International Journal of Applied Earth Observation and Geoinformation最新文献

{"title":"A downscaling algorithm for obtaining hourly gross primary productivity maps at the global scale","authors":"Yong Wang, Jiyan Wang, Wei Zhao, Yanqing Yang, Jiujiang Wu, Xiaobin Guan, Xinyao Xie","doi":"10.1016/j.jag.2025.105059","DOIUrl":"https://doi.org/10.1016/j.jag.2025.105059","url":null,"abstract":"Monitoring global vegetation gross primary productivity (GPP) at an hourly scale is critical for understanding terrestrial carbon dynamics, while recent global GPP products often suffer from limitations in their temporal resolutions. Here, a light use efficiency (LUE) model, integrated with FLUXNET and reanalysis datasets as meteorological inputs, was employed to obtain GPP at both 1-hourly and 6-hourly resolutions. We developed a downscaling algorithm that partitions 6-hourly GPP into 1-hourly estimates by weighting the 6-hourly values according to the hourly cosine of the solar zenith angle and applying linear regression. The algorithm was then applied to global 6-hourly reanalysis-driven GPP maps during 2001–2020. Using GPP simulated from 1-hourly meteorological inputs and eddy covariance (EC) GPP as references, the 6-hourly resolution GPP before and after downscaling were evaluated by mean-absolute-deviation (<ce:italic>MAD</ce:italic>) and nash–sutcliffe-efficiency (<ce:italic>NSE</ce:italic>). At 150 sites, results showed that the 6-hourly FLUXNET-driven and reanalysis-driven GPP after downscaling exhibited a significantly stronger correlation (<ce:italic>MAD</ce:italic> = 0.03 gCm<ce:sup loc=\"post\">−2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup>, <ce:italic>NSE</ce:italic> = 0.95) with corresponding 1-hourly estimates than the 6-hourly GPP estimates before downscaling (<ce:italic>MAD</ce:italic> = 0.06 gCm<ce:sup loc=\"post\">−2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup>, <ce:italic>NSE</ce:italic> = 0.83). Compared to EC GPP, the 6-hourly GPP estimates after downscaling also achieved notable improvements, with a <ce:italic>MAD</ce:italic> lower by 0.02 gCm<ce:sup loc=\"post\">−2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup> and an <ce:italic>NSE</ce:italic> higher by 0.09. At the global scale, the mean annual bias in total GPP summed from 6-hourly reanalysis-driven maps, decreased from 4.14 gCyr<ce:sup loc=\"post\">−1</ce:sup> before downscaling to 0.53 gCyr<ce:sup loc=\"post\">−1</ce:sup> after downscaling over the period 2001–2020, as compared with corresponding 1-hourly GPP maps. At the hourly scale, the mean relative error between the 6-hourly and corresponding 1-hourly GPP maps decreased from 32.40% before downscaling to 18.49% after downscaling. This downscaling algorithm effectively reduces biases in global GPP estimates, which could offer valuable insights into carbon modeling at finer temporal resolutions.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"38 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal large language models meet self-supervised diffusion for real-world aerial image super-resolution","authors":"Lijing Lu, Zhou Huang, Yi Bao, Lin Wan, Zhihang Li","doi":"10.1016/j.jag.2026.105136","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105136","url":null,"abstract":"Real-world aerial image super-resolution (SR) remains particularly challenging because degradations in remote-sensing imagery involve random combinations of anisotropic blur, signal-dependent noise, and unknown downsampling kernels. Most existing SR methods either rely on simplified degradation assumptions or lack semantic perception of degradation, resulting in limited generalization to real-world conditions. To address these gaps, we propose a novel diffusion-based SR framework that integrates Multi-modal Large Language Models (MLLMs) and self-supervised contrastive learning for extracting degradation-insensitive representation. Specifically, we introduce a contrastive learning strategy into a ControlNet module, where the HR and LR counterparts of the same image are regarded as positive pairs, while representations from different images serve as negative pairs, enabling the network to learn degradation-insensitive structural features. To further enhance semantic awareness of degradation, an MLLM-generated change caption is incorporated into the diffusion process as textual guidance, allowing the model to explicitly perceive and reconstruct different degradation types. Moreover, a classifier-free guidance (CFG) distillation strategy compresses the original dual-branch diffusion model into a single lightweight network, substantially improving inference efficiency while maintaining high reconstruction fidelity. Extensive experiments conducted on various datasets have showcased the superior performance of our proposed model compared to existing state-of-the-art methods. Furthermore, our distillation algorithm achieves a twofold reduction in inference time compared to its non-distilled counterpart, making it more feasible for real-time and resource-limited applications.