ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Mapping dynamics of large-scale high-precision pond datasets using a semi-automated method based on deep learning","authors":"Zhenbang Hao ,&nbsp;Lili Lin ,&nbsp;Christopher J. Post ,&nbsp;Elena A. Mikhailova ,&nbsp;Jeffery Allen","doi":"10.1016/j.isprsjprs.2025.06.018","DOIUrl":"10.1016/j.isprsjprs.2025.06.018","url":null,"abstract":"<div><div>With their large numbers and widespread distribution, ponds are crucial in stormwater interception, biodiversity, and freshwater resource conservation. However, due to their small size and shallow depth, ponds are highly susceptible to anthropogenic activities and climate variability, making it necessary to map their numbers, distribution, and change dynamics. Relying only on deep learning (DL) techniques is insufficient to create a pond identification dataset that does not contain errors. This study is the first of its kind that proposes a workflow to identify small ponds (&lt;5 ha) with minimal errors from the National Agricultural Imagery Program (NAIP) high-resolution aerial imagery using the combination of DL and a manual cross-correction approach. Ponds in South Carolina, United States, were detected and delineated for 2017 and 2019 using U-Net models. Next, the detection results from both years were used as reference data for cross-correction, removing false detections and adding omissions to obtain the refined high-precision pond datasets. The pond datasets were compared to the existing public datasets (JRC and NWI) to evaluate the performance of the proposed method. Finally, changes in ponds between two years and the predominant land cover around each pond in 2019 were analyzed in our study. The results showed that the refined high-precision pond dataset containing 70,449 ponds in 2017 and 71,858 ponds in 2019, with an average size of 0.5 ha, fills an important gap in existing pond data. The existing public datasets (JRC and NWI) do not identify 61.72% and 41.03% of the new high-precision pond dataset developed as part of our study in 2019. Based on land cover data, the largest number of ponds were located in forested areas (23,188 ponds, 0.76 ponds/km²), followed by wetlands (15,782 ponds, 0.76 ponds/km²). In contrast, barren land and hay/pasture had the highest pond density, reaching 2.67 ponds/km² and 1.93 ponds/km². A total of 2,979 ponds experienced changes between 2017 and 2019, and 69,664 ponds remained unchanged. The types of pond changes can be categorized as new pond construction, water level changes, and pond disappearance. Our study significantly advances a workflow and method for pond detection that leverages deep learning over large areas in diverse ecological regions and can provide high-precision pond datasets with minimal errors for pond evaluation and management.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"227 ","pages":"Pages 438-458"},"PeriodicalIF":10.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490097","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":"Optical properties of a toxin-producing dinoflagellate and its detection from Sentinel-2 MSI in nearshore waters","authors":"Conor McGlinchey ,&nbsp;Jesus Torres Palenzuela ,&nbsp;Luis Gonzalez-Vilas ,&nbsp;Mortimer Werther ,&nbsp;Dalin Jiang ,&nbsp;Andrew Tyler ,&nbsp;Yolanda Pazos ,&nbsp;Evangelos Spyrakos","doi":"10.1016/j.isprsjprs.2025.06.017","DOIUrl":"10.1016/j.isprsjprs.2025.06.017","url":null,"abstract":"<div><div>Harmful algal blooms (HABs) caused by the dinoflagellate <em>Alexandrium minutum</em> can pose risks to human and ecosystem health. HABs of different species can coexist in coastal waters and accumulate near the shoreline, challenging their detection through Earth observation (EO). In this study, we use <em>in situ</em> geo-bio-optical and taxonomical data from the <em>Rías Baixas</em> (NW Spain) to develop a new method for identifying high-concentration blooms of <em>A. minutum</em> and its application to Sentinel-2 Multispectral Instrument (S2 MSI). Our approach named <em>A. minutum index</em> (AMI) was developed to capture the low absorption and high backscattering properties of <em>A. minutum</em> cells between 560 and 570 nm. We tested and validated the performance of three atmospheric correction algorithms (AC) (C2RCC, POLYMER and ACOLITE) using matchups between <em>in situ</em> and satellite-derived R_rs. Results show that C2RCC had the lowest error across most wavelengths. Applying AMI to S2 MSI indicates that our approach can accurately identify high-concentration blooms of <em>A. minutum</em> (F1 score: 70 %, Kappa: 68.3 %, balanced accuracy: 87.7 %, MCC: 68.3 %) and discriminate blooms of <em>A. minutum</em> from other phytoplankton species. We compared AMI with three existing indices for detecting HABs in coastal