检索结果-内蒙古大学图书馆

Data fusion of extremely high resolution aerial imagery and LiDAR data for automated railroad centre line reconstruction

引用

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2011年第6期66卷 S40-S51页

作者： Beger, Reinhard Gedrange, Claudia Hecht, Robert Neubert, Marco Leibniz Inst Ecol & Reg Dev IOER Dresden Germany

The quality of remotely sensed data in regards of accuracy and resolution has considerably improved in recent years. Very small objects are detectable by means of imaging and laser scanning, yet there are only few studies to use such data for large scale mapping of railroad infrastructure. In this paper, an approach is presented that integrates extremely high resolution ortho-imagery and dense airborne laser scanning point clouds. These data sets are used to reconstruct railroad track centre lines. A feature level data fusion is carried out in order to combine the advantages of both data sets and to achieve a maximum of accuracy and completeness. The workflow consists of three successive processing steps. First, object-based image analysis is used to derive a railroad track mask from ortho-imagery. This spatial location information is then combined with the height information to classify the laser points. Lastly, the location of railroad track centre lines from these classified points were approximated using a feature extraction method based on an adapted random sample consensus algorithm. This workflow is tested on two railroad sections and was found to deliver very accurate results in a quickly and highly automated manner. (C) 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

关键词： Multi-source data fusion object-based image analysis LiDAR Aerial imagery RANSAC object extraction

来源：评论

学校读者我要写书评

暂无评论

Integrating Semi-Supervised Learning with an Expert System for Vegetation Cover Classification Using Sentinel-2 and RapidEye Data

引用

REMOTE SENSING 2022年第15期14卷 3605页

作者： Layegh, Nasir Farsad Darvishzadeh, Roshanak Skidmore, Andrew K. Persello, Claudio Krueger, Nina Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands MOSS Comp Graf Syst GmbH Hohenbrunner Weg 13 D-82024 Munich Germany

In complex classification tasks, such as the classification of heterogeneous vegetation covers, the high similarity between classes can confuse the classification algorithm when assigning the correct class labels to unlabelled samples. To overcome this problem, this study aimed to develop a classification method by integrating graph-based semi-supervised learning (SSL) and an expert system (ES). The proposed method was applied to vegetation cover classification in a wetland in the Netherlands using Sentinel-2 and RapidEye imagery. Our method consisted of three main steps: object-based image analysis (OBIA), integration of SSL and an ES (SSLES), and finally, random forest classification. The generated image objects and the related features were used to construct the graph in SSL. Then, an independently developed and trained ES was used in the labelling stage of SSL to reduce the uncertainty of the process, before the final classification. Different spectral band combinations of Sentinel-2 were then considered to improve the vegetation classification. Our results show that integrating SSL and an ES can result in significantly higher classification accuracy (83.6%) compared to a supervised classifier (64.9%), SSL alone (71.8%), and ES alone (69.5%). Moreover, utilisation of all Sentinel-2 red-edge spectral band combinations yielded the highest classification accuracy (overall accuracy of 83.6% with SSLES) compared to the inclusion of other band combinations. The results of this study indicate that the utilisation of an ES in the labelling process of SSL improves the reliability of the process and provides robust performance for the classification of vegetation cover.

关键词： semi-supervised learning expert system object-based image analysis Sentinel-2 vegetation cover image classification

来源：评论

学校读者我要写书评

暂无评论

Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes

引用

REMOTE SENSING OF ENVIRONMENT 2019年 230卷 111207-000页

作者： Rasanen, Aleksi Virtanen, Tarmo Univ Helsinki Helsinki Inst Sustainabil Sci HELSUS Fac Biol & Environm Sci Ecosyst & Environm Res Programme POB 65 FI-00014 Helsinki Finland Norwegian Univ Sci & Technol Dept Geog NO-7491 Trondheim Norway

