ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Multi-frequency tomographic SAR: A novel 3-D imaging configuration for limited acquisitions","authors":"Jian Zhao ,&nbsp;Zegang Ding ,&nbsp;Zhen Wang ,&nbsp;Tao Sun ,&nbsp;Kaiwen Zhu ,&nbsp;Yuhan Wang ,&nbsp;Zehua Dong ,&nbsp;Linghao Li ,&nbsp;Han Li","doi":"10.1016/j.isprsjprs.2025.02.029","DOIUrl":"10.1016/j.isprsjprs.2025.02.029","url":null,"abstract":"<div><div>Tomographic synthetic aperture radar (TomoSAR) technology, as an extension of interferometric SAR (InSAR), solves the layover problem and realizes three-dimensional (3-D) imaging. Now, it is an important research direction in the field of radar imaging. However, TomoSAR usually requires the SAR sensor to make enough acquisitions at different spatial locations to achieve high-quality 3-D imaging, which is high time-costly and inefficient. To solve this problem, we extend multi-frequency (MF) InSAR and propose a novel SAR 3-D imaging configuration: MF-TomoSAR. MF-TomoSAR utilizes limited acquisitions with enhanced degrees of freedom (DOF) in frequency to accomplish 3-D imaging. It can achieve a similar imaging quality as the traditional TomoSAR while significantly improving 3-D imaging efficiency. The main contributions are summarized as follows: First, inspired by the idea of extending multi-baseline (MB) InSAR to TomoSAR, the single baseline (SB) MF-TomoSAR signal model is proposed. The SBMF-TomoSAR model utilizes interferometric processing to eliminate the effects of scattering changes due to different working frequencies (WFs). In the extreme case of only one fixed baseline, multiple interferograms with different WFs can be considered as samples at different spatial frequencies (SFs) to achieve 3-D imaging through spectral estimation. Then, in order to solve the sampling limitation caused by a fixed baseline, the MF-TomoSAR configuration is generalized to a general case of multiple baselines, and the MBMF-TomoSAR signal model is proposed. The MBMF-TomoSAR model realizes SF sampling through different WFs with multiple baselines to achieve sampling expansion and ensure the 3-D imaging quality. Finally, the MF-TomoSAR processing framework is proposed with the baseline distribution optimization method. The MF-TomoSAR configuration (either SB or MB) does not change the essence of spectral estimation in tomographic processing, and the classical tomographic processing algorithms can be directly applied to MF-TomoSAR processing. The computer simulation and unmanned aerial vehicle (UAV) SAR 3-D imaging experiment verify the effectiveness of the proposed MF-TomoSAR configuration.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"223 ","pages":"Pages 91-108"},"PeriodicalIF":10.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite-Based energy balance for estimating actual sugarcane evapotranspiration in the Ethiopian Rift Valley","authors":"Gezahegn W. Woldemariam ,&nbsp;Berhan Gessesse Awoke ,&nbsp;Raian Vargas Maretto","doi":"10.1016/j.isprsjprs.2025.03.003","DOIUrl":"10.1016/j.isprsjprs.2025.03.003","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Satellite-derived actual evapotranspiration (&lt;em&gt;ETa&lt;/em&gt;) maps are essential for the development of innovative water management strategies. Over the past decades, multiple novel satellite remote sensing-based surface energy balance (SEB) &lt;em&gt;ETa&lt;/em&gt; modeling tools have been widely used to account for field-scale crop water use and irrigation monitoring. However, their predictive capabilities for intensively irrigated commercial sugarcane plantations in the semiarid ecosystems of the Main Ethiopian Rift remain unclear. In this study, we applied and evaluated the comparative performance of four well-established SEB models–SEBAL (Surface Energy Balance Algorithm for Land), METRIC (Mapping Evapotranspiration with Internalized Calibration), SSEB (Simplified Surface Energy Balance), and SSEBop (Operational Simplified Surface Energy Balance)–to estimate &lt;em&gt;ETa&lt;/em&gt; using Landsat imagery and weather measurements for the 2021–2022 season, along with an independent validation benchmark, actual evapotranspiration and interception (ETIa), and sugarcane evapotranspiration (ETc) data