检索结果-内蒙古大学图书馆

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

An object-based Change Detection Method Considering Temporal-Spatial Similarity in Long Time Series

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Chen, Lisu Li, Shanhong Zhu, Enyan Shanghai Maritime Univ Coll Ocean Sci & Engn Shanghai 201306 Peoples R China Ningbo Univ Zhejiang Collaborat Innovat Ctr Land & Marine Spat Ningbo 315211 Peoples R China Shanghai Maritime Univ Coll Transport & Commun Shanghai 201306 Peoples R China

A new object-based change detection method has been proposed to address the limitations of existing research based on pixel-based change detection, as well as the neglect of concurrent changes, pixel spatiotemporal information, and the alteration of boundary information due to changes. Additionally considering the concurrence of changes, the measurement of similarity in time series of adjacent pixels needs to take into account the phase shifting and scaling of the time series. In this study, based on the considerations mentioned above, we first constructed time series using available Landsat 5, 7, and 8 data collected in the study area and used a pixel-based change detection method to obtain change information. Then, considering the heterogeneity within objects, we first combined the change information to extract change seeds. After that, the inside_similarity metric was introduced, which is computed by dynamic time wraping (DTW) algorithm, and it was used to impose sequential constraints on the expansion of seeds. Considering that changes can alter both the interior and boundaries of objects, we applied a conditional judgment to all pixels outside the seeds. Through quantitative assessment in three experimental areas, the method proposed in this article improved producer's accuracy (PA) by 6.9%, 2.7%, and 5.5% and user's accuracy (UA) by 6.1%, 3.1%, and 6.6% with F1-score improved by 6.49%, 2.91%, and 7.45% compared to purely pixel-based change detection methods. Combined with qualitative assessment, the object-based change detection method is proved to increase the accuracy of change detection.

关键词： Time series analysis Accuracy Landsat Data mining Feature extraction Classification algorithms Change detection algorithms Remote sensing Image segmentation Urban areas Change detection dynamic time wraping (DTW) flood fill object-based

来源：评论

学校读者我要写书评

暂无评论

Semantic and Geometric Fusion for object-based 3D Change Detection in LiDAR Point Clouds

引用

REMOTE SENSING 2025年第7期17卷 1311-1311页

作者： Kharroubi, Abderrazzaq Remondino, Fabio Ballouch, Zouhair Hajji, Rafika Billen, Roland Univ Liege Spheres Res Unit Geospatial Data Sci & City Informat Modeling Lab G B-4000 Liege Belgium Bruno Kessler Fdn 3D Opt Metrol Unit I-38123 Trento Italy Hassan II IAV Coll Geomat Sci & Surveying Engn Rabat 10101 Morocco

Accurate three-dimensional change detection is essential for monitoring dynamic environments such as urban areas, infrastructure, and natural landscapes. Point-based methods are sensitive to noise and lack spatial coherence, while object-based approaches rely on clustering, which can miss fine-scale changes. To address these limitations, we introduce an object-based change detection framework integrating semantic segmentation and geometric change indicators. The proposed method first classifies bi-temporal point clouds into ground, vegetation, buildings, and moving objects. A cut-pursuit clustering algorithm then segments the data into spatially coherent objects, which are matched across epochs using a nearest-neighbor search based on centroid distance. Changes are characterized by a combination of geometric features-including verticality, sphericity, omnivariance, and surface variation-and semantic information. These features are processed by a random forest classifier to assign change labels. The model is evaluated on the Urb3DCD-v2 dataset, with feature importance analysis to identify important features. Results show an 81.83% mean intersection over union. An additional ablation study without clustering reached 83.43% but was more noise-sensitive, leading to fragmented detections. The proposed method improves the efficiency, interpretability, and spatial coherence of change classification, making it well suited for large-scale monitoring applications.

