检索结果-内蒙古大学图书馆

object-based strategy for generating high-resolution four-dimensional thermal surface models of buildings based on integration of visible and thermal unmanned aerial vehicle imagery

引用

JOURNAL OF APPLIED REMOTE SENSING 2024年第3期18卷

作者： Fallah, Alaleh Samadzadegan, Farhad Javan, Farzaneh Dadrass Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Univ Twente Dept Geoinformat Sci & Earth Observat ITC Enschede Netherlands

The mitigation of energy usage in urban areas, especially in buildings, has recently captured the attention of many city managers. Owing to the thermal images' limited resolution, especially at the edges, creating a high-resolution (HR) surface model from them is a challenging process. This research proposes a two-phase strategy to generate an HR four-dimensional thermal surface model of building roofs. In the single-source modification phase, an enhanced thermal orthophoto is produced by retraining the enhanced deep residual super-resolution deep network, and then, using state-of-the-art structures from motion, semi-global matching, and space intersection. The final surface model's resolution is raised by combining thermal data with visible unmanned aerial vehicle images to overcome the limitation of single-source methods in resolution increase. To this end, after visible orthophoto and digital surface model generation, buildings and their boundaries are extracted using the multi-feature semantic segmentation method. Next, in the multi-source modification phase, a fine-registered enhanced thermal orthophoto is generated, and thermal edges are identified around the boundary of the building. The visible and thermal boundaries are then matched, and any smoothness in the temperature edges is eliminated. The results show that the average difference in position between the thermal edges and building boundaries is reduced, and temperature smoothness is completely eliminated at the building edges. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

关键词： four-dimensional thermal surface model high-resolution super-resolution object-based deep-learning unmanned aerial vehicle

来源：评论

学校读者我要写书评

暂无评论

object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images

引用

PEERJ COMPUTER SCIENCE 2023年 9卷 e1290页

作者： Shen, Xiaole Guo, Yiquan Cao, Jinzhou Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China

Multiscale segmentation (MSS) is crucial in object-based image analysis methods (OBIA). How to describe the underlying features of remote sensing images and combine multiple features for object-based multiscale image segmentation is a hotspot in the field of OBIA. Traditional object-based segmentation methods mostly use spectral and shape features of remote sensing images and pay less attention to texture and edge features. We analyze traditional image segmentation methods and object-based MSS methods. Then, on the basis of comparing image texture feature description methods, a method for remote sensing image texture feature description based on time-frequency analysis is proposed. In addition, a method for measuring the texture heterogeneity of image objects is constructed on this basis. Using bottom-up region merging as an MSS strategy, an object-based MSS algorithm for remote sensing images combined with texture feature is proposed. Finally, based on the edge feature of remote sensing images, a description method of remote sensing image edge intensity and an edge fusion cost criterion are proposed. Combined with the heterogeneity criterion, an object-based MSS algorithm combining spectral, shape, texture, and edge features is proposed. Experiment results show that the comprehensive features object-based MSS algorithm proposed in this article can obtain more complete segmentation objects when segmenting ground objects with rich texture information and slender shapes and is not prone to over-segmentation. Compare with the traditional object-based segmentation algorithm, the average accuracy of the algorithm is increased by 4.54%, and the region ratio is close to 1, which will be more conducive to the subsequent processing and analysis of remote sensing images. In addition, the object-based MSS algorithm proposed in this article can effectively obtain more complete ground objects and can be widely used in scenes such as building extraction.