over irrigated sugarcane monoculture fields at the Metehara Sugar Estate in the Ethiopian Rift Valley. Cumulatively, the Landsat &lt;em&gt;ETa&lt;/em&gt; maps derived from the SEB models tracked spatially explicit patterns in the temporal dynamics of sugarcane water use footprint with a higher coefficient of determination (&lt;em&gt;R&lt;sup&gt;2&lt;/sup&gt;&lt;/em&gt;) of ≥ 0.90, with irrigation consumption accounting for more than 80 % of the water fluxes. At the field scale, SSEBop estimated average &lt;em&gt;ETa&lt;/em&gt; with superior accuracy (&lt;em&gt;R&lt;sup&gt;2&lt;/sup&gt; ≥&lt;/em&gt; 0.96; root mean square error (RMSE) = 0.29–5.9 mm; Nash-Sutcliffe model efficiency coefficient (NSE) = 0.86–0.92), resulting in a strong agreement with ETIa (&lt;em&gt;d&lt;/em&gt; = 0.95–0.98) and lower percentage bias (PBIAS ≈ 4 %), followed by SSEB (&lt;em&gt;R&lt;sup&gt;2&lt;/sup&gt;&lt;/em&gt; ≥ 0.91; RMSE = 0.25–12 mm, NSE = 0.64–0.89, PBIAS ≤ 8 %), while SEBAL and METRIC estimated &lt;em&gt;ETa&lt;/em&gt; with higher relative mean errors (RMSE = 0.83–24 mm) and PBIAS of 17 %. We found a reasonable concordance of the model-predicted average &lt;em&gt;ETa&lt;/em&gt; with ETIa and ETc values during the early sugarcane growth phases, with a higher deviation during the mid-peak atmospheric demand season and late growth phases. The estimated annual &lt;em&gt;ETa&lt;/em&gt; (mm yr&lt;sup&gt;−1&lt;/sup&gt;) ranged from 1303 to 1628 (2021) and 1185–1737 (2022), resulting in a two-year (2021–2022) average-of 1318–1682 mm and seasonal &lt;em&gt;ETa&lt;/em&gt; of 2238–2673 mm. Furthermore, we established a hierarchical rating method based on selected performance -metrics, which ranked the proposed models as follows: SSEBop &gt; SSEB &gt; METRIC &gt; SEBAL. In this sense, our findings showed how the optimal method for estimating &lt;em&gt;ETa&lt;/em&gt;, which serves as a proxy for -consumptive water use, can be prioritized for irrigated dryland crops with limited &lt;em&gt;in situ&lt;/em&gt; measurements by assimilating model sets with publicly available Earth observ","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"223 ","pages":"Pages 109-130"},"PeriodicalIF":10.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621000","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 cyanobacteria occurrence index derived from optical water types in a tropical lake","authors":"Davide Lomeo ,&nbsp;Stefan G.H. Simis ,&nbsp;Xiaohan Liu ,&nbsp;Nick Selmes ,&nbsp;Mark A. Warren ,&nbsp;Anne D. Jungblut ,&nbsp;Emma J. Tebbs","doi":"10.1016/j.isprsjprs.2025.03.006","DOIUrl":"10.1016/j.isprsjprs.2025.03.006","url":null,"abstract":"<div><div>Cyanobacteria blooms are a threat to water quality of lakes and reservoirs worldwide, requiring scalable monitoring solutions. Existing approaches for remote sensing of cyanobacteria focus on quantifying (accessory) photosynthetic pigment to map surface accumulations. These approaches have proven challenging to validate against in situ observations, limiting uptake in water quality management. Optical Water Types (OWTs) have been used in inland and ocean waters to dynamically select suitable algorithms over optical gradients, thereby helping to limit out-of-scope application of individual algorithms. Here, we present a proof-of-concept study in Winam Gulf, Lake Victoria, extending an existing OWT framework using a hybrid approach combining in situ and satellite-derived water types. This extended OWT set of 25 water types, obtained from K-means clustering &gt; 18 million Sentinel-3 Ocean and Land Colour Instrument (OLCI) spectra, was found to better capture the optical diversity of cyanobacteria bloom phases compared to the original OWT set. We translate this framework into a novel Cyanobacteria Occurrence Index (COI) by assigning weights to key optical features observed in the OWT set, such as phycocyanin absorption and surface accumulation. COI was strongly correlated with established algorithms for chlorophyll-<em>a</em> (Maximum Peak Height; <em>r</em> = 0.9) and phycocyanin (Simis07; <em>r</em> = 0.84), while potentially capturing various bloom phases in optically mixed conditions. We demonstrate how COI could be mapped onto a three-category risk classification to facilitate communication of cyanobacteria occurrence risk. Initial tests across diverse waterbodies suggest potential for wider application, though further validation across different environmental conditions is needed. This work provides a foundation for improved cyanobacteria monitoring in optically complex waters, particularly where conventional sampling approaches face limitations.