpasses the state-of-the-art methods by 1.02% and 1.34% in mIoU, highlighting its effectiveness. This study provides new insights into adapting SAM for domain-specific remote sensing segmentation tasks.
由于在高分辨率影像中同时存在大尺度变化和细粒度结构,因此遥感影像分割尤其困难。传统的基于cnn或基于变压器的网络通常难以捕捉全局上下文,同时保留边界细节,导致在小或薄对象上的性能下降。为了解决这些挑战,我们提出了一个基于分段任意模型2 (SC-SAM)的自提示校准网络。SC-SAM通过从轻量级解码器向冻结提示编码器馈送掩码提示来实现自提示。输出校准通过提出的交叉概率引导校准(CPGC)模块实现,该模块采用交叉概率不确定性作为补充指导,通过自提示输出来改进最终预测。此外,为了更好地跨尺度保存上下文信息和结构信息,设计了尺度解耦核混合(SDKM)模块。在ISPRS Vaihingen和Potsdam数据集上的实验结果表明,该方法的mIoU比现有方法分别高出1.02%和1.34%,表明了该方法的有效性。该研究为将SAM应用于特定领域的遥感分割任务提供了新的见解。
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引用次数: 0
User-Driven Land Cover Change Prediction Map Tool for Land Conservation Planning 基于用户驱动的土地保护规划土地覆盖变化预测地图工具
Pui-Yu Ling;Laura Nunes;Jonathan Srinivasan;Nasir Popalzay;Palmer Wilson;Jameson Quisenberry;Alex Borowicz
To be effective, ecosystem and habitat conservation must not only look at past losses but also understand the effects of current and future decisions on landscapes. Here, we present a transformative, user-driven land cover change prediction tool designed to aid land planners in strategic decision-making for conservation and habitat protection. Within an integrated map-based prediction pipeline, the tool uses machine learning (ML) and deep learning (DL) models to classify satellite images and make predictions of near-term land cover changes. The tool facilitates user interaction with a cloud-hosted ML model, making it accessible to nontechnical users for generating map-based predictions using big data. The tool’s key strength lies in its dynamic variable adjustment feature, empowering users to tailor scenarios related to potential future development planning. Through the integration of cloud-hosted ML and DL models with a user-centric interface, the tool has the potential to allow stakeholders and land planners to make informed decisions, actively minimizing habitat destruction and aligning with broader conservation objectives. We tested our approach in the context of central Texas, USA to evaluate its effectiveness in diverse conservation scenarios, with an average overall accuracy of 88% for the land cover class maps over four years and over 72% for the five-year land cover change prediction. While our approach has the potential to improve land management and planning for conservation, we also acknowledge the importance of rigorous model validation and ongoing refinement and highlight the need for technological advancement to be developed with strong stakeholder engagement.
为了有效地保护生态系统和栖息地,不仅要关注过去的损失,还要了解当前和未来的决策对景观的影响。在这里,我们提出了一个变革性的、用户驱动的土地覆盖变化预测工具,旨在帮助土地规划者进行保护和栖息地保护的战略决策。在集成的基于地图的预测管道中,该工具使用机器学习(ML)和深度学习(DL)模型对卫星图像进行分类,并对近期土地覆盖变化进行预测。该工具促进了用户与云托管ML模型的交互,使非技术用户可以使用大数据生成基于地图的预测。该工具的关键优势在于其动态变量调整功能,使用户能够根据潜在的未来发展规划定制场景。通过将云托管的ML和DL模型与以用户为中心的界面集成,该工具有可能使利益相关者和土地规划者做出明智的决策,积极减少栖息地破坏,并与更广泛的保护目标保持一致。我们在美国德克萨斯州中部测试了我们的方法,以评估其在不同保护情景下的有效性,4年土地覆盖分类图的平均总体精度为88%,5年土地覆盖变化预测的平均总体精度超过72%。虽然我们的方法有可能改善土地管理和保护规划,但我们也承认严格的模型验证和不断改进的重要性,并强调需要在利益相关者的大力参与下开发技术进步。
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引用次数: 0
Federated Aerial Video Captioning With Effective Temporal Adaptation 有效时间自适应的联邦航空视频字幕
Nguyen Anh Tu;Nursultan Makhanov;Kenzhebek Taniyev;Ton Duc Do
Aerial video captioning (VC) facilitates the automatic interpretation of dynamic scenes in remote sensing (RS), supporting critical applications, such as disaster response, traffic monitoring, and environmental surveillance. However, challenges, such as extreme angles and continuous camera motion, require adaptive modeling of complex temporal relationships. To tackle these challenges, we leverage an image-language model as the vision encoder and introduce a temporal adaptation module that combines convolution with self-attention layers to both capture local semantics across neighboring frames and model global temporal dependencies. This design allows our model to exploit the multimodal knowledge of the vision encoder while effectively reasoning over the spatiotemporal dynamics. In addition, privacy concerns often restrict access to annotated aerial datasets, posing further challenges for model training. To address this, we develop a federated learning (FL) framework that enables collaborative model training across decentralized clients. Within this framework, we establish a unified benchmark for systematic comparison of temporal adapters, text decoders, and FL strategies, hence filling a gap in the existing literature. Extensive experiments validate the robustness of our approach and its potential for advancing aerial VC.
