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3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA)

作者： Li, Dan Ke, Yinghai Gong, Huili Chen, Beibei Zhu, Lin Capital Normal Univ Base State Key Lab Urban Environm Proc & Digital Beijing Peoples R China

ISBN: (纸本)9781479941841

Urban vegetation, particularly trees, plays important roles in the urban ecosystems. In this study, we examined the potential of World View-2 imagery (acquired on September 14, 2012) for urban tree species classification in the capital city of Beijing, China. Four tree species including Chinese white poplar(Populus tomentosa Carriere),Chineses scholartree(Sophora Japonica), Gingko(Ginkgo biloba L.)and Paulownia(Paulownia Sieb.) were identified. To evaluate the impact of complex urban environment on urban tree species classification, we compared classification accuracies on shadow-removed image and shadow-recovered image. object-based hierarchical approach was used to detect shadow/non-shadow area and vegetated/non-vegetated area. Support-Vector-Machine method was used to classify tree species in vegetated area using object-based approach. We used Linear Correlation Correction(LCC) method to restore spectral information under shadowed area. The results show that tree species classification for shadow-removed imagery obtained overall accuracy of 80.97% and kappa value of 0.7286, while the accuracy and kappa value decreased to 76.88% and 0.6693when shadow-recovered image was used. In future research, we will explore the method to improve the accuracy of classification for shadow-recovered image.

关键词： urban tree species WorldView-2 shadow recovery object-based classification

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Možnosti objektově-orientované klasifikace pro určování vybraných biotopů nad horní hranicí lesa v Krkonošském národním parku.

Možnosti objektově-orientované klasifikace pro určován...

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作者： Jakešová, Lucie Charles University of Prague

Possibilities of object-based classification for selected biotopes above tree-line in the Krkonoše Mts. National Park detection Abstract The bachelor thesis is focused on the object-based classification of vegetation above the tree-line in the Krkonoše Mts. National Park using orthophoto with near infrared band and spatial resolution of 12.5 cm. Orthophoto was acquired in summer 2012. The classification legend was compiled by botanist of the national park. Software ENVI 5.1 was used for object-based classification using the field data. It provides two approaches to classification - Example-based and Rule-based. The overall accuracy of the best classification result was 75.97 % for 13 classes. Keywords: object-based classification, KRNAP, biotopes above tree-line, aerial optical scanner

关键词： object-based classification KRNAP biotopes above tree-line aerial optical scanner

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The possibilities to support ZB GIS®database update using object-based image analysis in ecognition developer software

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Geodezija ir Kartografija 2014年第3期40卷 122-132页

The process of updating a spatial database is a necessary part of database management in order to keep the stored information of an acceptable quality. The first step of database update requires change detection. Many methods have been suggested to detect changes, mostly pixel-based. Recently, with the spread of very high resolution images and object-based image analysis, object-based methods were developed, too. This article presents object-based change detection method for update of the vector database ZB GIS®, that is a geometric base of the Slovak National infrastructure of spatial information, using orthophoto of the area of interest. The method stages include the following: segmentation of orthophoto using the geometry of database objects and further according to spectral and spatial information, classification according to ZB GIS®, defining reclassification rules between two classes. The proposed method was tested in two localities – Malženice (agricultural landscape) and Chopok-Jasná (mountainous landscape) reaching the overall accuracy of classification 87.12% and 84.55%, respectively. The main limitation of the method is that it can be applied only for polygonal objects.

关键词： remote sensing change detection object-based image analysis object-based classification spatial database database update

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Cultivated land information extraction from high-resolution unmanned aerial vehicle imagery data

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JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷 083673-083673页

作者： Ma, Lei Cheng, Liang Han, Wenquan Zhong, Lishan Li, Manchun Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing 210097 Jiangsu Peoples R China Nanjing Univ Collaborat Innovat Ctr South China Sea Studies Nanjing 210097 Jiangsu Peoples R China Mapping & Geotechn Invest Co Ltd Nanjing Inst Surveying Nanjing 210019 Jiangsu Peoples R China

The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. Simultaneously, unmanned aerial vehicles (UAVs) have been increasingly used for natural resource applications in recent years as a result of their greater availability, the miniaturization of sensors, and the ability to deploy UAVs relatively quickly and repeatedly at low altitudes. We examine the potential of utilizing a small UAV for the characterization, assessment, and monitoring of cultivated land. Because most UAV images lack spectral information, we propose a novel cultivated land information extraction method based on a triangulation for cultivated land information extraction (TCLE) method. Thus, the information on more spatial properties of a region is incorporated into the classification process. The TCLE comprises three main steps: image segmentation, triangulation construction, and triangulation clustering using AUTOCLUST. Experiments were conducted on three UAV images in Deyang, China, using TCLE and eCognition for cultivated land information extraction (ECLE). Experimental results show that TCLE, which does not require training samples and has a much higher level of automation, can obtain accuracies equivalent to ECLE. Comparing with ECLE, TCLE also extracts coherent cultivated land with much less noise. As such, cultivated land information extraction based on high-resolution UAV images can be effectively and efficiently conducted using the proposed method. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： unmanned aerial vehicle AUTOCLUST cultivated land object-based classification Voronoi diagram