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"47 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big data-driven spectral index construction for fine-scale salt pond mapping","authors":"Jin Zhang, Chong Huang, He Li, Shuxuan Wang, Qingsheng Liu, Xiaoya Tang, Chenchen Zhang, Fenzhen Su","doi":"10.1016/j.jag.2026.105139","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105139","url":null,"abstract":"Accurate mapping of salt pond systems is crucial for coastal resource management and ecological conservation. However, existing spectral indices for salt pond detection are mostly derived empirically, limiting their ability to differentiate pond subtypes or generalize across diverse coastal environments. This study proposes a big data-driven framework for constructing novel spectral indices that enhance the fine-scale classification of salt ponds in the Bohai Rim region of China. By systematically generating and screening over 390,000 candidate indices derived from Sentinel-2 bands, four new indices were identified to effectively discriminate salt pond subtypes: the Non-Water Body Detection Index (NWBDI), Crystallization Pond Index (CPI), Red-Green Concentration Pond Index (RGCPI), and Evaporation Pond Discrimination Index (EPDI). To rigorously evaluate their performance, three Random Forest configurations were compared, including a baseline model with original spectral bands, an expert-index-enhanced model, and a model incorporating the proposed novel indices. Across the Bohai Rim region, the model with proposed new indices achieved an overall accuracy (OA) of 81.14%, demonstrating clear advantages over both original spectral bands (OA of 72.93%) and expert-index approaches (OA of 76.64%). The resulting fine-scale map revealed a total salt pond area of 2,214.33 km<ce:sup loc=\"post\">2</ce:sup> in 2020 across the Bohai Rim, consistent with national statistics. Beyond improving classification accuracy, the data-driven index discovery revealed physically meaningful spectral relationships linked to salinity gradients and hydro-biogeochemical properties, marking a methodological shift from expert-driven hypothesis testing to automated, data-driven feature discovery. This study demonstrates that the data-driven approach can provide a transferable solution for constructing task-specific spectral indices and advancing large-scale environmental monitoring.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"18 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-in-the-loop based framework for solid waste dumps detection in remote sensing images","authors":"Luhan Wang, Pengfeng Xiao, Xueliang Zhang, Yina Song, Lei Guo, Di Wang, Yao Zhang","doi":"10.1016/j.jag.2026.105133","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105133","url":null,"abstract":"Detecting solid waste dumps (SWDs) is crucial for a sustainable environment and public health. Deep learning methods have demonstrated significant potential in detecting SWDs from high-spatial resolution remote sensing images (RSIs). However, training a SWD detection model necessitates annotated data that encompasses diverse geographical distributions and styles. Following the conventional approach of manually labeling data prior to model training is both costly and time-consuming. In this study, we propose a human-in-the-loop framework for the detection of SWDs. An initial model is trained using public datasets of Dumpsites, solid waste aerial detection (SWAD), and AerialWaste to identify potential samples from extensive unlabeled RSIs, which are then validated by experts for data expansion and model reinforcement. Specifically, multi-view inference is introduced to enhance the applicability of the model for real-world SWD detection tasks by integrating inference results from multiple style-transformed images. Moreover, we utilize adaptive thresholds that are dynamically calculated from inference results in each round to select potential SWDs, all while maintaining a low computational cost. With the proposed framework, we construct the LHRS-SWD dataset for SWD detection, derived from a random sampling of over 70 countries and encompassing 3,377 SWDs. The effectiveness of our framework is validated through experiments on LHRS-SWD, with a 20.26% improvement in AP50 versus initial iteration.