航空视频字幕(VC)促进了遥感(RS)中动态场景的自动解释,支持关键应用,如灾害响应、交通监控和环境监测。然而,极端角度和连续摄像机运动等挑战需要对复杂的时间关系进行自适应建模。为了应对这些挑战,我们利用图像语言模型作为视觉编码器,并引入时间适应模块,该模块将卷积与自关注层结合起来,既可以捕获相邻帧之间的局部语义,又可以对全局时间依赖性进行建模。这种设计允许我们的模型利用视觉编码器的多模态知识,同时有效地对时空动态进行推理。此外,隐私问题往往限制了对带注释的航空数据集的访问,这给模型训练带来了进一步的挑战。为了解决这个问题,我们开发了一个联邦学习(FL)框架,支持跨分散客户端的协作模型训练。在这个框架内,我们建立了一个统一的基准,用于系统比较时间适配器、文本解码器和FL策略,从而填补了现有文献中的空白。大量的实验验证了我们的方法的鲁棒性及其推进空中VC的潜力。
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引用次数: 0
An Integrated Framework for Estimating the All-Sky Surface Downward Longwave Radiation From FY-3D/MERSI-II Imagery 基于FY-3D/MERSI-II影像的全天空表面向下长波辐射综合估算框架
Qi Zeng;Wanchun Zhang;Jie Cheng
This study develops an integrated framework for all-sky surface longwave downward radiation (SLDR) estimate for the medium resolution spectral imager-II (MERSI-II) onboard the Fengyun-3D (FY-3D) satellite. The framework comprises a hybrid method for the clear-sky SLDR estimate and a cloud base temperature (CBT)-based single-layer cloud model (SLCM) for the cloudy-sky SLDR estimate. In situ validation indicates that the hybrid method yields a bias/RMSE of −0.78/21.70 W/m2, whereas the SLCM achieves a bias/RMSE of 5.79/23.61 W/m2. The bias/RMSE of the all-sky SLDR is 3.37/22.93 W/m2. The estimated all-sky instantaneous SLDR was combined with ERA5 temporal information to derive daily SLDR using a bias-corrected sinusoidal integration method, yielding a bias of 0.04 W/m2 and an RMSE of 16.77 W/m2. These results demonstrate the robustness of the proposed framework and its substantial potential in generating both instantaneous and daily SLDR products at 1 km spatial resolution.
为风云三号卫星(FY-3D)上的中分辨率光谱成像仪ii (MERSI-II)开发了全天表面长波向下辐射(SLDR)估算的集成框架。该框架包括用于晴空SLDR估计的混合方法和用于云基温度(CBT)估计的单层云模型(SLCM)。原位验证表明,混合方法的偏差/RMSE为- 0.78/21.70 W/m2,而SLCM方法的偏差/RMSE为5.79/23.61 W/m2。全天SLDR的偏差/均方根误差为3.37/22.93 W/m2。将估计的全天瞬时SLDR与ERA5时间信息结合使用偏差校正正弦积分法得到日SLDR,偏差为0.04 W/m2, RMSE为16.77 W/m2。这些结果证明了所提出的框架的鲁棒性及其在生成1公里空间分辨率的瞬时和每日SLDR产品方面的巨大潜力。
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引用次数: 0
Improving New Zealand’s Vegetation Mapping Using Weakly Supervised Learning 利用弱监督学习改进新西兰植被制图
Brent Martin;Norman W. H. Mason;James D. Shepherd;Jan Schindler
The New Zealand Land Use Carbon Analysis System Land Use Map (LUCAS LUM) is a series of land use layers that map land use classes, including both exotic and native forest, dating back to 1990 and updated every four years since 2008. This map is a rich resource, but the significant effort required to update it means errors may creep in without detection. We trialed whether a deep learning model could be trained on this imperfect data. We found the model predicts exotic forestry nationally to a higher level of accuracy than previously achieved. The resulting layer was used to detect and correct missed exotic forest plantations in the current LUCAS LUM. We also demonstrate that the exotic forestry prediction is sufficiently sensitive to detect wilding conifer infestations and estimate infestation density. Our results highlight the effectiveness of weakly supervised learning, enabling accurate and scalable national land use and land cover mapping while drastically reducing manual labeling efforts.
