检索结果-内蒙古大学图书馆

An object-based SVM method incorporating optimal segmentation scale estimation using Bhattacharyya Distance for mapping salt cedar (Tamarisk spp.) with QuickBird imagery

引用

GISCIENCE & REMOTE SENSING 2015年第3期52卷 257-273页

作者： Xun, Lu Wang, Le Capital Normal Univ Coll Resources Environm & Tourism Key Lab Resource Environm & Geog Informat Syst Key Lab Dimens Informat Acquisit & Applicat 3Min Beijing 100048 Peoples R China SUNY Buffalo Dept Geog 105 Wilkeson Quadrangle Buffalo NY 14261 USA

Salt cedar, a predominant arid type of vegetation in Western China, provides invaluable ecosystem services and is important for economic sustainability. Most previous attempts to map salt cedar have been focused at the pixel level and not at the object level. A particular problem for object-based classification is determining the optimal scale for the segmentation. In this study, we proposed a new object-based image analysis method for classifying high resolution satellite image with support vector machine (SVM). Specifically, we set forth three objectives: (1) to choose the optimal multiscale parameters for different cover types with the aid of the Bhattacharyya Distance index;(2) to extract the class specific features for different classes to feed into the SVM classification procedure;and (3) to compare three different classification methods: the integration of object-SVM, SVM at the pixel level and nearest-neighbor at the object level. The result of the case study demonstrated that the multiscale object-SVM method, which employed spectral, texture and shadow features, produced the best overall accuracy (91.6%).

关键词： object-based image analysis SVM classification Bhattacharyya Distance salt cedar mapping QuickBird

来源：评论

学校读者我要写书评

暂无评论

Towards a framework for agent-based image analysis of remote-sensing data

引用

INTERNATIONAL JOURNAL OF image AND DATA FUSION 2015年第2期6卷 115-137页

作者： Hofmann, Peter Lettmayer, Paul Blaschke, Thomas Belgiu, Mariana Wegenkittl, Stefan Graf, Roland Lampoltshammer, Thomas Josef Andrejchenko, Vera Salzburg Univ Interfac Dept Geoinformat Z GIS Schillerstr 30 A-5020 Salzburg Austria Salzburg Univ Appl Sci Dept Informat Technol & Syst Management Salzburg Austria

object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects' properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).

关键词： agent-based image analysis agent-based systems remote sensing object-based image analysis autonomous systems automation of image analysis

来源：评论

学校读者我要写书评

暂无评论

object-based Dune analysis: Automated dune mapping and pattern characterization for Ganges Chasma and Gale crater, Mars

引用

GEOMORPHOLOGY 2015年 250卷 128-139页

作者： Vaz, David A. Sarmento, Pedro T. K. Barata, Maria T. Fenton, Lori K. Michaels, Timothy I. Univ Coimbra Observ Geofis & Astron Ctr Earth & Space Res P-3040004 Coimbra Portugal Inst Super Tecn Ctr Nat Resources & Environm CERENA P-1049011 Lisbon Portugal Carl Sagan Ctr SETI Inst Mountain View CA USA

A method that enables the automated mapping and characterization of dune fields on Mars is described. Using CTX image mosaics, the introduced object-based Dune analysis (OBDA) technique produces an objective and reproducible mapping of dune morphologies over extensive areas. The data set thus obtained integrates a large variety of data, allowing a simple cross-analysis of dune patterns, spectral and morphometric information, and mesoscale wind models. Two dune fields, located in Gale crater and Ganges Chasma, were used to test and validate the methodology. The segmentation of dune-related morphologies is highly efficient, reaching overall accuracies of 95%. In addition, we show that the automated segmentation of slipface traces is also possible with expected accuracies of 85-90%. A qualitative and quantitative comparison of the final outputs with photointerpretations is performed, and the precision of the directional characterization of the dune patterns is evaluated. We demonstrate a good agreement between the OBDA outputs and the photointerpreted dune morphologies, with local trend deviations below 450 for 80-95% of the mapped areas. Because the developed algorithm is tuned for the recognition of linear features from the imagery, the slipfaces of small barchans can be preferentially overlooked owing to their small extent at the spatial resolution of the CTX mosaics. Dune types composed of longer linear morphologies are much better represented, including correct mapping of secondary structures. Having proved the effectiveness and accuracy of the mapping procedure, we discuss its future applications for the improvement of dune catalogs on Mars. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Dune mapping object-based image analysis Pattern recognition Mars surface

来源：评论

学校读者我要写书评

暂无评论

ASSESSMENT OF SUPERVISED METHODS FOR MAPPING RAINFALL INDUCED LANDSLIDES IN VHR imageS

ASSESSMENT OF SUPERVISED METHODS FOR MAPPING RAINFALL INDUCE...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Heleno, Sandra Silveira, Margarida Matias, Magda Pina, Pedro Univ Lisbon Inst Super Tecn CERENA P-1699 Lisbon Portugal Univ Lisbon Inst Super Tecn ISR P-1699 Lisbon Portugal

