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Unsupervised Domain Transfer for Object Classification in 3D Point Clouds via Hierarchical Prompt Learning

IEEE Signal Processing Letters 2025年 32卷 1750-1754页

作者： Luo, Huan Fu, Kaiwei Fang, Lina Fuzhou University College of Computer and Data Science China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing China Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing Fuzhou350003 China Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Haixi Institute Fujian Quanzhou362216 China

Traditional object classification in 3D point cloud scenes relies heavily on large-scale labeled training data, which is both time-consuming and labor-intensive to obtain. Unsupervised Domain Transfer (UDT) mitigates this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. However, existing UDT-based methods often require complex neural architectures and substantial computational resources. This letter proposes a novel UDT framework that integrates hierarchical prompt learning with a 3D foundational model. The proposed method consists of a modality alignment stage and an unsupervised transfer stage. In the modality alignment stage, cross-modal hierarchical prompts are employed to align the Visual-Language (V-L) modality in the 3D foundational model through a V-L coupling module. In the unsupervised transfer stage, cross-domain hierarchical prompts and a Target-Source (T-S) coupling module facilitate the alignment of multi-scale contextual information across domains, ensuring efficient and accurate knowledge transfer. Extensive experiments conducted on multiple datasets collected from various laser scanners demonstrate the effectiveness of our proposed approach. © 1994-2012 IEEE.

关键词： Visual languages

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Can satellite observations detect global ocean heat content change with high resolution by deep learning?

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 52-68页

作者： Su, Hua Teng, Jianchen Zhang, Feiyan Wang, An Huang, Zhanchao Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education The Academy of Digital China Fuzhou University Fuzhou350108 China National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China Department of Geography Ghent University Ghent9000 Belgium

The development of in situ observations has significantly improved ocean heat content (OHC) estimation. However, high-resolution OHC data remain limited, hindering detailed studies on mesoscale oceanic warming variability. This study used a deep learning method-Densely Deep Neural Network (DDNN) to reconstruct a high-resolution (0.25° × 0.25°) global OHC dataset for the upper 2000m ocean from 1993 to 2023, named the Ocean Projection and Extension Neural Network 0.25° (OPEN0.25°) product. This deep ocean remote sensing approach integrates multi-source remote sensing data, including Sea Surface Temperature (SST), Absolute Dynamic Topography (ADT), and Sea Surface Wind (SSW), alongside spatiotemporal coordinates and in situ observations. The DDNN model was trained using Argo-based gridded data and EN4-profile data, initially undergoing pre-training to assimilate large-scale oceanic features, followed by fine-tuning to enhance its accuracy in capturing mesoscale thermal structures. Our results demonstrate that the DDNN model achieves high accuracy across various depths. Particularly, OPEN0.25° can effectively capture detailed thermal variations in regions with complex dynamics, as well as the heat transfer processes within the ocean interior, outperforming traditional methods in resolution. The research highlights that, influenced by strong El Niño-Southern Oscillation (ENSO) events, OHC in the upper 700m of the Pacific Ocean potentially far exceeding expectations over the past decade. Through this study, OPEN0.25° has demonstrated its critical role in detecting and monitoring long-term changes in global OHC at high resolution. © 2025 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

关键词： Submarine geophysics

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Detection and interpretation of the time-varying seasonal signals in China with multi-geodetic measurements

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Geodesy and Geodynamics 2025年第1期16卷 42-54页

作者： Liansheng Deng Yugang Xiao Qusen Chen Wei Peng Zhao Li Hua Chen Zhiwen Wu School of Electrical and Electronic Information Engineering Hubei Polytechnic UniversityHuangshi 435003China Hubei Luojia Laboratory GNSS Research CenterWuhan UniversityWuhan 430079China Changjiang Spatial Information Technology Engineering Co. Ltd.Wuhan 430010China Key Laboratory of Spatial Data Mining&Information Sharing of the Ministry of Education Fuzhou UniversityFuzhou 350108China School of Geodesy and Geomatics Wuhan UniversityWuhan 430079China The First Surveying and Mapping Institute of Hunan Province Changsha 410114China