新西兰土地利用碳分析系统土地利用图(LUCAS LUM)是一系列土地利用层,绘制了土地利用类别,包括外来森林和原生森林,可追溯到1990年,自2008年以来每四年更新一次。这张地图是一个丰富的资源,但更新它需要付出巨大的努力,这意味着错误可能会在没有被发现的情况下悄悄出现。我们测试了深度学习模型是否可以在这些不完美的数据上进行训练。我们发现,该模型在全国范围内预测外来林业的准确度高于以前的水平。所得到的层用于检测和纠正当前LUCAS LUM中缺失的外来森林种植园。我们还证明了外来森林预测在检测野生针叶树侵染和估计侵染密度方面具有足够的敏感性。我们的研究结果突出了弱监督学习的有效性,实现了准确和可扩展的国家土地利用和土地覆盖制图,同时大大减少了人工标记工作。
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引用次数: 0
Forest Tree Species Classification Based on Deep Ensemble Learning by Fusing High-Resolution, Multitemporal, and Hyperspectral Multisource Remote Sensing Data 基于高分辨率、多时相、高光谱多源遥感数据融合的深度集成学习森林树种分类
Dengli Yu;Lilin Tu;Ziqing Wei;Fuyao Zhu;Chengjun Yu;Denghong Wang;Jiayi Li;Xin Huang
Forest tree species classification has great significance for sustainable development of forest resource. Multisource remote sensing data provide abundant temporal, spatial, and spectral information for tree species classification. However, there lacks tree species classification methods, which comprehensively capture and fuse spatio–temporal–spectral information. Therefore, a tree species classification method based on deep ensemble learning of multisource spatio–temporal–spectral remote sensing data is proposed. First, multitemporal, high-resolution, and hyperspectral data are utilized for training temporal, spatial, and spectral deep networks. Furtherly, deep ensemble learning is developed for the fusion of spatio–temporal–spectral network outputs, where weighted fusion is implemented via dynamic weight optimization based on the spatio–temporal–spatial features. Experimental results indicate that the importance of temporal features is higher than that of spatial information, and spectral networks perform best among all network structures. After the spatio–temporal–spectral ensemble learning, the performance of tree species classification is further improved, and the overall accuracy (OA) of the proposed method reaches above 90%. The proposed algorithm realizes precise and fine-scale tree species classification and provides technique support for the monitoring and conservation of forest resource.
森林树种分类对森林资源的可持续发展具有重要意义。多源遥感数据为树种分类提供了丰富的时间、空间和光谱信息。然而,目前还缺乏全面捕获和融合时空光谱信息的树种分类方法。为此,提出了一种基于多源时空光谱遥感数据深度集成学习的树种分类方法。首先,多时相、高分辨率和高光谱数据被用于训练时间、空间和光谱深度网络。此外,针对时空光谱网络输出的融合,开发了深度集成学习,通过基于时空特征的动态权重优化实现加权融合。实验结果表明,时间特征的重要性高于空间信息,频谱网络在所有网络结构中表现最好。经过时空光谱集成学习,进一步提高了树种分类性能,总体准确率达到90%以上。该算法实现了精确、精细的树种分类,为森林资源的监测和保护提供了技术支持。
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引用次数: 0
YOLO-MFG: Multiscale and Feature-Preserving YOLO With Gated Attention for Remote Sensing Object Detection YOLO- mfg:基于门控关注的多尺度特征保持YOLO遥感目标检测
HengYu Li;Bo Huang;JianYong Lv
Driven by the increasing demand for intelligent Earth observation and large-scale scene understanding, remote sensing object detection has gained significant academic and practical importance. Despite notable progress in feature extraction and computational efficiency, many recent approaches still struggle to effectively handle issues such as detecting objects at multiple scales and preserving small targets. In this letter, an efficient remote sensing object detector called multiscale and feature-preserving YOLO with gated attention (YOLO-MFG) is proposed to address these challenges. First, a multiscale group shuffle attention (MGSA) module is introduced to adaptively aggregate multiscale spatial features, improving the model’s sensitivity to objects of diverse sizes. Second, the use of feature-preserving downsampling (FPD) enhances the downsampling process by introducing a triple-branch fusion mechanism that mitigates aliasing while jointly preserving semantics, saliency, and geometry. Finally, gated enhanced attention (GEA) is integrated to capture long-range dependencies and contextual cues crucial for remote sensing scenarios. The experimental results demonstrate that the proposed YOLO-MFG achieves a 2.9% improvement in mean average precision at an intersection over union (IoU) threshold of 0.5 (mAP50) on the optical remote sensing dataset SIMD compared with YOLO11. In addition, the mAP50 of detection results is improved by 1.4% and 4.2% on the DIOR and NWPU VHR-10 datasets, respectively.