ISBN: (纸本)9781479979295

In this work we develop and compare three different supervised approaches for semi-automatic mapping of landslides, including the separation of landslide source and transport areas, using a single GeoEye-1 image acquired after a rainfall-induced landslide event in Madeira Island. The methodologies cover object-based classification using support vector machine (SVM) algorithms;pixel-based classification using textons;and object-based classification with a rule-set framework. The assessment was made by comparison of the results obtained in the validation areas with the ground-truth landslide mapping. In what concerns landslide recognition, the results of the object-based and pixel-based machine-learning approaches have higher accuracy when compared with the rule-set method. The object-based SVM approach achieves false positive rate FPR=20% and false negative rate FNR=18% for landslide area detection, while the pixel-based texton method displays even higher accuracy (FPR=19% and FNR=9%) although at higher computational cost and slower execution. In what concerns internal mapping of landslide source areas, the three methods show lower but still reasonably good performance, in particular in the sunnier east-facing slopes.

关键词： Landslide object-based image analysis Textons VHR Optical imagery Madeira Island

来源：评论

学校读者我要写书评

暂无评论

SUPERPIXEL-based SEGMENTATION OF REMOTE SENSING imageS THROUGH CORRELATION CLUSTERING

SUPERPIXEL-BASED SEGMENTATION OF REMOTE SENSING IMAGES THROU...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Masi, Giuseppe Gaetano, Raffaele Poggi, Giovanni Scarpa, Giuseppe Univ Naples Federico II DIETI Naples Italy

ISBN: (纸本)9781479979295

In this paper a new object-oriented segmentation method for high-resolution remote sensing images is proposed. To limit computational complexity, a preliminary superpixel representation of the image is obtained by means of a suitable watershed transform. Then, a region adjacency graph is associated with the superpixels, with edge weights accounting for region similarity/dissimilarity. The final segmentation is then obtained by means of a graph-cutting approach, following a correlation clustering formulation. The optimal cut can be obtained by solving a Integer Linear Programming (ILP) problem, whose complexity, however, grows rapidly with the image size. Much faster near-optimal solutions are obtained, here, with a greedy solution. Experiments on a real-world high-resolution remote sensing image prove the potential of the approach.

关键词： object-based image analysis superpixel segmentation graph partitioning correlation clustering integer linear programming

来源：评论

学校读者我要写书评

暂无评论

Automatic Detection of Clouds from Aerial Photographs of Snowy Volcanoes 7

Automatic Detection of Clouds from Aerial Photographs of Sno...

引用

7th Mexican Conference on Pattern Recognition (MCPR)

作者： Chang, Carolina Vaca, Fernando Univ Simon Bolivar Grp Inteligencia Artificial Caracas Venezuela Inst Geog Militar Quito Ecuador

ISBN: (纸本)9783319192642;9783319192635

We propose a method for cloud detection from RGB aerial photographs of snow-capped volcanoes of Ecuador. For cartography purposes, clouds are undesired objects that occlude the terrain, while snow-covered areas are valid regions of a map. The traditional approach of image thresholding does not suffice when snowy areas cannot be dismissed from the image in advanced. We combine image thresholding with region growing and neural networks classification to detect clouds at the object level. We show that there is overlap at the pixel level of clouds and snow. At the classification task a fuzzy ARTMAP neural net achieves 91.4% of success in fast learning mode and 95.5% of success in slow learning mode at the same vigilance level, for 32x32 pixel images. Incremental learning is achieved at a loss of 0.4% of the network performance.

关键词： Cloud detection object-based image analysis Fuzzy artmap neural network

来源：评论

学校读者我要写书评

暂无评论

Impact of spatial resolution on correlation between segmentation evaluation metrics and forest classification accuracy 21

Impact of spatial resolution on correlation between segmenta...

引用

Conference on image and Signal Processing for Remote Sensing XXI

作者： Lenarcic, Andreja Svab Ritlop, Klemen Duric, Natasa Cotar, Klemen Ostir, Kristof Slovenian Ctr Excellence Space Sci & Technol SI-1000 Ljubljana Slovenia Univ Ljubljana Fac Civil & Geodet Engn SI-1000 Ljubljana Slovenia Slovenian Acad Sci & Arts Res Ctr SI-1000 Ljubljana Slovenia