The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical *** this study,we apply the singular spectrum analysis(SSA)method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic *** include 23-year observations from the Crustal Movement Observation Network of China(CMONOC),displacements inferred from the Gravity Recovery and Climate Experiment(GRACE),and loadings derived from Geophysical models(GM).The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations,and the SSA method outperforms the traditional least squares fitting(LSF)method in extracting and interpreting the time-varying seasonal signals from the original time *** decrease in the root mean square(RMS)correlates well with the annual cycle variance estimated by the SSA method,and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF *** SSA analysis,the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten ***,both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations,and the geophysical effects can account for 71%of the GNSS annual amplitudes,and the average RMS reduction is 15%.The SSA method has proved to be useful for investigating the GNSS timevarying seasonal *** could be applicable as an auxiliary tool in the improvement of nonlinear variations investigations.

关键词： GNSS coordinate time series Singularspectrumanalysis Time-varying seasonal signals Loading effects GRACE

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Geomorphological evolution of small watershed on the Chinese Loess Plateau based on slope shape spectra

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Earth Science Informatics 2025年第2期18卷 1-23页

作者： Jiang, Hongtao Chen, Nan Wang, Chen Li, Sijia Ou, Mengyao Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou University Fuzhou 350116 China The Academy of Digital China (Fujian) Fuzhou University Fuzhou 350116 China

The Chinese Loess Plateau, the world's largest loess deposit, is characterized by distinctive landforms and severe soil erosion. Limited long-term observational data has hindered a comprehensive understanding of watershed evolution in this region. This study uses laboratory-based artificial rainfall simulations to investigate the long-term geomorphological evolution of watersheds on the Chinese Loess Plateau. Digital elevation models and terrain analysis techniques were employed to analyze geomorphological processes. Through correlation analysis and Sheffield entropy calculations, an optimal combination of terrain factors—including slope, aspect, plan curvature, profile curvature, and elevation—was identified to accurately describe slope characteristics. Multi-resolution image segmentation, Moran's I, and weighted variance were used to optimize segmentation parameters, achieving precise slope unit segmentation. Based on these parameters, we classified slope shapes, constructed slope shape spectra, and analyzed their changes over time. Results indicate that as the watershed evolves, the entropy of slope spectra increases while skewness and kurtosis decrease, indicating greater geomorphological complexity and diversity. The watershed's evolution progresses through three stages: slope shape development, slope shape growth, and slope shape maturity, each marked by distinct geomorphological features. By analyzing landscape indices at both class and landscape levels, we observed significant variation during the early development stage, followed by stabilization during the maturation phase. These findings demonstrate that slope shape spectra effectively capture the dynamic evolution of slopes in the Chinese Loess Plateau, offering new insights for understanding its geomorphological evolution and providing a novel method for quantitative geomorphology. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Evolution Loess landform Segmentation Simulated watershed Slope shape spectrum

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Unsupervised part extraction of substation equipment based on joint multi-level voxels’ features of point clouds 5

Unsupervised part extraction of substation equipment based o...

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5th International Conference on Signal Processing and Computer Science, SPCS 2024

作者： Zhang, Meng Lin, Ding Ding, Jing Fang, Tao Key Lab of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou University Fujian Fuzhou350108 China State Grid Hangzhou Power Supply Company State Grid ZHEJIANG Electric Power co. LTD Zhejiang Hangzhou310000 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Aiming at the segmentation and extraction of the main part of substation equipment, we use Fast Point Feature Histograms (FPFH) and Locally Convex Connected Patches (LCCP) to obtain voxels’ integrated geometric features, then aggregate these features and their K nearest neighbors’ on voxels to build multi-level voxels’ features by bottom-up hierarchy, and achieve pre-segmentation of shapes with the flow-constrained super-voxel clustering algorithm;After the pre-segmentation, we conduct shape analysis to extract semantically meaningful instances of equipment components, achieving part-level point cloud data instance extraction of artificial equipment geometric features. Without training data or manual annotations, the work presented is simple and easy to implement. It can merge patches across surface-singularities. It needs a few parameters, can achieve automatic 3D instance extraction from point clouds for different scenes with the same or similar parameters. © 2025 SPIE.