关键词： Texture feature Edge intensity Time-frequency analysis Multiscale segmentation object-based

来源：评论

学校读者我要写书评

暂无评论

An object-based spectral and elevation feature fusion framework for landslide mapping using time-series Landsat-8 imagery

引用

GEOMATICS NATURAL HAZARDS & RISK 2025年第1期16卷

作者： Wen, Tsung-Han Teo, Tee-Ann Natl Yang Ming Chiao Tung Univ Dept Civil Engn Hsinchu Taiwan

This study presents an object-based spectral and elevation feature fusion framework for landslide mapping using time-series Landsat-8 imagery. The proposed approach integrates time-series multispectral image data with digital elevation models to enhance the robustness and accuracy of landslide detection. The methodology employs multiresolution segmentation with a fusion of spectral and topographic features, enabling the model to capture complex patterns and improve segmentation quality. The study used the multivariate long short-term memory fully convolutional network (LSTM-FCN) architecture to process the multivariate time-series data, significantly enhancing landslide detection performance. The experimental results demonstrated that the fused model outperformed models that used only spectral data, achieving higher accuracy and reducing commission and omission errors. Furthermore, the generalization capability of the model was validated on an independent test site, showcasing its potential applicability in diverse geographical contexts. Integrating diverse data sources is essential for accurate landslide detection and can offer valuable insights to facilitate disaster management and mitigation efforts.

关键词： object-based landslide detection time-series LSTM-FCN

来源：评论

学校读者我要写书评

暂无评论

Extraction of gully erosion using multi-level random forest model based on object-based image analysis

引用

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2025年 137卷

作者： Xu, Mengxia Wang, Mingchang Wang, Fengyan Ji, Xue Liu, Ziwei Liu, Xingnan Zhao, Shijun Wang, Minshui Jilin Univ Coll Geoexplorat Sci & Technol Changchun Peoples R China Design & Res Co Ltd China Water Northeastern Invest Changchun 130021 Peoples R China

Gully erosion cause soil organic matter loss, which poses a grave threat to food security and regional ecological sustainability. Remote sensing monitoring and information extraction of gully erosion are of great significance to protect cultivated land resources and agricultural production. To improve the extraction accuracy of gully erosion, multi-level random forest (RF) extraction model based on object-based image analysis (OBIA) is proposed to extract gully erosion information. The Gaofen-2 (GF-2) image was selected as the main data source, supplemented by topographic data, to segment the features in Dehui City based on multi-scale segmentation method. Fusing spectral, textural and geometric feature information, the RF Gini index (GI) was used for feature optimization. Gully erosion extraction based on feature classes was performed using multi-level RF model based on OBIA in the southwestern part of Dehui City, with an overall accuracy (OA) of 96.71% and a Kappa coefficient (Kappa) of 0.865. Compared with the single-level extraction results, the OA and Kappa were improved by 8.4% and 0.102, which indicated that this model has better performance and has certain application value for the research of gully erosion information extraction and dynamic monitoring.

关键词： Gully erosion Random forest GF-2 object-based Multi-level Remote sensing extraction

来源：评论

学校读者我要写书评

暂无评论

A Bi-Temporal-Feature-Difference- and object-based Method for Mapping Rice-Crayfish Fields in Sihong, China

引用

REMOTE SENSING 2023年第3期15卷 658页

作者： Ma, Siqi Wang, Danyang Yang, Haichao Hou, Huagang Li, Cheng Li, Zhaofu Nanjing Agr Univ Coll Resources & Environm Sci Nanjing 210095 Peoples R China

Rice-crayfish field (i.e., RCF) distribution mapping is crucial for the adjustment of the local crop cultivation structure and agricultural development. The single-temporal images of two phenological periods in the year were classified separately, and then the areas where the water disappeared were identified as RCFs in previous studies. However, due to the differences in the segmentation of lakes and rivers between the two images, the incorrect extraction of RCFs is unavoidable. To solve this problem, a bi-temporal-feature-difference-coupling object-based (BTFDOB) algorithm was proposed in order to map RCFs in Sihong County. We mapped RCFs by segmenting the bi-temporal images simultaneously based on the object-based method and selecting appropriate feature differences as the classification features. To evaluate the applicability, the classification results of the previous two years obtained using the single-temporal- and object-based (STOB) method were compared with the results of the BTFDOB method. The results suggested that spectral feature differences showed high feature importance, which could effectively distinguish the RCFs from non-RCFs. Our method worked well, with an overall accuracy (OA) of 96.77%. Compared with the STOB method, OA was improved by up to 2.18% across three years of data. The RCFs were concentrated in the low-lying eastern and southern regions, and the cultivation scale was expanded in Sihong. These findings indicate that the BTFDOB method can accurately identify RCFs, providing scientific support for the dynamic monitoring and rational management of the pattern.