ISBN: (纸本)9781628418538

Slovenia is one of the most forested countries in Europe. Its forest management authorities need information about the forest extent and state, as their responsibility lies in forest observation and preservation. Together with appropriate geographic information system mapping methods the remotely sensed data represent essential tool for an effective and sustainable forest management. Despite the large data availability, suitable mapping methods still present big challenge in terms of their speed which is often affected by the huge amount of data. The speed of the classification method could be maximised, if each of the steps in object-based classification was automated. However, automation is hard to achieve, since segmentation requires choosing optimum parameter values for optimal classification results. This paper focuses on the analysis of segmentation and classification performance and their correlation in a range of segmentation parameter values applied in the segmentation step. In order to find out which spatial resolution is still suitable for forest classification, forest classification accuracies obtained by using four images with different spatial resolutions were compared. Results of this study indicate that all high or very high spatial resolutions are suitable for optimal forest segmentation and classification, as long as appropriate scale and merge parameters combinations are used in the object-based classification. If computation interval includes all segmentation parameter combinations, all segmentation-classification correlations are spatial resolution independent and are generally high. If computation interval includes over- or optimal-segmentation parameter combinations, most segmentation-classification correlations are spatial resolution dependent.

关键词： object-based image analysis spatial resolution land cover segmentation quality segmentation quality measures classification accuracy assessment correlation forest

来源：评论

学校读者我要写书评

暂无评论

Fuzzy segmentation for geographic object-based image analysis

Fuzzy segmentation for geographic object-based image analysi...

引用

SPIE Conference on Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX

作者： Lizarazo, Ivan Elsner, Paul Univ Dist Francisco Jose de Caldas Carrera 7 40-53 Bogota Colombia Univ London Birkbeck Coll London WC1E 7HX England

ISBN: (纸本)9780819477835

image segmentation partitions remote sensing images into image objects before assigning them to categorical land cover classes. Current segmentation methods require users to invest considerable time and effort in the search for meaningful image objects. As an alternative method we propose 'fuzzy' segmentation that offers more flexibility in dealing with remote sensing uncertainty. In the proposed method, original bands are processed using regression techniques to output fuzzy image regions which express degrees of membership to target land cover classes. Contextual properties of fuzzy regions can be measured to indicate potential spectral confusion. A 'defuzzification' process is subsequently conducted to produce the categorical land cover classes. This method was tested using data sets of both high and medium spatial resolution. The results indicate that this approach is able to produce classification with satisfying accuracy and requires very little user interaction.

关键词： image segmentation object-based image analysis land cover classification

来源：评论

学校读者我要写书评

暂无评论

Estimating Pinyon and Juniper Cover Across Utah Using NAIP imagery

Estimating Pinyon and Juniper Cover Across Utah Using NAIP I...

引用

作者： Roundy, Darrell B. Brigham Young University

学位级别：M.Sc.

Expansion of Pinus L. (pinyon) and Juniperus L. (juniper) (P-J) trees into sagebrush (Artemisia L.) steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA) software (Feature Analyst: TM for ArcMap 10.1®, ENVI Feature Extraction®, and Trimble eCognition Developer 8.2®) to extract tree canopy cover using NAIP (National Agricultural imagery Program) imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point) on 309 subplots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was > 45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature Analyst, and 0.92 for eCognition). Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point method (r = 0.85 for ENVI, 0.83 for Feature Analyst, and 0.83 for eCognition). Results from this study suggest that OBIA techniques may be used to extract P-J tree canopy cover accurately and inexpensively. All software packages accurately evaluated accurately extracted P-J canopy cover from NAIP imagery when imagery was not blurred and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel's oak), which are shrubs with similar spectral values as P-J.

关键词： object-based image analysis canopy cover National Agriculture imagery Program eCognition Feature Analyst ENVI Feature Extraction

来源：评论

学校读者我要写书评

暂无评论

object-based image analysis for coal fire-related land cover mapping in coal mining areas

引用

Geocarto International 2009年第1期24卷 25-36页

作者： Gao, Yan Kerle, Norman Mas, Jean-F. Centro de Investigaciones en Geografia Ambiental Universidad Nacional Autónoma de México (UNAM) Morelia Michoacán Mexico International Institute for Geo-Information Science and Earth Observation (ITC) Enschede Netherlands

Coal fires worldwide are causing enormous economic loss and environmental pollution, prompting a need to develop the methodology to map, analyse, and monitor them. Coal fire-related land cover mapping yields the knowledge of the distribution of coal, which is crucial for the delineation of potential coal fires and coal fire risk areas. The conventional pixel-based method does not make use of spatial relationships, whereas object-based image analysis (OBIA) has the potential to consider not only objects' spectral but spatial information, in terms of size, shape, texture and relations to other image objects. This article reports on coal fire-related land cover mapping with OBIA using multi-resolution segmentation and hierarchical classification. Both spectral and spatial information were used in the classification. It yielded an overall accuracy of 12.3% higher than that by the pixel-based method (70.67%), and the difference is significant (p = 0.05) by McNemar's test.

关键词： Coal mining area Hierarchical network classification Land cover classification object-based image analysis Pixel-based image analysis

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：