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"223 ","pages":"Pages 58-77"},"PeriodicalIF":10.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated registration of forest point clouds from terrestrial and drone platforms using structural features","authors":"Yiliu Tan ,&nbsp;Xin Xu ,&nbsp;Hangkai You ,&nbsp;Yupan Zhang ,&nbsp;Di Wang ,&nbsp;Yuichi Onda ,&nbsp;Takashi Gomi ,&nbsp;Xinwei Wang ,&nbsp;Min Chen","doi":"10.1016/j.isprsjprs.2025.02.023","DOIUrl":"10.1016/j.isprsjprs.2025.02.023","url":null,"abstract":"<div><div>Light Detection and Ranging (LiDAR) technology has demonstrated significant effectiveness in forest remote sensing. Terrestrial Laser Scanning (TLS) and Drone Laser Scanning (DLS) systems reconstruct forest point clouds from distinct perspectives. However, a single-platform point cloud is insufficient for a comprehensive reconstruction of multi-layered forest structures. Therefore, registration of point clouds from multiple platforms is an important procedure for providing comprehensive three dimensional reconstruction of the trees for more accurate characterization in forest inventories. However, the irregular and intricate structures of forest scenes, which often lack easily recognizable geometric features such as lines and planes, present substantial challenges for existing registration algorithms, such as Coherent Point Drift(CPD), Fast Global Registration(FGR), and Four Points Congruent Sets(4PCS). To address these challenges, we develop a novel algorithm, namely <em>ForAlign</em>, for the registration of forest point clouds from TLS and DLS. Our algorithm incorporates a tree location-based matching procedure followed by dynamic programming for detailed alignment. It fully considers the issue of inconsistent point cloud density distributions from different platforms and utilizes differential entropy to identify subsets of points with consistent structural features from the two data sources. These subsets serve as the basis for point cloud alignment based on distribution information. To validate the generality and accuracy of the proposed <em>ForAlign</em>, we conducted experiments using both scanned and simulated data describing different forest environments. The results show that our method achieves superior performance, with an average translation error of 6.4 cm and a rotation error of 53.5 mrad, outperforming CPD, FGR, and 4PCS by 43.5%, 55.4%, and 44.0% in translation accuracy, and by 36.4%, 54.6%, and 42.4% in rotation accuracy, respectively. Our study demonstrates that <em>ForAlign</em> effectively mitigates the errors introduced by tree localization in the preprocessing steps caused by varying point densities in TLS and DLS datasets, successfully extracts corresponding tree features among complicated forest scenes, and enables a robust, automated end-to-end registration process. The source code of <em>ForAlign</em> and the dataset are available at <span><span>https://github.com/yiliutan/ForAlign</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"223 ","pages":"Pages 28-45"},"PeriodicalIF":10.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600789","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":"SAR altimeter 3-D localization with combined Delay Doppler Image and spatio-temporal echo similarity","authors":"Yu Wei,&nbsp;Weibo Qin,&nbsp;Fengming Hu","doi":"10.1016/j.isprsjprs.2025.02.027","DOIUrl":"10.1016/j.isprsjprs.2025.02.027","url":null,"abstract":"<div><div>The Synthetic Aperture Radar (SAR) altimeter is an active sensor, which is widely used in satellite microwave remote sensing. It can be also used for geophysical localization by evaluating the similarity between the acquired terrain profile and the prior data. However, typical factors, such as the linear assumption of terrain, high variation of