在智能对地观测和大尺度场景理解需求日益增长的推动下,遥感目标检测具有重要的理论和实践意义。尽管在特征提取和计算效率方面取得了显著进展,但许多新方法仍然难以有效地处理多尺度目标检测和小目标保存等问题。为了解决这些问题,本文提出了一种高效的遥感目标检测器,称为多尺度和特征保持的门控注意力YOLO (YOLO- mfg)。首先,引入多尺度群体洗牌注意(MGSA)模块自适应聚合多尺度空间特征,提高模型对不同大小目标的敏感性;其次,使用特征保持下采样(FPD)通过引入三分支融合机制来增强下采样过程,该机制在共同保持语义、显著性和几何形状的同时减轻了混化。最后,集成了门控增强注意力(GEA),以捕获遥感场景中至关重要的远程依赖关系和上下文线索。实验结果表明,在光学遥感数据集SIMD上,与YOLO11相比,YOLO-MFG在0.5 (mAP50)的交汇交汇(IoU)阈值下的平均精度提高了2.9%。在DIOR和NWPU VHR-10数据集上,检测结果的mAP50分别提高了1.4%和4.2%。
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引用次数: 0
The Sparse Adaptive Generalized S Transform 稀疏自适应广义S变换
Shengyi Wang;Xuehua Chen;Cong Wang;Junjie Liu;Xin Luo
High-resolution time–frequency analysis is crucial for seismic interpretation. Conventional sparse time–frequency transforms, such as the sparse generalized S transform (SGST), are not adaptive to the intrinsic characteristics of the signal. To address this limitation, we propose a sparse adaptive generalized S transform (SAGST). This method incorporates the signal amplitude spectrum into the Gaussian window function, allowing the window to adapt dynamically to the signal characteristics. This adaptive mechanism enables the construction of wavelet bases that are better matched to the signal. We apply the SAGST to the time–frequency analysis of both synthetic signal and field seismic data. The synthetic signal test shows that the SAGST achieves higher energy concentration, superior computational efficiency, and enhanced weak signal extraction compared with the sparse adaptive S transform (SAST) and SGST. A field example demonstrates that the SAGST can be used to indicate low-frequency shadow associated with hydrocarbon reservoirs.
高分辨率时频分析是地震解释的关键。传统的稀疏时频变换,如稀疏广义S变换(SGST),不能适应信号的固有特性。为了解决这一限制,我们提出了一种稀疏自适应广义S变换(SAGST)。该方法将信号幅度谱纳入高斯窗函数,使窗口能够动态适应信号特性。这种自适应机制使得构建与信号更好匹配的小波基成为可能。我们将SAGST应用于合成信号和现场地震资料的时频分析。合成信号测试表明,与稀疏自适应S变换(SAST)和SGST相比,SAGST的能量集中度更高,计算效率更高,弱信号提取能力更强。现场实例表明,SAGST可以用于识别与油气藏相关的低频阴影。
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引用次数: 0
ARTEA: A Multistage Adaptive Preprocessing Algorithm for Subsurface Target Enhancement in Ground Penetrating Radar 探地雷达地下目标增强多阶段自适应预处理算法
Wenqiang Ding;Changying Ma;Xintong Dong;Xuan Li
The heterogeneity of subsurface media induces multipath scattering and dielectric loss in ground penetrating radar (GPR) signal propagation, which results in wavefront distortion and signal attenuation. These effects degrade B-scan profiles by blurring target signatures, hindering automated feature extraction, and reducing the clarity of regions of interest (ROI). To address these issues, we propose the adaptive region target enhancement algorithm (ARTEA), a multistage preprocessing framework. ARTEA integrates dynamic range compression, continuous-scale normalization guided by adaptive sigma maps, and a frequency-domain refinement step. By dynamically adjusting parameters according to local signal characteristics, ARTEA is designed to achieve an effective tradeoff between artifact suppression and target preservation. Experiments on both synthetic and field GPR data demonstrate that ARTEA can enhance target contrast and structural fidelity while suppressing artifacts and preserving essential target features.