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"89 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach for Quasi-Global daily continuous surface soil moisture downscaling at 500-m resolution using CYGNSS observations","authors":"Jundong Wang, Ting Yang, Wanxue Zhu, Shiji Li, Zixuan Tang, Wei Wan, Zhigang Sun","doi":"10.1016/j.jag.2026.105137","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105137","url":null,"abstract":"Accurate Surface Soil Moisture (SSM) with high spatiotemporal resolution is essential for hydrological modeling and agricultural applications. The Cyclone Global Navigation Satellite System (CYGNSS) provides quasi-global SSM with relatively high temporal resolution, while its coarse spatial resolution limits regional usability. This study proposed a novel approach to generate daily continuous quasi-global SSM at 500-m resolution by integrating CYGNSS observations with optical imagery, without relying on in-situ SSM inputs. The method includes: (1) reconstruction of daily Surface Reflectivity (SR) using the Previously-Observed Behavior Interpolation (POBI) algorithm and retrieval of 9-km SSM via a modified Reflectivity-Vegetation-Roughness (R-V-R) model; (2) constructing a direct relationship between SSM and optical reflectance using the enhanced OPtical TRApezoid Model (OPTRAM) as the foundational basis for subsequent downscaling process; (3) downscale coarse-resolution CYGNSS SSM using the Bayesian algorithm without requiring any in-situ SSM observations as inputs. The framework is applied over August 2019–2022 and validated against in-situ SSM from 194 International Soil Moisture Network (ISMN) sites across diverse climate and land cover conditions. Results show improved spatial detail while maintaining accuracy, with a mean unbiased root-mean-square error (ubRMSE) of 0.061 cm<ce:sup loc=\"post\">3</ce:sup>/cm<ce:sup loc=\"post\">3</ce:sup> and a positive G<ce:inf loc=\"post\">DOWN</ce:inf> value of 0.017, indicating effective SSM downscaling. In addition, the proposed method outperforms both Linear and Random Forest models while maintaining robust performance. Overall, it offers a scalable solution for generating high-resolution, daily SSM products directly from satellite data.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"31 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KFIA-Net: a knowledge fusion and imbalance-aware network for multi-category SAR ship detection","authors":"Zhongzhen Sun, Xianghui Zhang, Xiangguang Leng, Xueqi Wu, Boli Xiong, Kefeng Ji, Gangyao Kuang","doi":"10.1016/j.jag.2026.105127","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105127","url":null,"abstract":"Multi-category synthetic aperture radar (SAR) ship detection is limited by heterogeneity in imaging mechanisms and severe class imbalance, yielding accurate localization but frequent misclassification. To address this issue, this paper proposes a Knowledge Fusion and Imbalance-Aware Network (KFIA-Net). Specifically, we first propose a Domain Knowledge Feature Extraction (DKFE) to extract and encode knowledge tokens from four priors. Second, a Knowledge Cross-Attention Fusion (KCAF) module is designed to perform interpretable and sparsely selectable channel modulation using cross-attention and FiLM decoding. Thirdly, we further design an Imbalance-Aware Loss Function (IALF) that combines prior calibration, minority category margin expansion, and knowledge-consistency weighting to reduce loss bias. Finally, systematic experiments and comparisons are conducted on three datasets: SRSDD-v1.0, FAIR-CSAR-v1.0, and NUDT-SARship-v1.0. Our KFIA-Net achieves mAP<ce:inf loc=\"post\">50</ce:inf> scores of 64.29%, 37.99%, and 78.26%, and mAP<ce:inf loc=\"post\">75</ce:inf> scores of 34.96%, 19.70%, and 66.36%, respectively. These results demonstrate knowledge injection simultaneously improves class accuracy and sustains robust localization. Furthermore, KFIA-Net requires only 11.47 M parameters and 66.79G FLOPs, achieving an inference speed of 47.21 FPS on a 1024 × 1024 input, achieving a good trade-off between accuracy and efficiency.