the ground elevation, and wide beam width will degrade the positioning accuracy of localization. In this paper, a 3-D localization method combining Delay-Doppler Image (DDI) and spatio-temporal echo similarity is proposed for the SAR altimeter. Firstly, the signal model of the SAR altimeter for DDI imaging is established. Then, the image-matching algorithm for the DDI is developed to achieve localization in the along-track and height directions. Additionally, spatio-temporal similarity is used to deal with cross-track positioning errors. The main advantage of the proposed method is the reliable 3-D localization, especially for extreme radar configurations, such as large undulations and wide beam width. Experimental results based on both simulated and real data validate the method, showing a significant improvement in localization accuracy.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"223 ","pages":"Pages 46-57"},"PeriodicalIF":10.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600612","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":"Mobile robotic multi-view photometric stereo","authors":"Suryansh Kumar","doi":"10.1016/j.isprsjprs.2025.02.012","DOIUrl":"10.1016/j.isprsjprs.2025.02.012","url":null,"abstract":"<div><div>Multi-View Photometric Stereo (MVPS) is a popular method for fine-detailed 3D acquisition of an object from images. Despite its outstanding results on diverse material objects, a typical MVPS experimental setup requires a well-calibrated light source and a monocular camera installed on an immovable base. This restricts the use of MVPS on a movable platform, limiting us from taking MVPS benefits in 3D acquisition for mobile robotics applications. To this end, we introduce a new mobile robotic system for MVPS. While the proposed system brings advantages, it introduces additional algorithmic challenges. Addressing them, in this paper, we further propose an incremental approach for mobile robotic MVPS. Our approach leverages a supervised learning setup to predict per-view surface normal, object depth, and per-pixel uncertainty in model-predicted results. A refined depth map per view is obtained by solving an MVPS-driven optimization problem proposed in this paper. Later, we fuse the refined depth map while tracking the camera pose w.r.t the reference frame to recover globally consistent object 3D geometry. Experimental results show the advantages of our robotic system and algorithm, featuring the local high-frequency surface detail recovery with globally consistent object shape. Our work is beyond any MVPS system yet presented, providing encouraging results on objects with unknown reflectance properties using fewer frames without a tiring calibration and installation process, enabling computationally efficient robotic automation approach to photogrammetry. The proposed approach is nearly 100 times computationally faster than the state-of-the-art MVPS methods such as Kaya et al., (2023), Kaya et al., (2022) while maintaining the similar results when tested on subjects taken from the benchmark DiLiGenT MV dataset (Li et al., 2020). Furthermore, our system and accompanying algorithm is data-efficient, i.e., it uses significantly fewer frames at test time to perform 3D acquisition<span><span>¹</span></span></div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"223 ","pages":"Pages 15-27"},"PeriodicalIF":10.6,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580722","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":"SDCluster: A clustering based self-supervised pre-training method for semantic segmentation of remote sensing images","authors":"Hanwen Xu ,&nbsp;Chenxiao Zhang ,&nbsp;Peng Yue ,&nbsp;Kaixuan Wang","doi":"10.1016/j.isprsjprs.2025.02.021","DOIUrl":"10.1016/j.isprsjprs.2025.02.021","url":null,"abstract":"<div><div>Reducing the reliance of remote sensing semantic segmentation models on labeled training data is essential for practical model deployment. Self-supervised pre-training methods, which learn representations from unlabeled data by designing pretext tasks, provide an approach to address this requirement. One inconvenience of the currently contrastive learning-based and masked image modeling-based self-supervised