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3rd International Conference on Remote Sensing and Geoinformation of the Environment (RSCy)

作者： Dragozi, E. Gitas, Ioannis Z. Stavrakoudis, Dimitris G. Minakou, C. Aristotle Univ Thessaloniki Lab Forest Management & Remote Sensing GR-54006 Thessaloniki Greece

ISBN: (纸本)9781628417005

Forest fires greatly influence the stability and functions of the forest ecosystems. The ever increasing need for accurate and detailed information regarding post-fire effects (burn severity) has led to several studies on the matter. In this study the combined use of Very High Resolution (VHR) satellite data (GeoEye), object-based image analysis (OBIA) and Composite Burn Index (CBI) measurements in estimating burn severity, at two different time points (2011 and 2012) is assessed. The accuracy of the produced maps was assessed and changes in burn severity between the two dates were detected using the post classification comparison approach. It was found that the produced burn severity map for 2011 was approximately 10% more accurate than that of 2012. This was mainly attributed to the increased heterogeneity of the study area in the second year, which led to an increased number of mixed class objects and consequently made it more difficult to spectrally discriminate between the severity classes. Following the post-classification analysis, the severity class changes were mainly attributed to the trees' ability to survive severe fire damage and sprout new leaves. Moreover, the results of the study suggest that when classifying CBI-based burn severity using VHR imagery it would be preferable to use images captured soon after the fire.

关键词： Burn severity object-based image analysis Composite Burn Index GeoEye

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A NEW FRAMEWORK FOR object-based image analysis based ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER

A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEG...

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International Conference on Sensors and Models in Remote Sensing and Photogrammetry

作者： Hadavand, A. Saadatseresht, M. Homayouni, S. Univ Tehran Sch Surveying & Geospatial Informat Engn Tehran 14174 Iran Univ Ottawa Dept Geog Environm Studies & Geomat Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9781629935201

In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS), a hierarchy of image objects. Optimization of SSS, respect to NDVI and DSM values in each super object is used to get the best scale in local regions of image scene. Optimized SSS segmentations are finally classified to produce the final land cover map. Very high resolution aerial image and digital surface model provided by ISPRS 2D semantic labelling dataset is used in our experiments. The result of our proposed method is comparable to those of ESP tool, a well-known method to estimate the scale of segmentation, and marginally improved the overall accuracy of classification from 79% to 80%.

关键词： Segmentation Scale parameter object-based image analysis Land Cover Classification

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Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images

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JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷

作者： Hamedianfar, Alireza Shafri, Helmi Zulhaidi Mohd Mansor, Shattri Ahmad, Noordin Univ Putra Malaysia Dept Civil Engn Fac Engn Serdang 43400 Selangor Malaysia Univ Putra Malaysia GISRC Fac Engn Serdang 43400 Selangor Malaysia Natl Space Agcy Bangunan Komersil PJH Putrajaya 62570 Malaysia

image classification of roofing types, road pavements, and natural features can assist land-cover maps in further examining the effects of such features on health, pollution, and the microclimate in urban settings. Airborne hyperspectral sensors with high spectral and spatial resolutions can be employed for detailed characterization of urban areas. This study aims to develop a procedure that is instrumental for automated knowledge discovery and mapping of urban surface materials from a large feature space of hyperspectral images. Two different images over Universiti Putra Malaysia (UPM) and Kuala Lumpur (KL), Malaysia, were captured by using hyperspectral sensors with 20 and 128 bands. The images were used to explore the combined performance of a data mining (DM) algorithm and object-based image analysis (OBIA). A large number of attributes were discovered with the C4.5 DM algorithm, which also generated the classification model as a decision tree. The UPM and KL classified images achieved 93.42 and 88.36% overall accuracy. The high accuracy of object-based classification can be linked to the knowledge discovery produced by the DM algorithm. This algorithm increased the productivity of OBIA, expedited the process of attribute selection, and resulted in an easy-to-use representation of a knowledge model from a decision tree structure. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： data mining object-based image analysis urban area hyperspectral imagery urban mapping

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Monitoring of Oil Exploitation Infrastructure by Combining Unsupervised Pixel-based Classification of Polarimetric SAR and object-based image analysis

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REMOTE SENSING 2014年第12期6卷 11977-12004页

作者： Plank, Simon Mager, Alexander Schoepfer, Elisabeth German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD D-82234 Oberpfaffenhofen Germany