关键词： rice-crayfish field bi-temporal feature differences object-based classification

来源：评论

学校读者我要写书评

暂无评论

Investigating location-based and object-based inhibition of return in patients with schizophrenia

引用

JOURNAL OF PSYCHIATRIC RESEARCH 2025年 187卷 101-107页

作者： Wu, Caili Dagg, Paul Molgat, Carmen Interior Hlth Tertiary Mental Hlth & Subst Use Serv Kamloops BC V2C 6W5 Canada Univ British Columbia Dept Psychiat Vancouver BC V6T 1Z4 Canada

Inhibition of return (IOR) is a well-observed effect in attention orienting tasks. People are slower and/or less accurate to detect a target that appeared at a recently attended location than a novel one. Sometimes inhibition can slow the return of attention to a particular object (object-based IOR). object-based IOR has been demonstrated in both dynamic and static displays. In static displays, presumably object-based IOR can be additive to location-based IOR. Research investigating IOR in patients with schizophrenia have yielded conflicting results. To date no study has examined object-based IOR in patients with schizophrenia. The current study used a cue back to fixation exogenous cuing paradigm with static displays to investigate both location-based and objectbased IOR in patients with schizophrenia and healthy controls. The results showed that significant locationbased IOR occurred in both groups, with reduced magnitude of IOR in the patient group than in the control group. No additive object-based IOR was observed in either group. Furthermore, IOR in patients did not correlate to their age, education, and symptom severity. However, it is possible that IOR is related to their general cognitive function. Overall, the results support the hypothesis that location-based IOR plays a much more essential role in the inhibitory mechanisms that facilitate visual search.

关键词： Schizophrenia Inhibition of return Location-based object-based Experimental Cognitive impairment

来源：评论

学校读者我要写书评

暂无评论

Spatial transfer of object-based statistical learning

引用

ATTENTION PERCEPTION & PSYCHOPHYSICS 2024年第3期86卷 768-775页

作者： van Moorselaar, Dirk Theeuwes, Jan Vrije Univ Amsterdam Dept Expt & Appl Psychol Amsterdam Netherlands Inst Brain & Behav Amsterdam iBBA Amsterdam Netherlands ISPA Inst Univ William James Ctr Res Lisbon Portugal

A large number of recent studies have demonstrated that efficient attentional selection depends to a large extent on the ability to extract regularities present in the environment. Through statistical learning, attentional selection is facilitated by directing attention to locations in space that were relevant in the past while suppressing locations that previously were distracting. The current study shows that we are not only able to learn to prioritize locations in space but also locations within objects independent of space. Participants learned that within a specific object, particular locations within the object were more likely to contain relevant information than other locations. The current results show that this learned prioritization was bound to the object as the learned bias to prioritize a specific location within the object stayed in place even when the object moved to a completely different location in space. We conclude that in addition to spatial attention prioritization of locations in space, it is also possible to learn to prioritize relevant locations within specific objects. The current findings have implications for the inferred spatial priority map of attentional weights as this map cannot be strictly retinotopically organized.

关键词： Attention object-based Attention in learning Visual search

来源：评论

学校读者我要写书评

暂无评论

object-based Image Analysis of Hyper Spectral Imagery Using Semantic Segmentation Techniques

Object-Based Image Analysis of Hyper Spectral Imagery Using ...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Amit Kumar Bargavi, Manju Singh, Akhilendra Pratap College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

object-based image analysis of Hyperspectral Imagery using Semantic Segmentation strategies is a singular approach for analyzing far-off sensing statistics. This method leverages the energy of a superior system gaining knowledge of (ML) and computer vision algorithms to analyze multidimensional hyperspectral image datasets. The goal is to robustly organize pixels into clusters in step with their spectral and spatial traits. Those clusters are then used to give meaningful records approximately the content material of the photo., the enter pix are pre-processed to reduce noise and boom contrast. A semantic segmentation algorithm is then used to generate excessive-degree masks of the items of interest. The outcomes of those masks are mixed with the input hyperspectral statistics to create several feature vectors describing the spectral and texture homes of every cluster. Sooner or later, a device-mastering algorithm categorizes the gadgets consistent with their traits, presenting precise information about the items in the picture. © 2024 IEEE.