methods is the difficulty in evaluating the quality of the pre-trained model without fine-tuning for semantic segmentation task. Hence, this paper proposes a pixel-level clustering-based self-supervised learning method, named SDCluster, which allows for a qualitative evaluation of the pre-trained model through visualizing the clustering results. Specifically, SDCluster extends the self-distillation framework to the pixel-level by incorporating the clustering assignment module. Then, clustering constraint modules, including prototype constraint module and semantic consistency constraint module, are designed to eliminate ineffective cluster prototypes and preserve the semantic information of ground objects. Benefiting from the correlation between pixel-level clustering and per-pixel classification of semantic segmentation, experimental results indicate that SDCluster exhibits competitive fine-tuning accuracy and robust few-shot segmentation capabilities when compared to prevalent self-supervised methods. Large-scale pre-training experiment and practical application experiment also prove the generalization ability and extensibility of the proposed method. The code and the dataset for practical application experiment are available at <span><span>https://github.com/openrsgis/SDCluster</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"223 ","pages":"Pages 1-14"},"PeriodicalIF":10.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563151","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":"FengYun-3 meteorological satellites’ microwave radiation Imagers enhance land surface temperature measurements across the diurnal cycle","authors":"Yuyang Xiong ,&nbsp;Tianjie Zhao ,&nbsp;Haishen Lü ,&nbsp;Zhiqing Peng ,&nbsp;Jingyao Zheng ,&nbsp;Yu Bai ,&nbsp;Panpan Yao ,&nbsp;Peng Guo ,&nbsp;Peilin Song ,&nbsp;Zushuai Wei ,&nbsp;Ronghan Xu ,&nbsp;Shengli Wu ,&nbsp;Lixin Dong ,&nbsp;Lin Chen ,&nbsp;Na Xu ,&nbsp;Xiuqing Hu ,&nbsp;Peng Zhang ,&nbsp;Letu Husi ,&nbsp;Jiancheng Shi","doi":"10.1016/j.isprsjprs.2025.02.018","DOIUrl":"10.1016/j.isprsjprs.2025.02.018","url":null,"abstract":"<div><div>Land Surface Temperature (LST) is a vital meteorological variable for assessing hydrological, ecological, and climatological dynamics, as well as energy exchanges at the land–atmosphere interface. Accurate and frequent LST measurement is essential for meteorological satellites. However, existing retrieval algorithms often fail to capture the nuances of diurnal temperature variations. This study utilizes the exceptional diurnal sampling capabilities of the Microwave Radiation Imagers (MWRI) on China’s FengYun-3 (FY-3) satellites to improve LST measurements throughout the day. The objective is to develop a global algorithm that can distinguish between frozen and thawed states of near-surface landscape. This algorithm integrates multi-channel brightness temperature data and an array of microwave indices to enhance accuracy across diverse land cover types. Validation against in-situ measurements, alongside the comparative analysis with ERA5 and MODIS LST products demonstrate the algorithm’s high robustness. Results reveal a correlation coefficient exceeding 0.87 between FY-3 MWRI-derived LST and 5-cm soil temperature, with a root mean squared error (RMSE) near 4 K, except at 14:00 for FY-3D. The theoretical uncertainty, estimated using triple collocation analysis of the three LST datasets from FY-3 MWRI, ERA5 and MODIS, is less than 4 K for the majority of the globe. Additionally, the FY-3 MWRI exhibits reduced diurnal variation in LST as compared to MODIS LST, the peak temperatures recorded by FY-3 MWRI occur with a certain time lag relative to MODIS, and the diurnal temperature range is generally narrower, showcasing its adeptness in delineating diurnal temperature cycles when deployed across the FY-3B/C/D satellite constellation.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"222 ","pages":"Pages 204-224"},"PeriodicalIF":10.