In developing countries, there is a high correlation between the dependence of oil exports and violent conflicts. Furthermore, even in countries which experienced a peaceful development of their oil industry, land use and environmental issues occur. Therefore, independent monitoring of oil field infrastructure may support problem solving. Earth observation data enables fast monitoring of large areas which allows comparing the real amount of land used by the oil exploitation and the companies' contractual obligations. The target feature of this monitoring is the infrastructure of the oil exploitation, oil well pads-rectangular features of bare land covering an area of approximately 50-60 m x 100 m. This article presents an automated feature extraction procedure based on the combination of a pixel-based unsupervised classification of polarimetric synthetic aperture radar data (PolSAR) and an object-based post-classification. The method is developed and tested using dual-polarimetric TerraSAR-X imagery acquired over the Doba basin in south Chad. The advantages of PolSAR are independence of the cloud coverage (vs. optical imagery) and the possibility of detailed land use classification (vs. single-pol SAR). The PolSAR classification uses the polarimetric Wishart probability density function based on the anisotropy/entropy/alpha decomposition. The object-based post-classification refinement, based on properties of the feature targets such as shape and area, increases the user's accuracy of the methodology by an order of a magnitude. The final achieved user's and producer's accuracy is 59%-71% in each case (area based accuracy assessment). Considering only the numbers of correctly/falsely detected oil well pads, the user's and producer's accuracies increase to even 74%-89%. In an iterative training procedure the best suited polarimetric speckle filter and processing parameters of the developed feature extraction procedure are determined. The high transferability of the me

关键词： SAR polarimetry object-based image analysis feature extraction natural resources monitoring oil exploitation

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An object-based image analysis of Pinyon and Juniper Woodlands Treated to Reduce Fuels

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ENVIRONMENTAL MANAGEMENT 2014年第3期53卷 660-671页

作者： Hulet, April Roundy, Bruce A. Petersen, Steven L. Jensen, Ryan R. Bunting, Stephen C. ARS USDA Eastern Oregon Agr Res Ctr Burns OR 97720 USA Brigham Young Univ Provo UT 84602 USA Brigham Young Univ Dept Geog Provo UT 84602 USA Univ Idaho Dept Rangeland Ecol & Management Moscow ID 83843 USA

Mechanical and prescribed fire treatments are commonly used to reduce fuel loads and maintain or restore sagebrush steppe rangelands across the Great Basin where pinyon (Pinus) and juniper (Juniperus) trees are encroaching and infilling. Geospatial technologies, particularly remote sensing, could potentially be used in these ecosystems to (1) evaluate the longevity of fuel reduction treatments, (2) provide data for planning and designing future fuel-reduction treatments, and (3) assess the spatial distribution of horizontal fuel structure following fuel-reduction treatments. High-spatial resolution color-infrared imagery (0.06-m pixels) was acquired for pinyon and juniper woodland plots where fuels were reduced by either prescribed fire, tree cutting, or mastication at five sites in Oregon, California, Nevada, and Utah. imagery was taken with a Vexcel UltraCam X digital camera in June 2009. Within each treatment plot, ground cover was measured as part of the Sagebrush Steppe Treatment Evaluation Project. Trimble eCognition Developer was used to classify land cover classes using object-based image analysis (OBIA) techniques. Differences between cover estimates using OBIA and ground-measurements were not consistently higher or lower for any land cover class and when evaluated for individual sites, were within +/- 5 % of each other. The overall accuracy and the K (hat) statistic for classified thematic maps for each treatment were: prescribed burn 85 % and 0.81;cut and fell 82 % and 0.77, and mastication 84 % and 0.80. Although cover assessments from OBIA differed somewhat from ground measurements, they are sufficiently accurate to evaluate treatment success and for supporting a broad range of management concerns.

关键词： eCognition object-based image analysis Prescribed burn Mastication Pinyon-juniper woodlands Restoration

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Estimating fractional land cover in semi-arid central Kalahari: the impact of mapping method (spectral unmixing vs. object-based image analysis) and vegetation morphology

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GEOCARTO INTERNATIONAL 2014年第8期29卷 860-877页

作者： Mishra, Niti B. Crews, Kelley A. Univ Texas Austin Dept Geog & Environm Austin TX 78712 USA

Focusing on the central Kalahari, this study utilized fractional cover of photosynthetic vegetation (f(PV)), non-photosynthetic vegetation (f(NPV)) and bare soil (f(BS)), derived in situ and estimated from GeoEye-1 imagery using Multiple End-member Spectral Mixture analysis (MESMA) and object-based image analysis (OBIA) to determine superior method for fractional cover estimation and the impact of vegetation morphology on the estimation accuracy. MESMA mapped fractional cover by testing endmember models of varying complexity. based on OBIA, image was segmented at five segmentation scales followed by classification. MESMA provided more accurate fractional cover estimates than OBIA. The increasing segmentation scale in OBIA resulted in a consistent increase in error. Different vegetation morphology types showed varied responses to the changing segmentation scale, reflecting their unique ecology and physiognomy. While areas under woody cover produced lower error even at coarse segmentation scales, those with herbaceous cover provided low error only at the fine segmentation scale.