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"236 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic modeling of InSAR-derived land subsidence hazard in New York City for transportation infrastructure damage risk assessments","authors":"Ntambila Daud, Oluwaseyi Dasho, Manoochehr Shirzaei","doi":"10.1016/j.jag.2026.105118","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105118","url":null,"abstract":"Land subsidence is a growing geohazard that poses a significant threat to critical infrastructure, particularly in urban coastal cities. This study uses Interferometric Synthetic Aperture Radar (InSAR) data from 2016 to 2024 to estimate angular distortion rates to assess infrastructure damage risk in New York City. We applied a probabilistic framework to evaluate multiple “what-if” scenarios and project long-term risks, providing actionable insights for resilience and mitigation planning. Results reveal persistent subsidence in low-elevation and reclaimed zones (∼-5 mm/yr) with localized uplift (∼+1.5 mm/yr), affecting major airports, subway segments, and highways. Fifty-year projections indicate high angular distortion probabilities (0.6–0.8), with economic exposure estimated at ∼$8.20 billion for ∼ 6.1 km of subway lines and ∼$10.54 billion for ∼ 7.8 km of highways exceeding –2 mm/yr. Despite their limited spatial extent, these segments represent a disproportionately large share of total exposure. The findings emphasize the need for continuous monitoring, proactive mitigation, and targeted investment, highlighting the value of integrating geodetic data with probabilistic modeling to address subsidence and climate-related hazards.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"26 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quad-pol reconstruction of dual-pol SAR data via a physically constrained diffusion model for building damage assessment","authors":"Zihuan Guo, Hong Zhang, Xiao-Ming Li, Yukun Fan, Haoxuan Duan, Qiming Zeng, Ji Ge, Chao Wang","doi":"10.1016/j.jag.2026.105132","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105132","url":null,"abstract":"Quad-polarimetric (quad-pol) synthetic aperture radar (SAR) data provides crucial polarimetric information for post-disaster building damage assessment. However, most current spaceborne SAR platforms prioritize dual-polarization (dual-pol) mode, which ensures high temporal and spatial data availability but limits damage analysis accuracy due to the absence of some polarimetric information. Existing methods for reconstructing dual-pol to quad-pol SAR data often fail to ensure that the reconstructed data meets fundamental physical properties, while traditional building damage detection methods still struggle to accurately capture complex depolarization effects. To address these challenges, this paper proposes a diffusion model-based method for reconstructing dual-pol data to quad-pol data, applied to post-earthquake building damage analysis. The method introduces a Positive Semi-definite Constraint Module and a Plug-and-Play SVD Parameter Fine-tuning Module to ensure the physical validity and accuracy of the reconstructed data. Additionally, a Stokes vector-based Degree of Polarization frequency analysis method is proposed to enhance the description of depolarization information. A multi-dimensional polarimetric feature combination is constructed for grid-level building damage assessment. Experiments on Gaofen-3, ALOS-2/PALSAR-2, and Sentinel-1 data show that the proposed method performs optimally in complex scenarios, with all pixels meeting the positive semi-definite constraint. Compared to the original dual-pol SAR data, building damage assessment using the reconstructed quad-pol SAR data resulted in an F1 score improvement of 16.3% and 8.4% for detecting moderately and severely damaged buildings, respectively. This research provides crucial technical support for fully harnessing the potential of dual-pol SAR data in building damage assessment.