waters and found that our approach achieved a better performance, with the NDTI, RGCI and NDCI yielding F1 scores of 21.28, 21.74, and 0.0 % and MCC values of 15.0, 15.0 and 0.0 %, respectively. We also investigated the spatial resolution of S2 MSI to Sentinel-3 Ocean and Land Colour Instrument (S3 OLCI) for mapping fine-scale variations in <em>A. minutum</em> blooms. We found that the higher spatial resolution data from S2 MSI were highly useful for detecting small-scale variations in <em>A. minutum</em> in nearshore waters, enabling their detection in the mid-inner part of the <em>Rías</em>, where aquaculture activities are more prominent. This study also showcases the significance of accurate AC in near-shore waters, where high-concentration blooms can be more prevalent. Our findings show that greater errors in AC are observed in near-shore pixels, where the socio-economic and environmental impact of HABs are typically more severe.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"227 ","pages":"Pages 415-437"},"PeriodicalIF":10.6,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480395","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":"Pointwise deep learning for leaf-wood segmentation of tropical tree point clouds from terrestrial laser scanning","authors":"Wouter A.J. Van den Broeck,&nbsp;Louise Terryn,&nbsp;Shilin Chen,&nbsp;Wout Cherlet,&nbsp;Zane T. Cooper,&nbsp;Kim Calders","doi":"10.1016/j.isprsjprs.2025.06.023","DOIUrl":"10.1016/j.isprsjprs.2025.06.023","url":null,"abstract":"<div><div>Terrestrial laser scanning (TLS) is increasingly used in forest monitoring, providing detailed 3D measurements of vegetation structure. However, the semantic understanding of tropical tree point clouds, particularly the segmentation of leaf and wood components, remains a challenge. Deep learning (DL) on point clouds has been gaining traction as a valuable tool for automated leaf-wood segmentation, but its widespread adoption is impeded by data availability, a lack of open-source trained models, and knowledge on its influence on subsequent woody volume reconstruction. To address these issues, this paper makes three key contributions. First, it introduces a new dataset consisting of 148 tropical tree TLS point clouds from north-eastern Australia with manual leaf-wood annotations. Second, it uses this dataset to compare several state-of-the-art point-wise DL networks and benchmark these against traditional approaches, using a common training and inference pipeline to allow for a fair model comparison. We conduct an ablation study to examine the effects of various hyperparameters and modelling choices, focusing solely on point coordinates as input to develop a model adaptable to different forest types, platforms, and point cloud qualities. Third, we assess the impact of point-wise segmentation quality on tropical tree volume estimation using quantitative structure model (QSM) reconstruction on the extracted woody component. Results show that our newly trained DL models significantly outperform traditional benchmarks for leaf-wood segmentation of tropical tree point clouds from TLS, with PointTransformer achieving the highest performance (mIoU = 92.2 %). Quantitative and qualitative analyses reveal that DL methods excel in distinguishing woody points, crucial for woody volume estimation via QSMs, but may suffer from connectivity issues due to lack of physical constraints. Volumes of trees segmented using PointTransformer closely match those of manually segmented trees (MAE = 7.1 %), highlighting its suitability for automated woody volume estimation. Although this study demonstrates the effectiveness of state-of-the-art neural architectures for tropical tree point cloud processing, advocating for their integration into forest structure analysis pipelines, future work should focus on enhancing quantity, quality and variety of training data, to increase model robustness and generalisability. We make the dataset, code and trained DL models publicly available.<span><span>¹</span></span></div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"227 ","pages":"Pages 366-382"},"PeriodicalIF":10.