关键词： 3D change detection point clouds semantic segmentation cut-pursuit object-based point-based LiDAR

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

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 Tertiary Mental Health & Substance Use Services Interior Health Kamloops V2C 6W5 BC Canada Department of Psychiatry University of British Columbia Vancouver V6T 1Z4 BC 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 object-based IOR in patients with schizophrenia and healthy controls. The results showed that significant location-based 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. © 2025

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

来源：评论

学校读者我要写书评

暂无评论

object-based Classification Method for PolSAR Images with Improved Scattering Powers and Contextual Features

引用

Chinese Journal of Electronics 2017年第4期26卷 803-809页

作者： YUAN Zhengwu CHEN Ran CHEN Cuiping LUO Xiaobo LIU Minghao College of computer science and technology Chongqing University of Posts and Telecommunications

This paper proposes a new object-based classification method for Polarimetric synthetic aperture radar(Pol SAR) images, which considers scattering powers from an improved model-based polarimetric decomposition approach, as well as the spatial and textural features. With the decomposition, the scattering ambiguities between oriented buildings and vegetation are reduced. Furthermore,various contextual features are extracted from the object and incorporated into the K-nearest neighbors(k-NN)based classification. To reduce the feature redundancy, a new Supervised locally linear embedding(S-LLE) dimensionality reduction method is introduced to map the high dimensional polarimetric signatures into the most compact low-dimensional structure for classification. Experimental results with Airborne synthetic aperture rada(AIRSAR)C-band Pol SAR image demonstrate the superior performance to other methods.

关键词： object-based Polarimetric decomposi tion Contextual features Supervised locally linear embed ding(S-LLE) Polarimetric synthetic aperture radar(PolSAR)

来源：评论

学校读者我要写书评

暂无评论

object-based Multipass InSAR via Robust Low-Rank Tensor Decomposition

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2018年第6期56卷 3062-3077页

作者： Kang, Jian Wang, Yuanyuan Schmitt, Michael Zhu, Xiao Xiang Tech Univ Munich Signal Proc Earth Observat D-80333 Munich Germany German Aerosp Ctr DLR Remote Sensing Technol Inst D-82234 Wessling Germany

The most unique advantage of multipass synthetic aperture radar interferometry (InSAR) is the retrieval of long-term geophysical parameters, e.g., linear deformation rates, over large areas. Recently, an object-based multipass InSAR framework has been proposed by Kang, as an alternative to the typical single-pixel methods, e.g., persistent scatterer interferometry (PSI), or pixel-cluster-based methods, e.g., SqueeSAR. This enables the exploitation of inherent properties of InSAR phase stacks on an object level. As a follow-on, this paper investigates the inherent low rank property of such phase tensors and proposes a Robust Multipass InSAR technique via object-based low rank tensor decomposition. We demonstrate that the filtered InSAR phase stacks can improve the accuracy of geophysical parameters estimated via conventional multipass InSAR techniques, e.g., PSI, by a factor of 10-30 in typical settings. The proposed method is particularly effective against outliers, such as pixels with unmodeled phases. These merits, in turn, can effectively reduce the number of images required for a reliable estimation. The promising performance of the proposed method is demonstrated using high-resolution TerraSAR-X image stacks.

关键词： Iterative reweight low rank object-based synthetic aperture radar (SAR) SAR interferometry (InSAR) tensor decomposition

来源：评论

学校读者我要写书评

暂无评论

object-based image analysis for mapping geomorphic zones of coral reefs in the Xisha Islands, China

引用

Acta Oceanologica Sinica 2016年第12期35卷 19-27页

作者： XU Jingping ZHAO Jianhua LI Fang WANG Lin SONG Derui WEN Shiyong WANG Fei GAO Ning National Marine Environmental Monitoring Center

Mapping regional spatial patterns of coral reef geomorphology provides the primary information to understand the constructive processes in the reef ecosystem. However, this work is challenged by the pixel-based image classification method for its comparatively low accuracy. In this paper, an object-based image analysis(OBIA)method was presented to map intra-reef geomorphology of coral reefs in the Xisha Islands, China using Landsat 8satellite imagery. Following the work of the Millennium Coral Reef Mapping Project, a regional reef class hierarchy with ten geomorphic classes was first defined. Then, incorporating the hierarchical concept and integrating the spectral and additional spatial information such as context, shape and contextual relationships, a large-scale geomorphic map was produced by OBIA with accuracies generally more than 80%. Although the robustness of OBIA has been validated in the applications of coral reef mapping from individual reefs to reef system in this paper, further work is still required to improve its transferability.