关键词： semi-arid savanna fractional cover MESMA object-based image analysis GeoEye

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Detection of Gully-Affected Areas by Applying object-based image analysis (OBIA) in the Region of Taroudannt, Morocco

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REMOTE SENSING 2014年第9期6卷 8287-8309页

作者： d'Oleire-Oltmanns, Sebastian Marzolff, Irene Tiede, Dirk Blaschke, Thomas Salzburg Univ Dept Geoinformat Z GIS A-5020 Salzburg Austria Goethe Univ Frankfurt Dept Phys Geog Remote Sensing & GIS Res Grp D-60438 Frankfurt Germany

This study aims at the detection of gully-affected areas by applying object-based image analysis in the region of Taroudannt, Morocco, which is highly affected by gully erosion while simultaneously representing a major region of agro-industry with a high demand of arable land. As high-resolution optical satellite data are readily available from various sensors and with a much better temporal resolution than 3D terrain data, an area-wide mapping approach to extract gully-affected areas using only optical satellite imagery was developed. The methodology additionally incorporates expert knowledge and freely-available vector data in a cyclic object-based image analysis approach. This connects the two fields of geomorphology and remote sensing. The classification results show the successful implementation of the developed approach and allow conclusions on the current distribution of gullies. The results of the classification were checked against manually delineated reference data incorporating expert knowledge based on several field campaigns in the area, resulting in an overall classification accuracy of 62%. The error of omission accounts for 38% and the error of commission for 16%, respectively. Additionally, a manual assessment was carried out to assess the quality of the applied classification algorithm. The limited error of omission contributes with 23% to the overall error of omission and the limited error of commission contributes with 98% to the overall error of commission. This assessment improves the results and confirms the high quality of the developed approach for area-wide mapping of gully-affected areas in larger regions. In the field of landform mapping, the overall quality of the classification results is often assessed with more than one method to incorporate all aspects adequately.

关键词： object-based image analysis gully landform mapping gully-affected areas accuracy manual assessment optical satellite data

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An object-based image analysis for building seismic vulnerability assessment using high-resolution remote sensing imagery

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NATURAL HAZARDS 2014年第1期71卷 151-174页

作者： Wu, Hao Cheng, Zhiping Shi, Wenzhong Miao, Zelang Xu, Chenchen Wuhan Univ Technol Sch Resources & Environm Engn Wuhan 430070 Hubei Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Kowloon 999077 Hong Kong Peoples R China China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China

Building seismic vulnerability assessment plays an important role in formulating pre-disaster mitigation strategies for developing countries. The occurrence of high-resolution satellite sensors has greatly motivated it by providing a promising approach to obtain building information. However, this also brings a big challenge to the accurate building extraction and its coherent integration with the assessment model. The main objective of this paper is to investigate how to extract building attributes from high-resolution remote sensing imagery using the object-based image analysis (OBIA) method, so as to accurately and conveniently assess building seismic vulnerability by the combination of in situ field data. A general framework for the assessment of building seismic vulnerability is presented, including (1) the extraction of building information using OBIA, (2) building height estimation, and (3) the support vector machine (SVM)-based building seismic vulnerability assessment. Particularly, an integrated solution is proposed that merges the strengths of multiple spatial contextual relationships and some typical image object measures, under the unified framework to improve building information extraction at different scale levels as well as for different interest objects. With the aid of 35 building samples from two powerful earthquakes in China, the cloud-free WorldView-2 images and some building structure parameters from field survey were used to quantity the grades of building seismic vulnerability in Wuhan Optics Valley, China. The results show that all 48 buildings among the study area have been well detected with an overall accuracy of 80.67 % and the mean error of heights estimated from building shadow is less than 2 m. This indicates that the integrated analysis strategy based on OBIA is suitable for extracting the building information from high-resolution remote sensing imagery. Additionally, the assessment results using SVM show that the building seismic v

关键词： Building seismic vulnerability assessment object-based image analysis Building information extraction Building height estimation Remote sensing

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Cover Estimations Using object-based image analysis Rule Sets Developed Across Multiple Scales in Pinyon-Juniper Woodlands

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RANGELAND ECOLOGY & MANAGEMENT 2014年第3期67卷 318-327页