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OBIA based hierarchical image classification for industrial lake water

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SCIENCE OF THE TOTAL ENVIRONMENT 2014年第1期487卷 565-573页

作者： Avci, Z. D. Uca Karaman, M. Ozelkan, E. Kumral, M. Budakoglu, M. Istanbul Tech Univ Dept Astronaut Engn Fac Aeronaut & Astronaut TR-80626 Istanbul Turkey Istanbul Tech Univ Geochem Res Grp TR-80626 Istanbul Turkey Istanbul Tech Univ JAL Labs Dept Geol Engn Fac Mines TR-80626 Istanbul Turkey Istanbul Tech Univ Agr & Environm Informat Res Ctr TR-80626 Istanbul Turkey

Water management is very important in water mining regions for the sustainability of the natural environment and for industrial activities. This study focused on Acigol Lake, which is an important wetland for sodium sulphate (Na2SO4) production, a significant natural protection area and habitat for local bird species and endemic species of this saline environment, and a stopover for migrating flamingos. By a hierarchical classification method, ponds representing the industrial part were classified according to in-situ measured Baume values, and lake water representing the natural part was classified according to in-situ measurements of water depth. The latter is directly related to the water level, which should not exceed a critical level determined by the regulatory authorities. The resulting data, produced at an accuracy of around 80%, illustrates the status in two main regions for a single date. The output of the analysis may be meaningful for firms and environmental researchers, and authorizations can provide a good perspective for decision making for sustainable resource management in the region which has uncommon and specific ecological characteristics. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Industrial lake Baume Hierarchical classification object-based classification ASTER

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object-based classification as an alternative approach to the traditional pixel-based classification to identify potential habitat of the Grasshopper Sparrow

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ENVIRONMENTAL MANAGEMENT 2008年第1期41卷 20-31页

作者： Jobin, Benoit Labrecque, Sandra Grenier, Marcelle Falardeau, Gilles Canadian Wildlife Serv Ste Foy PQ G1V 4H5 Canada

The traditional method of identifying wildlife habitat distribution over large regions consists of pixel-based classification of satellite images into a suite of habitat classes used to select suitable habitat patches. object-based classification is a new method that can achieve the same objective based on the segmentation of spectral bands of the image creating homogeneous polygons with regard to spatial or spectral characteristics. The segmentation algorithm does not solely rely on the single pixel value, but also on shape, texture, and pixel spatial continuity. The object-based classification is a knowledge base process where an interpretation key is developed using ground control points and objects are assigned to specific classes according to threshold values of determined spectral and/or spatial attributes. We developed a model using the eCognition software to identify suitable habitats for the Grasshopper Sparrow, a rare and declining species found in southwestern Quebec. The model was developed in a region with known breeding sites and applied on other images covering adjacent regions where potential breeding habitats may be present. We were successful in locating potential habitats in areas where dairy farming prevailed but failed in an adjacent region covered by a distinct Landsat scene and dominated by annual crops. We discuss the added value of this method, such as the possibility to use the contextual information associated to objects and the ability to eliminate unsuitable areas in the segmentation and land cover classification processes, as well as technical and logistical constraints. A series of recommendations on the use of this method and on conservation issues of Grasshopper Sparrow habitat is also provided.

关键词： ammodramus savannarum Grasshopper Sparrow object-based classification pixel-based classification Quebec segmentation

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The Attribute Accuracy Assessment of Land Cover Data in the National Geographic Conditions Survey

The Attribute Accuracy Assessment of Land Cover Data in the ...

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“地理空间数据库与位置服务”国际学术会议

作者： Xiaole Ji Xiao Niu Shandong Provincial Institute of Land Surveying and Mapping Jinan Shandong ProvinceChina

With the widespread national survey of geographic conditions,object-based data has already became the most common data organization pattern in the area of land cover *** the accuracy of object-based land cover data is related to lots of processes of data production,such like the efficiency of inside production and the quality of final land cover ***,there are a great deal of requirements of accuracy assessment of object-based classification *** approaches for accuracy assessment in surveying and mapping are not aimed at land cover *** is necessary to employ the accuracy assessment in imagery *** traditional pixel-based accuracy assessing methods are inadequate for the *** measures we improved are based on error matrix and using objects as sample units,because the pixel sample units are not suitable for assessing the accuracy of object-based classification *** to pixel samples,we realize that the uniformity of object samples has *** order to make the indexes generating from error matrix reliable,we using the areas of object samples as the weight to establish the error matrix of object-based image classification *** compare the result of two error matrixes setting up by the number of object samples and the sum of area of object *** error matrix using the sum of area of object sample is proved to be an intuitive,useful technique for reflecting the actual accuracy of object-based imagery classification result.