It has been argued that even centimeter-level resolution is needed for mapping vegetation patterns in spatially heterogeneous landscapes such as northern peatlands. However, there are few systematic tests for determining what kind of spatial resolution and data combinations are needed and what the differences in mapping accuracy are when different datasets are omitted or included. We conducted 78 different object-based supervised random forest classifications on a patterned fen and its surroundings in Kaamanen, northern Finland, using remotely sensed optical imagery, topography, and vegetation height datasets from different platforms (unmanned aerial vehicle (UAV), aerial, satellite) with spatial resolution ranging from 5 cm to 3m. We compared differences in classification performance when we altered (1) classification and segmentation input data and features calculated from the data, or (2) the segmentation scale. We constructed training data with the help of transect-based field sampling and UAV imagery and tested classification accuracy using 412 field-surveyed vegetation plots. The most accurate classifications (75.7% overall accuracy) were obtained when we segmented a 5 cm resolution UAV image with a small segmentation scale and calculated features from all datasets. Classification accuracy was 2.2 percentage points (pp) lower with the most accurate aerial image (50 cm resolution) based classification, and 7.6 pp and 11.9pp lower with the most accurate WorldView-2 (2 m resolution) and PlanetScope (3 m resolution) satellite image based classifications respectively. Classification accuracies were low (46.7-56.0%) when we used only spectral data from one dataset. The inclusion of gray-level co-occurrence matrix textural features increased classification accuracy by 0.4-12.1pp and inclusion of multiple datasets by 8.2-25.0pp. Segmentation scale had a minor effect on classification accuracy (2.5-7.3pp difference between the finest and coarsest segmentation scale);ho

关键词： Arctic Data fusion Drone Land cover classification Lidar Northern boreal object-based image analysis Peatland UAS Ultra-high spatial resolution Very-high spatial resolution

来源：评论

学校读者我要写书评

暂无评论

object-based Predictive Modeling (OBPM) for Archaeology: Finding Control Places in Mountainous Environments

引用

REMOTE SENSING 2021年第6期13卷 1197-1197页

作者： Magnini, Luigi Bettineschi, Cinzia Univ Sassari Dept Hist Human Sci & Educ I-07100 Sassari Italy Univ Padua Dept Cultural Heritage Archaeol & Hist Art Cinema I-35139 Padua Italy

This contribution examines the potential of object-based image analysis (OBIA) for archaeological predictive modeling starting from elevation data, by testing a ruleset for the location of "control places" on two test areas in the Alpine environment (northern Italy). The ruleset was developed on the western Asiago Plateau (Vicenza Province, Veneto) and subsequently re-applied (semi)automatically in the Isarco Valley (South Tirol). Firstly, we considered the physiographic, climatic, and morphological characteristics of the selected areas and we applied 3 DTM processing techniques: Slope, local dominance, and solar radiation. Subsequently, we employed an object-based approach to classification. Solar radiation, local dominance, and slope were visualized as a three-layer RGB image that was segmented with the multiresolution algorithm. The classification was implemented with a ruleset that selected only image-objects with high local dominance and solar radiation, but low slope, which were considered more suitable parameters for human occupation. The classification returned five areas on the Asiago Plateau that were remotely and ground controlled, confirming anthropic exploitation covering a time span from protohistory (2nd-1st millennium BC) to the First World War. Subsequently, the same model was applied to the Isarco Valley to verify the replicability of the method. The procedure resulted in 36 potential control places which find good correspondence with the archaeological sites discovered in the area. Previously unknown contexts were further controlled using very high-resolution (VHR) aerial images and digital terrain model (DTM) data, which often suggested a possible (pre-proto)historic human frequentation. The outcomes of the analysis proved the feasibility of the approach, which can be exported and applied to similar mountainous landscapes for site predictivity analysis.

关键词： predictive modeling object-based image analysis control places archaeological remote sensing northern Italy hilltop settlements protohistory

来源：评论

学校读者我要写书评

暂无评论

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

引用

REMOTE SENSING 2016年第3期8卷 235-235页

作者： Karakizi, Christina Oikonomou, Marios Karantzalos, Konstantinos Natl Tech Univ Athens Remote Sensing Lab Heroon Polytech 9 Zografos 15780 Greece

In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, developed and evaluated an object-based classification framework towards the detection of vineyards, the vine canopy extraction and the vine variety discrimination from very high resolution multispectral data. A novel set of spectral, spatial and textural features, as well as rules, segmentation scales and a set of parameters are proposed based on object-based image analysis. The validation of the developed methodology was carried out on multitemporal WorldView-2 satellite data at four different viticulture regions in Greece. Concurrent in situ canopy reflectance observations were acquired from a portable spectroradiometer during the field campaigns. The performed quantitative evaluation indicated that the developed approach managed in all cases to detect vineyards with high completeness and correctness detection rates, i.e., over 89%. The vine canopy extraction methodology was validated with overall accuracy (OA) rates of above 96%. The quantitative evaluation regarding the vine variety discrimination task, including experiments with up to six different varieties, reached OA rates above 85% at the parcel level. The combined analysis of the experimental results with the spectral signatures from the in situ reflectance data indicated that certain vine varieties (e.g., Merlot) presented distinct spectral patterns across the VNIR spectrum.