作者： Hulet, April Roundy, Bruce A. Petersen, Steven L. Jensen, Ryan R. Bunting, Stephen C. USDA ARS Eastern Oregon Agr Res Ctr Burns OR 97720 USA Brigham Young Univ Dept Plant & Wildlife Sci Provo UT 84602 USA Brigham Young Univ Dept Geog Provo UT 84602 USA Univ Idaho Dept Rangeland Ecol & Management Moscow ID 83844 USA

Numerous studies have been conducted that evaluate the utility of remote sensing for monitoring and assessing vegetation and ground cover to support land management decisions and complement ground measurements. However, few comparisons have been made that evaluate the utility of object-based image analysis (OBIA) to accurately classify a landscape where rule sets (models) have been developed at various scales. In this study, OBIA rule sets used to estimate land cover from high spatial resolution imagery (0.06-m pixel) on Pinus L. (pinyon) and Juniperus L. (juniper) woodlands were developed using eCognition Developer at four scales with varying grains-1) individual plot, 2) individual sites, 3) regions (western juniper vs. Utah juniper sites), and 4) pinyon-juniper woodland network (all plots) that were within the same study extent. Color-infrared imagery was acquired over five sites in Oregon, California, Nevada, and Utah with a Vexcel UltraCamX digital camera in June 2009. Ground cover measurements were also collected at study sites in 2009 on 80 0.1-ha plots. Correlations between OBIA and ground measurements were relatively high for individual plot and site rule sets (ranging from r=0.52 to r=0.98). Correlations for regional and network rule sets were lower (ranging from r=0.24 to r=0.63), which was expected due to radiance differences between the images as well as vegetation differences found at each site. All site and plot OBIA average cover percentage estimates for live trees, shrubs, perennial herbaceous vegetation, litter, and bare ground were within 5% of the ground measurements, and all region and network OBIA average cover percentage estimates were within 10%. The trade-off for decreased accuracy over a larger area (region and network rule sets) may be useful to prioritize management strategies but will unlikely capture subtle shifts in understory plant communities that site and plot rule sets often capture.

关键词： eCognition Developer high spatial resolution imagery object-based image analysis rule sets SageSTEP

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Evaluation of object-based image analysis techniques on very high-resolution satellite image for biomass estimation in a watershed of hilly forest of Nepal

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APPLIED GEOMATICS 2014年第1期6卷 59-68页

作者： Hussin, Yousif Ali Gilani, Hammad van Leeuwen, Louise Murthy, M. S. R. Shah, Rachna Baral, Srijana Tsendbazar, Nandin-Erdene Shrestha, Saurav Shah, Shyam Kumar Qamer, Faisal Mueen Univ Twente Fac Geo Informat Sci & Earth Observat NL-7500 AE Enschede Netherlands Int Ctr Integrated Mt Dev GPOB 3226Khumaltar Kathmandu Nepal Liaoning Tech Univ Sch Geomat Fuxing Liaoning Peoples R China Natl Trust Nat Conservat Cent Zoo Kathmandu Nepal Rupantaran Nepal Kathmandu Nepal Wageningen Univ & Res Ctr Lab Geo Informat Sci & Remote Sensing NL-6700 AA Wageningen Netherlands GoN UNDP CSUWN Kathmandu Nepal Dept Natl Pk & Wildlife Conservat Kathmandu Nepal

Methods of forest carbon estimation using remote-sensing data and techniques are evolving within a short timeframe as compared to traditional forest inventory methods. object-based image analysis (OBIA) provided new opportunities to improve biomass and carbon stock estimation and mapping by delineating and classifying crown projection area (CPA) of individual trees. In this paper, image segmentation techniques of OBIA (region growing and valley following) are being applied on GeoEye-1 satellite data and compared in terms of accuracy in Ludhikhola watershed in the Gorkha District of Nepal. Accuracy assessment of tree crown delineation of both segmentation approaches were analyzed using accuracymeasures of D and one-to-one (1:1) correspondence. The combination of over-segmentation and undersegmentation, D is interpreted as the "closeness" measure to an ideal segmentation result in relation to a predefined reference set. Region growing and valley following segmentation 68 and 58 % accuracy, respectively, was achieved and linear regression model was developed for carbon stock for Shorea robusta which resulted into a coefficient of determination value of 0.67 at 95 % confidence level and the coefficient of determination resulted into a value of 0.70 for other species. The research concluded that region growing approach showed better delineation as compared to valley follow approach, since it used both features of local maxima and local minima.

关键词： object-based image analysis Crow projection area Segmentation Tree crown delineation and linear regression model

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