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548806","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":"Mitigation of tropospheric turbulent delays in InSAR time series by incorporating a stochastic process","authors":"Hailu Chen ,&nbsp;Yunzhong Shen ,&nbsp;Lei Zhang ,&nbsp;Hongyu Liang ,&nbsp;Tengfei Feng ,&nbsp;Xinyou Song","doi":"10.1016/j.isprsjprs.2025.02.028","DOIUrl":"10.1016/j.isprsjprs.2025.02.028","url":null,"abstract":"<div><div>Tropospheric delays present a significant challenge to accurately mapping the Earth’s surface movements using interferometric synthetic aperture radar (InSAR). These delays are typically divided into stratified and turbulent components. While efforts have been made to address the stratified component, effectively mitigating turbulence remains an ongoing challenge. In response, this study proposes a joint model that compasses both the deterministic components and stochastic elements to account for the phases raised by turbulent delays in full InSAR time series. In the joint model, the deformation phases are parameterized by time-domain polynomial, while the turbulent delays are treated as spatially correlated stochastic variables, defined by spatial variance–covariance functions. Least Squares Collocation (LSC) and Variance-Covariance Estimation (VCE) are employed to solve this joint model, enabling simultaneous estimation of modelled deformation and turbulent mixing from full InSAR time series. The rationale is rooted in the distinct temporal dependencies of deformation and turbulent delay. Its efficacy and versatility are demonstrated using simulated and Sentinel-1 data from Hong Kong International Airport (China) and the Southern Valley of California (USA). In simulations, the root mean square error (RMSE) of the differential delays decreased from 2.4 to 0.8 cm. In the Southern Valley, comparison with 70 GPS measurements showed a 73.7 % reduction in mean RMSE, from 1.9 to 0.5 cm. These results confirm the effectiveness of this approach in mitigating tropospheric turbulence delays in the time domain.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"222 ","pages":"Pages 186-203"},"PeriodicalIF":10.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548805","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":"TACMT: Text-aware cross-modal transformer for visual grounding on high-resolution SAR images","authors":"Tianyang Li ,&nbsp;Chao Wang ,&nbsp;Sirui Tian ,&nbsp;Bo Zhang ,&nbsp;Fan Wu ,&nbsp;Yixian Tang ,&nbsp;Hong Zhang","doi":"10.1016/j.isprsjprs.2025.02.022","DOIUrl":"10.1016/j.isprsjprs.2025.02.022","url":null,"abstract":"<div><div>This paper introduces a novel task of visual grounding for high-resolution synthetic aperture radar images (SARVG). SARVG aims to identify the referred object in images through natural language instructions. While object detection on SAR images has been extensively investigated, identifying objects based on natural language remains under-explored. Due to the unique satellite view and side-look geometry, substantial expertise is often required to interpret objects, making it challenging to generalize across different sensors. Therefore, we propose to construct a dataset and develop multimodal deep learning models for the SARVG task. Our contributions can be summarized as follows. Using power transmission tower detection as an example, we have built a new benchmark of SARVG based on images from different SAR sensors to fully promote SARVG research. Subsequently, a novel text-aware cross-modal Transformer (TACMT) is proposed which follows DETR’s architecture. We develop a cross-modal encoder to enhance the visual features associated with the textual descriptions. Next, a text-aware query selection module is devised to select relevant context features as the decoder query. To retrieve the object from various scenes, we further design a cross-scale fusion module to fuse features from different levels for accurate target localization. Finally, extensive experiments on our dataset and widely used public datasets have demonstrated the effectiveness of our proposed model. This work provides valuable insights for SAR image interpretation. The code and dataset are available at <span><span>https://github.com/CAESAR-Radi/TACMT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"222 ","pages":"Pages 152-166"},"PeriodicalIF":10.6,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526819","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}