关键词： Superpixels

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Enhancing Fish Counting in Sonar Images With Multi-Task Learning and Local-Global Feature Interaction

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IEEE Sensors Journal 2025年

作者： Wang, Yuhang Wu, Qunyong Yang, Shiyu Li, Mengmeng Wang, keyue Chen, Xuanyu Fuzhou350128 China Fuzhou University Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350128 China

Accurate fish counting is crucial for environmental monitoring and management, with sonar imaging providing a non-intrusive way to gather data in previously inaccessible underwater environments. However, the strong visual similarity between fish and noise in sonar images presents significant challenges in achieving high counting accuracy. Existing methods rely on attention maps to emphasize fish regions but do not fully capture the discriminative features between fish and noise, limiting counting accuracy. To address this, we propose the local-global multi-task transformer (LGMFormer) to enhance fish counting in sonar images. The model employs an encoder-decoder architecture, with density map regression as the primary task and multi-class semantic segmentation as an auxiliary task. By predicting multi-class segmentation maps, the shared network layers fully learn discriminative features between fish and noise. We also develop a segmentation-enhanced density head to further strengthen the connection between tasks. Within LGMFormer, the local-global feature interaction module is designed to fuse local spatial detail features with global correlation features for more precise fish counting. Additionally, a high-level feature guidance module is developed to retain more detail during the feature fusion process between the encoder and decoder. We also develop an automatic image segmentation method based on Otsu's thresholding to create multi-class segmentation labels for sonar images. Extensive experiments on a public sonar fish counting dataset demonstrate that LGMFormer outperforms state-of-the-art methods in counting accuracy, reducing the mean absolute error of count prediction by 17.6% and the false recognition rate by 42.9%. The source code will be available at https://***/camerayuhang/LGMFormer. © 2001-2012 IEEE.

关键词： Semantic Segmentation

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Building Extraction From Multi-View RGB-H Images With General Instance Segmentation Networks and a Grouping Optimization Algorithm

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IEEE Geoscience and Remote Sensing Letters 2025年 22卷

作者： Yu, Dawen Cheng, Hao Fuzhou University Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education The Academy of Digital China Fuzhou350108 China Wuhan University School of Remote Sensing and Information Engineering Wuhan430079 China

Bird’s-eye-view (BEV) building mapping from remote sensing images is a studying hotspot with broad applications. In recent years, deep learning (DL) has significantly advanced the development of automatic building extraction methods. However, most existing research focuses on segmenting buildings from a single perspective, such as orthophotos, overlooking the rich information of multi-view images. In surveying and mapping, individual building instances need to be separated even when they are adjacent or touching. Since orthophotos cannot capture building walls due to self-occlusion, distinguishing between closely connected buildings in densely built areas becomes challenging. To tackle this issue, we propose a multi-view collaborative pipeline for instance-level building segmentation. This pipeline utilizes a grouping optimization algorithm to merge segmentation results from multiple views, which are predicted by general instance segmentation networks and projected onto the BEV, to produce the final building instance polygons. Both qualitative and quantitative results show that the proposed multi-view collaborative pipeline significantly outperforms the popular orthophoto-based pipeline on the InstanceBuilding dataset. © 2004-2012 IEEE.

关键词： Convolutional neural networks

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Mshf-Yolo:Cotton Growth Detection Algorithm Integrated Multi-Semantic and High-Frequency Features

SSRN

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SSRN 2025年

作者： Luo, Jiahuan Wu, Qunyong Wang, Yuhang Zhou, Zhan Zhuo, Zhihao Guo, Henyu Fuzhou University Fujian Fuzhou350128 China Key Laboratory of Spatial Data Mining and Information Sharing the Ministry of Education Fuzhou University Fujian Fuzhou350128 China

Cotton growth monitoring is a crucial method for achieving precise management of cotton. Currently, deeplearning-based object detection models have been widely applied in cotton detection. However, existingmodels often struggle to achieve good detection results when facing challenges such as complex environments,occlusion and low contrast. To address these issues, this paper proposes a precise MSHF-YOLO based on theYOLOv8 algorithm. In this network, we introduce Multi-Semantic spatial and Channel Attention (MSCA)into the backbone, and the upsampling and downsampling modules in the network’s neck are replacedwith the DySample and Adaptive Wavelet Down (AWD) modules to reduce the loss of high-frequencyfeatures. Finally, to further emphasize high-frequency features, we add a high-frequency boost module tothe detection head. The test results show that the algorithm achieves mAP@0.5 and mAP@0.75 of 86.0% and68.2%, respectively, which represents an improvement of 5.5% and 3.5% compared to the original ***, the model size is reduced by 12.5%. Therefore, this algorithm shows great potential for cottongrowth monitoring applications. © 2025, The Authors. All rights reserved.