关键词： Land cover Accuracy assessment National geographic survey Error matrix object-based classification Weight High-resolution remotely sensed imagery

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Satellite based calculation of spatially distributed crop water requirements for cotton and wheat cultivation in Fergana Valley, Uzbekistan

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GLOBAL AND PLANETARY CHANGE 2013年第parta期110卷 88-98页

作者： Conrad, Christopher Rahmann, Maren Machwitz, Miriam Stulina, Galina Paeth, Heiko Dech, Stefan Univ Wurzburg Inst Geog & Geol Wurzburg Germany Ctr Rech Publ Gabriel Lippmann Luxembourg Luxembourg Interstate Commiss Water Coordinat Cent Asia SIC Sci Informat Ctr Tashkent Uzbekistan

This study focuses on the generation of reliable data for improving land and water use in Central Asia. An object-based remote sensing classification is applied and combined with the CropWat model developed by the Food and Agriculture Organization (FAO) to determine crop distribution and water requirements for irrigation of cotton and winter-wheat in Fergana Valley, Uzbekistan. The crop classification is conducted on RapidEye and Landsat data acquired before the onset of the main summer irrigation phases in July using a random forest algorithm. The ClimWat database of FAO is utilized for calculating crop water requirements (CWR) and crop irrigation requirements (CIR). classification reveals an overall accuracy of 86.2% and exceeds a producer's (user's) accuracy of 95% (89%) for both, cotton and wheat. In 2010, cotton and winter-wheat are planted on 66.7% of the agricultural area under investigation, whereas orchard areas amount to 15.5%. The CWR modelled for winter-wheat and cotton cultivation revealed 5443 m(3) ha(-1) and 9278 m(3) ha(-1), respectively. Subtracting effective precipitation leads to OR of 4133 m(3) ha(-1) and 8813 m(3) ha(-1). Comparisons of CWR and CIR for the area dominating crops with the total of water officially allocated for irrigation underline the pressure on the water resources in the entire Syr Darya catchment and suggest modifications of the cropping system towards more winter crops. The early season crop maps can be used for water saving as they enable modifications of water allocation plans within the different irrigation subsystems of the valley. The method for mapping spatially distributed CWR and CIR can be transferred to other irrigated areas in Central Asia and beyond. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Fergana Valley object-based classification Crop water requirement Water management Remote sensing RapidEye

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Multi-Sensor Monitoring System for Forest Cover Change Assessment in Central Africa

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2013年第1期6卷 110-120页

作者： Desclee, Baudouin Simonetti, Dario Mayaux, Philippe Achard, Frederic Commiss European Communities Joint Res Ctr Inst Environm & Sustainabil I-21020 Ispra Italy Arcadia SIT I-21020 Ispra Italy

Forest monitoring from earth observation is crucial over tropical regions to assess forest extent and provide up-to-date estimates of deforestation rates. based on a systematic sample of 20x20 km size sites, a processing chain has been developed at the European Commission's Joint Research Centre (JRC) for producing deforestation estimates between years 1990, 2000 and 2005. Whereas this monitoring exercise was based on Landsat imagery, limitations in Landsat availability over Central Africa for year 2010 required alternative imagery such as the Disaster Monitoring Constellation (DMC). The classification module of the existing JRC processing chain is based on tasseled caps analysis (TCap-based). We adapted this module to DMC imagery by selecting the most suitable object-features through their assessments using a sub-sample of existing land-cover maps of years 1990 and 2000. The processing chain is adapted for the production of land-cover change maps between years 2000 and 2010. The accuracy of the land-cover maps produced for year 2010 with the two methods (original TCap-based and adapted Multi-Sensor) is assessed through a reference dataset. Overall accuracies are similar for both approaches (93% and 95% respectively), but the Multi-Sensor approach shows a significant improvement when considering only changed objects (83% overall accuracy versus 56% for TCap-based). Our results show that, even by using DMC imagery with lower radiometric quality (compared to Landsat) an automated classification can provide land-cover maps with similar accuracy thanks to an appropriate object-features selection. Similar adaptations need to be developed for other satellite imagery such as SPOT and RapidEye.

关键词： Change detection DMC deforestation forest mapping and monitoring Landsat TM object-based classification multi-temporal analysis remote sensing

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SVM-based classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

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REMOTE SENSING 2013年第8期5卷 3749-3775页

作者： Zhang, Jixian Lin, Xiangguo Ning, Xiaogang Chinese Acad Surveying & Mapping Key Lab Mapping Space Beijing 100830 Peoples R China

object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications.

关键词： airborne LiDAR object-based classification point clouds segmentation SVM

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