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472286","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 mining scene understanding framework with limited labeled samples jointly driven by object-level spatial relationships and multi-task network","authors":"Dehui Dong ,&nbsp;Dongping Ming ,&nbsp;Lu Xu ,&nbsp;Yue Zhang","doi":"10.1016/j.isprsjprs.2025.06.024","DOIUrl":"10.1016/j.isprsjprs.2025.06.024","url":null,"abstract":"<div><div>Accurately delineating mining areas is crucial for monitoring illegal mining activities. Currently, in large-scale and limited labeled sample scenes, mining areas are easily interfered by targets such as clouds, farmland, and roads with similar spectral characteristics, leading to serious misclassification issues. In response to this problem, this paper proposes a mining area understanding framework based on object-level spatial relation constraints, simulating the way that humans interpret mining scenes in remote sensing images. The framework first constructs a Multi-task Network for joint Panoptic Segmentation and Relation Prediction (PSRP-MNet), aiming to achieve high-precision segmentation of mining area scenes and acquisition of explicit object-level spatial relations. The network contained an explicit spatial relation matching module, a lightweight segmentation head, and multi-scale deformable attention to achieve a comprehensive fusion of deep-level features between different tasks and thus realize a rational utilization of multi-scale semantic information. The spatial relation matching module explicitly models and matches the spatial positional relations between targets existing in mining areas, helping the model understand mining areas from the perspective of object-level targets. The lightweight design of the segmentation head maintains high performance while reducing model complexity and parameters. Subsequently, the spatial relations were matched with the prior object-level spatial relation knowledge criteria constructed in this paper, determining the integrated functional structures in the scene to further constrain the segmentation results. The guidance of spatial relations helps PSRP-MNet correct its predictions when errors occur, leading to excellent performance in limited labeled sample tasks. Two sufficiently large scenes were selected as study areas, and approximately 1000 image samples were used for training. Multiple sets of comparative experiments were conducted to validate the framework’s effectiveness and cross-regional generalization ability in limited labeled sample tasks. It was observed that the introduction of spatial relations and the association between different tasks reduced the error accumulation of PSRP-MNet. This research is expected to provide a reference for the regular monitoring of mineral resources.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"227 ","pages":"Pages 383-396"},"PeriodicalIF":10.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472287","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":"STANet-TLA: leveraging deep learning and prior knowledge for large-scale soybean breeding plot segmentation and high-yielding variety screening from UAV time-series data","authors":"Shaochen Li ,&nbsp;Yinmeng Song ,&nbsp;Ke Wang ,&nbsp;Yiqiang Liu ,&nbsp;Junhong Xian ,&nbsp;Hongshan Wu ,&nbsp;Xintong Zhang ,&nbsp;Yanjun Su ,&nbsp;Jin Wu ,&nbsp;Qinghua Guo ,&nbsp;Shan Xu ,&nbsp;Dong Jiang ,&nbsp;Jiao Wang ,&nbsp;Jinming Zhao ,&nbsp;Xianzhong Feng ,&nbsp;Lijuan Qiu ,&nbsp;Yanfeng Ding ,&nbsp;Shichao Jin","doi":"10.1016/j.isprsjprs.2025.06.012","DOIUrl":"10.1016/j.isprsjprs.2025.06.012","url":null,"abstract":"<div><div>High-yielding varieties screening is essential for food security, which requires the monitoring of canopy growth, the extraction of dynamic traits, and the estimation of yield at the variety level. Unmanned Aerial Vehicle (UAV) provides a valuable source of high-resolution spatio-temporal data, which can accelerate plot-level phenotyping and variety screening. However, the automatic extraction of breeding plot boundaries from UAV images is challenging due to complex backgrounds, dynamic canopies, and varying row and plot intervals. In this study, we introduce a novel method called <em>STANet-TLA</em> for breeding plot extraction to screen high-yielding varieties. <em>STANet-TLA</em> leverages a self-designed spatio-temporal feature alignment network (<em>STANet</em>) for semantic segmentation and a prior knowledge-constrained traction line aggregation method (<em>TLA</em>) for instance segmentation. To evaluate our model, we constructed a comprehensive dataset named <em>SoyUAV</em>, which includes 21,319 images of more than 977 genotypes at almost all growth stages. The results demonstrated that: (1) <em>STANet</em> achieved an intersection over union (<em>IoU</em>) of 85.43 % and an F1-score (<em>F1</em>) of 91.89% for canopy semantic segmentation, outperforming eight state-of-the-art deep learning networks with average improvements of 5.80 % in <em>IoU</em> and 4.65 % in <em>F1</em>. Based on the semantic segmentation results, <em>TLA</em> achieved an <em>IoU</em> of 93.31 % and an <em>F1</em> of 95.13 % for plot instance segmentation; (2) <em>STANet</em> demonstrated effective transferability across different years, locations, and data types, achieving high <em>IoU</em> scores of 88.22%, 89.53%, and 79.16%, respectively. <em>STANet-TLA</em> was suitable for plot instance segmentation with different planting designs; (3) The accuracy of high-yielding varieties screening was 60 % using Random Forest with static phenotypes in the plots obtained by <em>STANet-TLA</em> segmentation. This accuracy was improved to 70.59 % and 75 % when incorporating time-series and dynamic-fitting phenotypes, respectively. Our datasets and models are publicly available, which we believe will significantly facilitate advanced UAV-based plant phenotyping and widespread large-scale breeding applications.