关键词： object-based image analysis features spectral signatures crops precision viticulture classification

来源：评论

学校读者我要写书评

暂无评论

object-based Detection of Impervious Area in Agriculture Land Using High-Resolution Satellite image

引用

测量工程 2019年 58卷 21-36页

作者： Nurahida Laili 王驥魁

Agricultural land is important for the food security of a nation. However, the total agriculture land area across the world has been decreasing every year due to human activities. Developing technology, especially in businesses and other industries, is resulting in the increased construction of buildings, which then results in loss of land. People build factories, houses, etc. on agricultural areas, which then become impervious areas as the water absorption ability of the ground underneath decreases. A periodical assessments to record the change in the total farmland area need to be carried out. Traditional manual digitation is usually conducted to detect impervious areas in agricultural land. However, this process is laborious. Thus, this study uses an object-based approach that employs high-resolution satellite images to detect the impervious areas. A pan-sharpened Pleiades image with 0.5-meter resolution and four spectral bands were utilized. The HSV (hue-saturation-value) bands derived from the RGB bands were added as object features to extract the impervious area. The spectral feature, i.e., HSV, NDVI, NDWI, the soil extraction algorithms, and the shape feature, i.e., size and compactness, were deployed to extract the impervious area within the agricultural land. An F1-score of 0.70 was obtained from this proposed method. Furthermore, the transferability test was carried out by applying the method to different image size and different image acquisition time. The result shows that the method is stable to process various image scenes.

关键词： image classification image segmentation object-based image analysis Pleiades image

来源：评论

学校读者我要写书评

暂无评论

A Multi-Satellite Mapping Framework for Floating Kelp Forests

引用

REMOTE SENSING 2023年第5期15卷 1276-1276页

作者： Gendall, Lianna Schroeder, Sarah B. Wills, Peter Hessing-Lewis, Margot Costa, Maycira Univ Victoria SPECTRAL Remote Sensing Lab 3800 Finnerty Rd Victoria BC V8P 5C2 Canada Canadian Hydrog Serv Inst Ocean Sci 9860 West Saanich Rd Sidney BC V8L 4B2 Canada Hakai Inst Campbell River BC V0P 1H0 Canada

Kelp forests provide key habitat on the Pacific Coast of Canada;however, the long-term changes in their distribution and abundance remain poorly understood. With advances in satellite technology, floating kelp forests can now be monitored across large-scale areas. We present a methodological framework using an object-based image analysis approach that enables the combination of imagery from multiple satellites at different spatial resolutions and temporal coverage, to map kelp forests with floating canopy through time. The framework comprises four steps: (1) compilation and quality assessment;(2) preprocessing;(3) an object-oriented classification;and (4) an accuracy assessment. Additionally, the impact of spatial resolution on the detectability of floating kelp forests is described. Overall, this workflow was successful in producing accurate maps of floating kelp forests, with global accuracy scores of between 88% and 94%. When comparing the impact of resolution on detectability, lower resolutions were less reliable at detecting small kelp forests in high slope areas. based on the analysis, we suggest removing high slope areas (11.4%) from time series analyses using high- to medium-resolution satellite imagery and that error, in this case up to 7%, be considered when comparing imagery at different resolutions in low-mid slope areas through time.

关键词： kelp forests multispectral satellite time series spatial resolution object-based image analysis remote sensing

来源：评论

学校读者我要写书评

暂无评论

Automating field boundary delineation with multi-temporal Sentinel-2 imagery

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2019年 167卷 105078-000页

作者： Watkins, Barry Van Niekerk, Adriaan Univ Stellenbosch Dept Geog & Environm Studies Stellenbosch South Africa