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"89 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Resolution (0.9 m) terrace mapping in Low-Latitude hilly regions using deep learning and Area–Slope Denoising: A case study from Guangdong Province, China","authors":"Yinghai Zhao, Hanquan Cheng, Siyuan Peng, Suhong Liu, Yun Xie, Baoyuan Liu","doi":"10.1016/j.jag.2026.105126","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105126","url":null,"abstract":"Terrace construction is a crucial human intervention for improving slope productivity and preventing soil erosion, especially in low-latitude hilly regions with heavy and concentrated rainfall. However, existing terrace mapping methods perform poorly in such areas. In this study, we generated the first high-resolution (0.9 m) terrace distribution map for low-latitude hilly regions by integrating deep learning with slope-area threshold denoising. Using Guangdong Province as a case study, we combined Google Earth imagery, SRTM DEM, and Global 10-m land-cover data. The results showed that the optimal 0.9 m resolution achieved 93.34 % overall accuracy (OA), 79.18 % F1-score, and 65.53 % intersection over union (IoU) on our Guangdong terrace dataset, processing 400 km<ce:sup loc=\"post\">2</ce:sup> in 89 min, showing an excellent accuracy–speed trade-off. Statistical analysis revealed that terraces in Guangdong are predominantly small (4725 m<ce:sup loc=\"post\">2</ce:sup> on average) and gently sloped (8.85°). Provincial validation confirmed superior performance, with a producer’s accuracy of 80.38 %. Spatially, terraces are mainly clustered in inland hilly areas, especially in northern Guangdong, with additional clusters in the peripheral zones of the Pearl River Delta and in the western and eastern regions. Cities with the largest terrace coverage are Qingyuan (801.98 km<ce:sup loc=\"post\">2</ce:sup>), Zhaoqing (733.11 km<ce:sup loc=\"post\">2</ce:sup>), and Shaoguan (701.53 km<ce:sup loc=\"post\">2</ce:sup>). This high-precision dataset supports agricultural management, soil erosion risk assessment, ecological conservation, and rural revitalisation, while also offering a transferable framework for mapping terrain features in complex landscapes worldwide using remote sensing imagery.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"84 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dual pixel-level and subpatch-level network with cross-temporal super resolution for change detection across spatial resolutions","authors":"Dawei Wen, Yunlong Zhang, Binqiang Zhang, Deng Chen, Xiaofeng Pan, Xin Huang","doi":"10.1016/j.jag.2026.105134","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105134","url":null,"abstract":"Remote sensing image change detection is critical for monitoring earth surface dynamics. Although deep learning has significantly improved change detection performance, traditional and existing deep super-resolution techniques for cross-resolution change detection often assume bi-temporal images share the same resolution, at least in the training phase. They also suffer from limitations including dependency on expensive high-resolution paired training data, suboptimal performance transfer from super-resolution to change detection accuracy, and heavy reliance on extensive pixel-level annotations. To address these limitations, we propose a novel dual pixel-level and subpatch-level network with cross-temporal super resolution (DPSNet) for change detection across spatial resolutions. Our method DPSNet, comprises two core components: 1) a Reference Image-Guided Generative Adversarial Network (RefIGM GAN) for cross-temporal super resolution; and 2) a Semi-supervised Dual-Path Network (SDNet) for pixel-level and subpatch-level change detection. A resource-efficient alternating optimization strategy is employed between RefIGM GAN and SDNet, creating a virtuous cycle in which super-resolution improves detection accuracy, and detection results optimize super-resolution reconstruction. Experiments were conducted on three datasets, i.e., CDD, SYSU, and HTCD, each characterized by distinct resolution variations. The CDD and SYSU datasets include bi-temporal images with 4 × and 8 × resolution differences, respectively, while the HTCD dataset contains both satellite and UAV imagery with inherent resolution disparities. The results demonstrate that by integrating reference-guided super resolution and semi-supervised learning, effective cross-resolution change detection can be achieved with only limited high-resolution data and pixel-level labels, showing great practical significance in scenarios where solely low-resolution historical images are available. Our source code will be released at <ce:inter-ref xlink:href=\"https://github.com/Flandre7155/DPSNet\" xlink:type=\"simple\">https://github.com/Flandre7155/DPSNet</ce:inter-ref>.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"29 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}