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"227 ","pages":"Pages 397-414"},"PeriodicalIF":10.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480394","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":"Total solution for simultaneous pose and correspondence estimation of drone images in urban environments","authors":"Shuang Li,&nbsp;Jie Shan","doi":"10.1016/j.isprsjprs.2025.06.027","DOIUrl":"10.1016/j.isprsjprs.2025.06.027","url":null,"abstract":"<div><div>Vision-based pose estimation for drone images in urban environments is particularly challenging when reliable GNSS and IMU signals are unavailable and the search space spans large areas. Traditional methods depend on known correspondences of well-defined landmark objects, which are not always feasible in complex urban environments. To address this problem, we propose a total solution that simultaneously estimates the image pose and its correspondences to a semantic map database. A cascaded network, named dual-head SegFormer, is developed to generate multi-class semantic segmentation maps and high-quality road centerlines from images. A city-wide coarse-to-fine image localization strategy aligns the image segmentation map with the database map using class-label consistency and graph representation indices, yielding initial poses for further optimization. The final pose is determined by minimizing a novel objective function that evaluates the differences between the image and database across three key aspects: semantic maps, road attributes, and tie point reprojection errors. Evaluated on three urban drone image datasets, our method achieves position and rotation errors below 2.03 m and 2 <span><math><mrow><msup><mrow><mspace></mspace></mrow><mo>°</mo></msup></mrow></math></span> relative to the bundle adjustment results. By incorporating semantic features and an improved objective function, our method achieves notable enhancements in robustness and accuracy compared to prior approach that relied exclusively on road attributes. This work provides a dependable alternative for vision-based navigation, further reducing dependence on GNSS data or precise initial pose information.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"227 ","pages":"Pages 349-365"},"PeriodicalIF":10.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472284","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":"Teaching in adverse scenes: a statistically feedback-driven threshold and mask adjustment teacher-student framework for object detection in UAV images under adverse scenes","authors":"Hongyu Chen ,&nbsp;Jiping Liu ,&nbsp;Yong Wang ,&nbsp;Jun Zhu ,&nbsp;Dejun Feng ,&nbsp;Yakun Xie","doi":"10.1016/j.isprsjprs.2025.06.009","DOIUrl":"10.1016/j.isprsjprs.2025.06.009","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) have become a key platform for aerial object detection, but their performance in real-world scenarios is often severely impacted by adverse environmental conditions, such as fog and haze. Achieving robust UAV object detection under these challenging conditions is crucial for enhancing the all-weather situational awareness capabilities of UAVs. This is especially critical in key application scenarios, such as rapid disaster response and information interpretation, which demand reliable visual perception around the clock. Unsupervised Domain Adaptation (UDA) has shown promise in effectively alleviating the performance degradation caused by domain gaps between source and target domains, and it can potentially be generalized to UAV object detection in adverse scenes. However, existing UDA studies are based on natural images or clear UAV imagery, and research focused on UAV imagery in adverse conditions is still in its infancy. Moreover, due to the unique perspective of UAVs and the interference from adverse conditions, these methods often fail to accurately align features and are influenced by limited or noisy pseudo-labels. To address this, we propose the first benchmark for UAV object detection in adverse scenes, the Statistical Feedback-Driven Threshold and Mask Adjustment Teacher-Student Framework (SF-TMAT). Specifically, SF-TMAT introduces a design called Dynamic Step Feedback Mask Adjustment Autoencoder (DSFMA), which dynamically adjusts the mask ratio and