Knowledge of the extent and location of cultivated fields are critical for agricultural monitoring, food security planning and commodity trading. The increased observational capacity of remotely sensed data, such as Sentinel-2 imagery, constitutes a major advantage over in situ methods, particularly for agricultural applications that require frequent updates. However, the development of fully automated cultivated field delineation methods remains a challenge. In this study an existing object-based image analysis (OBIA) methodology, called Canny edge-detection in conjunction with watershed segmentation (CEWS), was modified to automatically identify and delineate agricultural fields from multi-temporal Sentinel-2 imagery in five diverse agricultural landscapes. The robustness of the technique was evaluated by comparing its outputs to those of standard perpixel, supervised classifications. Reference field boundaries were manually digitised and used for quantitative accuracy assessments. Area and edge metrics were used to evaluate the accuracy of the extracted boundaries. Results show that, on average, CEWS produced significantly higher field boundary accuracies than the supervised per-pixel approach in terms of both mean absolute error (MAE) (similar to 31 m lower) and overall accuracy (similar to 13.8% higher). The closed field boundaries produced by CEWS, as well as its capability to operate without any a priori knowledge, was found to be its main strengths for integration into operational workflows. However, in complex landscapes (e.g. Swartland and Mooketsi) it was found that some fields had weak boundaries due to the homogeneity between adjacent fields, making edge detection problematic. The incorporation of higher resolution imagery into the edge detection process is proposed, particularly in agricultural areas with very small and irregularly shaped fields. Despite these challenges, it was concluded that the multi-temporal CEWS approach will perform well in most

关键词： Sentinel-2 imagery Edge detection Watershed segmentation object-based image analysis Agricultural field boundary delineation

来源：评论

学校读者我要写书评

暂无评论

Change Detection of High Spatial Resolution images based on Region-Line Primitive Association analysis and Evidence Fusion

引用

REMOTE SENSING 2019年第21期11卷 2484-2484页

作者： Huang, Jiru Liu, Yang Wang, Min Zheng, Yalan Wang, Jie Ming, Dongping Nanjing Normal Univ Sch Geog Nanjing 210023 Jiangsu Peoples R China Nanjing Normal Univ Minist Educ Key Lab Virtual Geog Environm Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China State Key Lab Cultivat Base Geog Environm Evolut Nanjing 210023 Jiangsu Peoples R China China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China

Change detection (CD) remains an important issue in remote sensing applications, especially for high spatial resolution (HSR) images, but it has yet to be fully resolved. This work proposes a novel object-based change detection (OBCD) method for HSR images that is based on region-line primitive association analysis and evidence fusion. In the proposed method, bitemporal images are separately segmented, and the segmentation results are overlapped to obtain the temporal region primitives (TRPs). The temporal line primitives (TLPs) are obtained by straight line detection on bitemporal images. In the initial CD stage, Dempster-Shafer evidence theory fuses the multiple items of evidence of the TRPs' spectrum, edge, and gradient changes, and obtains the initial changed areas. In the refining CD stage, the association between the TRPs and their contacting TLPs in the unchanged areas is established on the basis of the region-line primitive association framework, and the TRPs' main line directions (MLDs) are calculated. Some changed TRPs omitted in the initial CD stage are recovered by their MLD changes, thereby refining the initial CD results. Different from common OBCD methods, the proposed method considers the change evidence of TRPs' internal and boundary information simultaneously via information complementation between TRPs and TLPs. The proposed method can significantly reduce missed alarms while maintaining a low level of false alarms in OBCD, thereby improving total accuracy. In our experiments, our method is superior to common CD methods, including change vector analysis (CVA), PCA-k-means, and iterative reweighted multivariate alteration detection (IRMAD), in terms of overall accuracy, missed alarms, and Kappa coefficient.

关键词： object-based image analysis change detection high spatial resolution imagery evidence theory region-line primitive association framework

来源：评论

学校读者我要写书评

暂无评论

High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH₄ Fluxes by use of Unmanned Aerial System (UAS) and CIR imagery

引用

REMOTE SENSING 2016年第3期8卷 173-173页

作者： Lehmann, Jan R. K. Muenchberger, Wiebke Knoth, Christian Blodau, Christian Nieberding, Felix Prinz, Torsten Pancotto, Veronica A. Kleinebecker, Till Univ Munster Inst Landscape Ecol Heisenbergstr 2 D-48149 Munster Germany Univ Munster Inst Geoinformat Heisenbergstr 2 D-48149 Munster Germany Ctr Austral Invest Cient CADIC CONICET B Houssay 200 RA-9410 Ushuaia Tierra Del Fueg Argentina

South Patagonian peat bogs are little studied sources of methane (CH4). Since CH4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH4 fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH4 fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species- and 0.83 for the microform-level classification, respectively. CH4 fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH4 closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH4 fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg center dot m(-2)center dot day(-1) to 26.8 mg center dot m(-2)center dot day(-1). Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH4 fluxes when plot-scale fluxes are up-scaled.

关键词： RPAS closed chamber object-based image analysis UAV peatland methane OBIA classification

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：