地下介质的非均质性导致探地雷达信号在传播过程中产生多径散射和介质损耗,从而导致波前畸变和信号衰减。这些影响通过模糊目标签名、阻碍自动特征提取和降低感兴趣区域(ROI)的清晰度来降低b扫描配置文件。为了解决这些问题,我们提出了一种多阶段预处理框架——自适应区域目标增强算法(ARTEA)。ARTEA集成了动态范围压缩,由自适应西格玛图引导的连续尺度归一化和频域细化步骤。ARTEA通过根据局部信号特征动态调整参数,实现了伪影抑制和目标保存的有效平衡。在合成和实地GPR数据上的实验表明,ARTEA可以在抑制伪影和保留目标基本特征的同时增强目标对比度和结构保真度。
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引用次数: 0
A Lightweight Multifeature Hybrid Mamba for Remote Sensing Image Scene Classification 一种用于遥感影像场景分类的轻型多特征混合曼巴算法
Huihui Dong;Jingcao Li;Zongfang Ma;Zhijie Li;Mengkun Liu;Xiaohui Wei;Licheng Jiao
Remote sensing (RS) image scene classification has wide applications in the field of RS. Although the existing methods have achieved remarkable performance, there are still limitations in feature extraction and lightweight design. Current multibranch models, although performing well, have large parameter counts and high computational costs, making them difficult to deploy on resource-constrained edge devices, such as uncrewed aerial vehicles (UAVs). On the other hand, lightweight models like StarNet, having less parameter, but rely on elementwise multiplication to generate features and lack the capture of explicit long-range spatial feature, resulting in insufficient classification accuracy. To address these issues, this letter proposes a lightweight mamba-based hybrid network, namely LMHMamba, whose core is an innovative lightweight multifeature hybrid Mamba (LMHM) module. This module combines the advantage of StarNet in implicitly generating high-dimensional nonlinear features, introduces a lightweight state-space module to enhance spatial feature learning capabilities, and then uses local and global attention modules to emphasize local and global features. This enables effective multidimensional feature fusion while maintaining low parameter. We validate the performance of LMHMamba model on three RS scene classification datasets and compare it with mainstream lightweight models and the latest methods. Experimental results show that LMHMamba achieves advanced levels in both classification accuracy and computational efficiency, significantly outperforming the existing lightweight models, providing an efficient solution for edge deployment. Code is available at https://github.com/yizhilanmaodhh/LMHMamba
遥感图像场景分类在遥感领域有着广泛的应用,现有方法虽然取得了显著的成绩,但在特征提取和轻量化设计等方面仍存在局限性。目前的多分支模型虽然性能良好,但参数数量大,计算成本高,难以部署在资源受限的边缘设备上,如无人驾驶飞行器(uav)。另一方面,像StarNet这样的轻量级模型,参数较少,但依赖于元素乘法来生成特征,缺乏明确的远程空间特征的捕获,导致分类精度不足。为了解决这些问题,这封信提出了一个轻量级的基于曼巴的混合网络,即LMHMamba,其核心是一个创新的轻量级多功能混合曼巴(LMHM)模块。该模块结合StarNet在隐式生成高维非线性特征方面的优势,引入轻量级状态空间模块增强空间特征学习能力,利用局部和全局关注模块强调局部和全局特征。这使得有效的多维特征融合,同时保持低参数。在三个RS场景分类数据集上验证了LMHMamba模型的性能,并与主流轻量化模型和最新方法进行了比较。实验结果表明,LMHMamba在分类精度和计算效率方面都达到了先进水平,显著优于现有的轻量化模型,为边缘部署提供了高效的解决方案。代码可从https://github.com/yizhilanmaodhh/LMHMamba获得
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引用次数: 0
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IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society
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