reconstructs feature maps by integrating training progress and loss feedback. This approach dynamically adjusts the learning focus at different training stages to meet the model’s needs for learning features at varying levels of granularity. Additionally, we propose a unique Variance Feedback Smoothing Threshold (VFST) strategy, which statistically computes the mean confidence of each class and dynamically adjusts the selection threshold by incorporating a variance penalty term. This strategy improves the quality of pseudo-labels and uncovers potentially valid labels, thus mitigating domain bias. Extensive experiments demonstrate the superiority and generalization capability of the proposed SF-TMAT in UAV object detection under adverse scene conditions. The Code is released at <span><span>https://github.com/ChenHuyoo</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"227 ","pages":"Pages 332-348"},"PeriodicalIF":10.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365690","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":"Bundle adjustment for multi-source Mars orbiter imagery with generalized control constraints","authors":"Qionghua You ,&nbsp;Zhen Ye ,&nbsp;Chen Chen ,&nbsp;Huan Xie ,&nbsp;Yanmin Jin ,&nbsp;Rong Huang ,&nbsp;Changyou Xu ,&nbsp;Yusheng Xu ,&nbsp;Xiaohua Tong","doi":"10.1016/j.isprsjprs.2025.05.030","DOIUrl":"10.1016/j.isprsjprs.2025.05.030","url":null,"abstract":"<div><div>Integration of multi-source Mars orbiter imagery leverages diverse datasets to enhance surface details and mapping accuracy. Achieving spatial consistency is crucial, yet challenges arise from significant resolution differences and the lack of ground control points. This paper proposes a bundle adjustment method with generalized control constraints tailored for multi-source orbiter imagery. A bias compensation model using affine and cubic spline functions is employed to effectively correct linear and nonlinear distortions. Generalized control constraints integrate planar and elevation aspects through planar control points, virtual control points, tie points, and terrain information. Planar weak control constraints are established by crater features extraction utilizing the Segment Anything Model 2, ellipse fitting, and accurate point set matching, with further enhancement from slope information derived from reference terrain. Elevation control constraints are established using the reference Digital Elevation Models and slope data. An adaptive weighting strategy is developed to combine these aspects optimally, facilitating precise registration of multi-source orbiter imagery to a unified Mars reference frame. Experiments with Mars Context Camera (CTX) and High-Resolution Imaging Science Experiment data validate the performance of the proposed method across various Martian terrains, demonstrating significant reductions in reprojection residuals from approximately 10 pixels to under 0.8 pixels. Additionally, the planar positioning accuracy improved from a maximum of over 300 m to within 18 m, with elevation accuracy within 3 m. Comparisons with existing adjustment methods and fully controlled CTX mosaic products confirm the effectiveness and reliability of the proposed method.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"227 ","pages":"Pages 316-331"},"PeriodicalIF":10.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365767","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 in-seasonal phenology monitoring approach for wheat breeding accessions with time-series RGB imagery by using a combination KNN-CNN-RF model","authors":"Meng Zhou ,&nbsp;Jie Zhu ,&nbsp;Hongxu Ai ,&nbsp;Yangming Zhang ,&nbsp;Timothy A. Warner ,&nbsp;Hengbiao Zheng ,&nbsp;Chongya Jiang ,&nbsp;Tao Cheng ,&nbsp;Yongchao Tian ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Xia Yao","doi":"10.1016/j.isprsjprs.2025.06.015","DOIUrl":"10.1016/j.isprsjprs.2025.06.015","url":null,"abstract":"<div><div>Accurate and near real-time monitoring of wheat phenology is crucial for both cultivation and breeding. It plays a pivotal role in guiding planting management, optimizing variety selection, enhancing yield and quality, and providing a scientific foundation for wheat production. This study aims to develop a high-throughput approach for identifying the real-time phenological stages and estimating the initiation date of key stages for abundant breeding accessions using UAV-derived RGB imagery. A two-year field experiment was conducted across diverse wheat accessions worldwide, including the Watkins landraces and modern varieties at different ecological locations in three provinces of Guangdong, Jiangsu, and Hebei. The minimalist neural network model VanillaNet was employed to classify the five phenological stages: tillering stage (TS), jointing and booting stage (JBS), heading stage (HS), anthesis and filling stage (AFS), and maturity stage (MS), based on image features. Meanwhile, the K-nearest neighbors algorithm categorized the phenological stage into three class—TS, JBS and heading to maturity stage (HMS)—using UAV-derived three-dimensional height data. To improve the classification accuracy, the weight coefficient was introduced to integrate the prediction probability of two classifiers. Finally, a random forest (RF) model was developed to estimate the initiation dates of key phenological stage based on the integrated time-series prediction probabilities. The results showed that the integrated classifier exhibited accuracies of 0.96, 0.88, 0.66, 0.87, and 0.96 in the five stages, respectively. Compared to the classification results obtained solely using neural network models, the increase in the F1-scores for the first three phenology stages was 7.41 %, 5.81 %, and 13.16 %, respectively. After stage classification, the RF model demonstrated robust performance in predicting the initiation dates of jointing, heading, anthesis, and maturity stages, with a coefficient of determination of 0.61–0.91 and a root mean square error of 1.83–4.09 days. Furthermore, the accuracy of phenological monitoring was analyzed under different data collection frequencies, revealing that the optimal interval for data collection was within 5–13 days. The proposed methodology realized synchronously the classification and quantification of phenological stages, thereby serving as a high-throughput screening technology of fine variety in smart wheat breeding.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"227 ","pages":"Pages 297-315"},"PeriodicalIF":10.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364891","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":"HIUFE: Hybrid intelligence-based unauthorized farmland excavation scene cognition","authors":"Shunxi Yin ,&nbsp;Wanzeng Liu ,&nbsp;Jun Chen ,&nbsp;Jiaxin Ren ,&nbsp;Yuan Tao ,&nbsp;Yilin Wang ,&nbsp;Jiadong Zhang","doi":"10.1016/j.isprsjprs.2025.06.016","DOIUrl":"10.1016/j.isprsjprs.2025.06.016","url":null,"abstract":"<div><div>Unauthorized farmland excavation refers to activities such as digging, mining, and related resource development within farmland boundaries, conducted without legal authorization or in violation of relevant regulations. These activities directly contribute to the destruction and functional degradation of farmland, posing significant threats to national food security and social stability. Existing farmland monitoring methods utilizing video recognition exhibit limitations, including high false positive rates, and low levels of automation. To address these challenges, this paper proposes a hybrid intelligence-based cognitive approach to video scene analysis for unauthorized farmland excavation activities. At the data level, a video dataset capturing the behavioral interactions of construction machinery in unauthorized farmland excavation scenes is constructed, incorporating temporal and spatial dimensions to comprehensively depict interaction features among the machinery. At the algorithmic level, considering the frequent motion of objects and the high timeliness requirements in video scenes, expert knowledge is integrated to enhance YOLOv8, specifically proposing a hybrid intelligence-based object behavior recognition model that accurately captures subtle feature differences in the same object under different behaviors. During the inference phase, a knowledge graph and reasoning mechanism are constructed to deeply integrate dynamic video information with domain knowledge, overcoming the challenge of incomplete recognition of object interaction and achieving precise identification of unauthorized farmland excavation activities. Comparative experiments thoroughly validate the model’s superiority in identifying subtle feature differences. Compared to the latest single-stage object detection model, YOLO11, the proposed object behavior recognition model improves the F1 score by 3.26% (from 85.17% to 88.43%). Ablation experiments further confirm the effectiveness of incorporating expert knowledge. For example, the CSPELAN module, enhanced with multi-scale feature knowledge, increases the F1 score by 3.75% (from 84.29% to 88.04%). The research outcomes not only provide efficient and reliable technical support for farmland protection, but also contribute valuable practical experience and methodological references to theoretical innovation and technological development in the field of geospatial intelligence analysis.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"227 ","pages":"Pages 276-296"},"PeriodicalIF":10.6,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330458","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}