关键词： object-based Landsat 8 geomorphic mapping Xisha Islands

来源：评论

学校读者我要写书评

暂无评论

object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site

引用

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2008年第2期63卷 237-250页

作者： Mallinis, Georgios Koutsias, Nikos Tsakiri-Strati, Maria Karteris, Michael Aristotle Univ Thessaloniki Sch Forestry & Nat Environ GR-54124 Thessaloniki Greece Univ Ioannina Dept Environm & Nat Resources Management GR-30100 Agrinion Greece Aristotle Univ Thessaloniki Fac Rural & Surveying Engn Dept Cadastre Photogrammetry & Cartog GR-54124 Thessaloniki Greece

A multi-scale, object-based analysis of a Quickbird satellite image has been carried out to delineate forest vegetation polygons in a natural forest in Northern Greece. Following a multi-resolution segmentation, a classification tree was developed and compared using a nearest neighbour classifier for the assignment of image segments to classes. Additionally, texture images derived from local indicators of spatial association were calculated and used to improve the classification. The best results were obtained when texture images were considered in the classification sequence, however, the accuracy of the final map did not exceed 80%. The classification tree yielded better results than the nearest neighbour algorithm. Overall, the object-based classification approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping Mediterranean forest ecosystems. (C) 2007 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： forest classification texture quickbird object-based multi-scale

来源：评论

学校读者我要写书评

暂无评论

object-based Area-to-Point Regression Kriging for Pansharpening

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第10期59卷 8599-8614页

作者： Zhang, Yihang Atkinson, Peter M. Ling, Feng Foody, Giles M. Wang, Qunming Ge, Yong Li, Xiaodong Du, Yun Chinese Acad Sci Innovat Acad Precis Measurement Sci & Technol Key Lab Monitoring & Estimate Environm & Disaster Wuhan 430077 Peoples R China Univ Lancaster Fac Sci & Technol Lancaster Environm Ctr Lancaster LA1 4YQ England Univ Nottingham Sch Geog Nottingham NG7 2RD England Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China

Optical earth observation satellite sensors often provide a coarse spatial resolution (CR) multispectral (MS) image together with a fine spatial resolution (FR) panchromatic (PAN) image. Pansharpening is a technique applied to such satellite sensor images to generate an FR MS image by injecting spatial detail taken from the FR PAN image while simultaneously preserving the spectral information of MS image. Pansharpening methods are mostly applied on a per-pixel basis and use the PAN image to extract spatial detail. However, many land cover objects in FR satellite sensor images are not illustrated as independent pixels, but as many spatially aggregated pixels that contain important semantic information. In this article, an object-based pansharpening approach, termed object-based area-to-point regression kriging (OATPRK), is proposed. OATPRK aims to fuse the MS and PAN images at the object-based scale and, thus, takes advantage of both the unified spectral information within the CR MS images and the spatial detail of the FR PAN image. OATPRK is composed of three stages: image segmentation, object-based regression, and residual downscaling. Three data sets acquired from IKONOS and Worldview-2 and 11 benchmark pansharpening algorithms were used to provide a comprehensive assessment of the proposed OATPRK approach. In both the synthetic and real experiments, OATPRK produced the most superior pan-sharpened results in terms of visual and quantitative assessment. OATPRK is a new conceptual method that advances the pixel-level geostatistical pansharpening approach to the object level and provides more accurate pan-sharpened MS images.

关键词： Downscaling geostatistics image fusion object-based pansharpening segmentation

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：