关键词： Wavelet transforms

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Deriving the slope-mean shielded astronomical solar radiation spectrum and slope-mean possible sunshine duration spectrum over the Loess Plateau

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Journal of Mountain Science 2020年第1期17卷 133-146页

作者： CHEN Nan Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou University Spatial Information Research Center of Fujian Province Fuzhou University

Solar radiation is often shielded by terrain relief, especially in mountainous areas, before reaching the surface of the Earth. The objective of this paper is to study the spatial structures of the shielded astronomical solar radiation(SASR) and the possible sunshine duration(PSD) over the Loess Plateau. To this end, we chose six test areas representing different landforms over the Loess Plateau and the software package of Matlab was used as the main computing platform. In each test area, 5-m-resolution digital elevation model established from 1:10,000 scale topographic maps was used to compute the corresponding slope, SASR and PSD. Then, we defined the concepts of the slope-mean SASR spectrum and the slope-mean PSD spectrum, and proposed a method to extract them from the computed slope, SASR and PSD over rectangular analysis windows. Using this method, we found both spectrums in a year or in a season for each of the four seasons in the six test areas. Each spectrum was found only when the area of the corresponding rectangular analysis window was greater than the corresponding stable area of the spectrum. The values of the two spectrums decreased when the slope ***, the values of the stable areas of the spectrums in a year or in a season were positively correlated with the variable coefficients of the slope or the profile curvature. The values of the stable areas of the two spectrums in a year or in a season may represent the minimum value of test areas for corresponding future research on the spatial structures of the SASR or PSD. All the findings herein suggest that the spatial structures of the PSD and the SASR are caused by the interactions between solar radiation and terrain relief and that the method for extracting either spectrum is effective for detecting their spatial structures. This study may deepen our understanding of the spatial structure of solar radiation and help us further explore the distribution of solar energy in mountainous re

关键词： Digital elevation model Shielded extraterrestrial solar radiation Spectrum Possible Sunshine duration Slope Loess Plateau

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Relationship between spatial heterogeneity and wavelength in multisensor airborne images

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Geo-spatial information Science 2015年第1期18卷 56-64页

作者： Bingwen QIU Wen QI Chongcheng CHEN Weijiao LI Key Laboratory of Spatial Data Mining&Information Sharing of Ministry of Education Spatial Information Research Centre of Fujian ProvinceFuzhou UniversityFuzhou 350002China

Variogram has been utilized to exploring the spatial heterogeneity of remote sensing images,especially its association with spatial ***,very few attentions have been drawn on evaluating the spatial heterogeneity of multisensor airborne imagery and its relationship with spectral ***,an investigation was carried out on multisensor airborne images to determine the relation between spatial heterogeneity and spectral wavelength for woodland,grass,and urban landscapes by applying variogram *** airborne thematic mapper(ATM),compact airborne spectrographic imager(CASI),and Specim AISA Eagle airborne images at Harwood Forest,Monks wood,Cambridge,and River Frome areas,UK,were *** revealed that(1)the red band contained greater spatial variability than near-infrared wavelengths and other visible wavebands;(2)there was a steep gradient at the red edge in reference to its spatial variability of multisensor airborne images;(3)only for natural landscape such as woodland and grass,near-infrared waveband contains greater within-scene variations than the blue and green bands;(4)compared with the discrepancy of spatial resolution introduced by multisensor images(ATM,CASI,and Eagle),the specific landscape and spectral bands were more important in determining heterogeneity by means of original visible,near-infrared bands,and normalized difference vegetation index(NDVI).These findings remained us to be caution when combining and interpreting spatial variability and spatial structures derived from airborne images with different spatial resolution and spectral ***,the outcomes of this study also have considerable implications in terms of designing and choosing suitable images for different applications.

关键词： variogram modeling spatial heterogeneity airborne thematic mapper(ATM)imagery compact airborne spectrographic imager(CASI)imagery Eagle airborne imagery landscape wavelength

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