关键词： algorithms Hyperspectral Imagery meaningful object-based

来源：评论

学校读者我要写书评

暂无评论

An object-based Ground Filtering of Airborne LiDAR Data for Large-Area DTM Generation

引用

REMOTE SENSING 2023年第16期15卷 4105-4105页

作者： Song, Hunsoo Jung, Jinha Purdue Univ Lyles Sch Civil Engn 550 Stadium Mall Dr W Lafayette IN 47907 USA

Digital terrain model (DTM) creation is a modeling process that represents the Earth's surface. An aptly designed DTM generation method tailored for intended study can significantly streamline ensuing processes and assist in managing errors and uncertainties, particularly in large-area projects. However, existing methods often exhibit inconsistent and inexplicable results, struggle to clearly define what an object is, and often fail to filter large objects due to their locally confined operations. We introduce a new DTM generation method that performs object-based ground filtering, which is particularly beneficial for urban topography. This method defines objects as areas fully enclosed by steep slopes and grounds as smoothly connected areas, enabling reliable "object-based" segmentation and filtering, extending beyond the local context. Our primary operation, controlled by a slope threshold parameter, simplifies tuning and ensures predictable results, thereby reducing uncertainties in large-area modeling. Uniquely, our method considers surface water bodies in modeling and treats connected artificial terrains (e.g., overpasses) as ground. This contrasts with conventional methods, which often create noise near water bodies and behave inconsistently around overpasses and bridges, making our approach particularly beneficial for large-area 3D urban mapping. Examined on extensive and diverse datasets, our method offers unique features and high accuracy, and we have thoroughly assessed potential artifacts to guide potential users.

关键词： digital terrain model airborne laser scanning object-based ground filtering LiDAR

来源：评论

学校读者我要写书评

暂无评论

A Landmark Selection Method for object-based Visual Outdoor Localization Approaches of Automated Ground Vehicles 19

A Landmark Selection Method for Object-Based Visual Outdoor ...

引用

19th 3D GeoInfo Conference / 31st EG-ICE International Workshop on Intelligent Computing in Engineering

作者： Frank, Florian Buckel, Peter Hoegner, Ludwig Hofstedt, Petra Inst Continuing Educ Knowledge & Technol Transfer Friedrichshafen Germany Baden Wuerttemberg Cooperat State Univ DHBW Friedrichshafen Germany Hsch Munchen Univ Appl Sci Dept Geoinformat Munic Germany Brandenburg Univ Technol Cottbus Senftenberg Programming Languages & Compiler Construct Cottbus Germany

Autonomous vehicles must navigate independently in an outdoor environment using features or objects. However, some objects may be more or less suitable for localization due to their attributes. Therefore, this work investigates the suitability of landmarks for camera- and object-based outdoor localization methods. First, object attributes are methodically derived from the requirements of object-based localization. The physical representation on the camera image plane, probability of occurrence, and persistence were identified as influencing the object localization suitability. The influence of the object's camera image plane representation regarding object recognition algorithms is not considered or discussed, but advice on the minimum object pixel size is provided. The first milestone was the creation of an equation for object localization suitability calculation by normalizing and multiplying the identified attributes. Simultaneously, potential objects from the outdoor environment were identified, resulting in a structured object catalog. The results of the equation and catalog are a ranked according to the object localization suitability in a comparison table. Our comparison demonstrates that objects such as buildings or trees are more suitable than street lane markings for self-localization. However, most current datasets do not include the proposed instantiated objects. The paper addresses this issue, assists in the object selection for outdoor localization methods and provides input for the creation of future-oriented datasets and autonomous driving maps.

关键词： Outdoor Localization object-based Camera Landmark Autonomous Driving Dataset

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：