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object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

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SENSORS 2008年第3期8卷 1613-1636页

作者： Zhou, Weiqi Troy, Austin Grove, Morgan Univ Vermont Rubenstein Sch Environm & Nat Resources George D Aiken Ctr Burlington VT 05405 USA US Forest Serv Northeastern Res Stn USDA S Burlington VT 05403 USA

Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the Gwynns Falls watershed from 1999 to 2004. The Gwynns Falls watershed includes portions of Baltimore City and Baltimore County, Maryland, USA. An object-based approach was first applied to implement the land cover classification separately for each of the two years. The overall accuracies of the classification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. Following the classification, we conducted a comparison of two different land cover change detection methods: traditional (i.e., pixel-based) post-classification comparison and object-based post-classification comparison. The results from our analyses indicated that an object-based approach provides a better means for change detection than a pixel based method because it provides an effective way to incorporate spatial information and expert knowledge into the change detection process. The overall accuracy of the change map produced by the object-based method was 90.0%, with Kappa statistic of 0.854, whereas the overall accuracy and Kappa statistic of that by the pixel-based method were 81.3% and 0.712, respectively.

关键词： object-based image analysis post-classification change detection high-spatial resolution image urban landscape Baltimore LTER

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Mapping Banana Plants from High Spatial Resolution Orthophotos to Facilitate Plant Health Assessment

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REMOTE SENSING 2014年第9期6卷 8261-8286页

作者： Johansen, Kasper Sohlbach, Malte Sullivan, Barry Stringer, Samantha Peasley, David Phinn, Stuart Univ Queensland Sch Geog Planning & Environm Management Biophys Remote Sensing Grp Brisbane Qld 4072 Australia Trimble Geospatial D-80636 Munich Germany Australian Banana Growers Council Inc South Gate East Commercial Ctr Rocklea Qld 4106 Australia Peasley Hort Serv Murwillumbah NSW 2484 Australia

The Banana Bunchy Top Virus (Genus: Babuvirus) reduces plant growth and prevents banana production. Because of the very large number of properties with banana plants in South East Queensland, Australia, a mapping approach was developed to delineate individual and clusters of banana plants to help plant identification and enable prioritization of plant inspections for Banana Bunchy Top Virus. Due to current outbreaks in South East Queensland, there are concerns that the virus may spread to the major banana growing districts further north. The mapping approach developed was based on very high spatial resolution airborne orthophotos. object-based image analysis was used to: (1) detect banana plants using edge and line detection approaches;(2) produce accurate and realistic outlines around classified banana plants;and (3) evaluate the mapping results. The mapping approach was developed based on 10 image tiles of 1 km x 1 km and was applied to orthophotos (3600 image tiles) from September 2011 covering the entire Sunshine Coast Region in South East Queensland. based on field inspections of the classified maps, a user's mapping accuracy of 88% (n = 146) was achieved. The results will facilitate the detection of banana plants and increase the inspection rate of Banana Bunchy Top Virus in the future.

关键词： banana plants Banana Bunchy Top Virus orthophotos high spatial resolution object-based image analysis eCognition

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Automated classification of debris-covered glaciers combining optical, SAR and topographic data in an object-based environment

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REMOTE SENSING OF ENVIRONMENT 2015年 170卷 372-387页

作者： Robson, Benjamin Aubrey Nuth, Christopher Dahl, Svein Olaf Hoelbling, Daniel Strozzi, Tazio Nielsen, Pal Ringkjob Univ Bergen Dept Geog N-5007 Bergen Norway Univ Oslo Dept Geosci N-0316 Oslo Norway Salzburg Univ Dept Geoinfomat GIS Z A-5020 Salzburg Austria GAMMA Remote Sensing CH-3073 Gumlingen Switzerland

Satellite imagery is increasingly used to monitor glacier area changes and create glacier inventories. Robust and efficient pixel-based band ratios have proven to be accurate for automatically delineating clean glacier ice, however such classifications are restricted on debris-covered ice due to its spectral similarity with surrounding terrain. object-based image analysis (OBIA) has emerged as a new analysis technique within remote sensing. It offers many advantages over pixel-based classification techniques due to the ability to work with multiple data sources and handle data contextually and hierarchically. By making use of OBIA capabilities we automatically classify dean ice and debris-covered ice in the challenging area surrounding Mount Manaslu in Nepal using optical (Landsat 8), topographic (void-filled SRTM) and SAR coherence (ALOS PALSAR) data. Clean ice was classified with a mean accuracy of 93% whilst debris-covered ice was classified with an accuracy of 83% when compared to manually corrected outlines, providing a total glacier accuracy of 91%. With further developments in the classification, steep tributary sections of ice could be contextually included, raising the accuracy to over 94%. One prominent advantage of OBIA is that it allows some post-processing and correction of the glacier outlines automatically, reducing the amount of manual correction needed. OBIA incorporating SAR coherence data is recommended for future mapping of debris-covered ice. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Debris-covered glacier object-based image analysis Landsat 8 SAR coherence Semi-automatic classification Himalayas

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Framework for susceptibility analysis of layered rock slopes considering the dimensions of the mapping units and geological data resolution at various map scales

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ENGINEERING GEOLOGY 2018年 246卷 310-325页

作者： Lin, Cheng-Han Lin, Ming-Lang Peng, Hou-Ren Lin, Hsi-Hung Natl Cheng Kung Univ Dept Civil Engn Tainan Taiwan Natl Taiwan Univ Dept Civil Engn Taipei Taiwan Cent Geol Survey MOEA Environm & Engn Geol Div Taipei Taiwan

In Taiwan, the failure of rock slopes has caused the catastrophic loss of lives and property. To understand the risks associated with rock slope failure, susceptibility analysis has been widely adopted as a fundamental task. However, currently, most susceptibility analyses have investigated only landslide occurrences and have been limited to the regional scale. In this case, susceptibility maps cannot provide detailed information about the rock slope stability as required by the engineers. To fill this gap, this study developed a methodological framework to analyze the susceptibility of layered rock slopes from regional to local scales. In each map scale, the appropriate size of the slope unit (SU) was produced as the mapping unit through the object-based image analysis (OBIA) method, and the susceptibility map was generated with an explicit purpose according to the resolution of geological data. Multiscale susceptibility analysis of layered rock slopes was performed in a metamorphic area in northern Taiwan. The performance of the susceptibility maps was validated by the field investigations and the data of the geologically sensitive area, which were released by the Central Geological Survey of Taiwan. The results show that different phases of the evaluation of rock slope stability were addressed, including the landslide activity and the rock slope characterization on the regional scale and the failure mechanisms on the local scale. In addition, suitable SUs can also be semiautomatically reproduced by the OBIA method with suggested strategies.

关键词： Layered rock slope Multiscale susceptibility analysis Slope unit Geological data resolution object-based image analysis

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Local and global evaluation for remote sensing image segmentation

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年第Aug.期130卷 256-276页

作者： Su, Tengfei Zhang, Shengwei Inner Mongolia Agr Univ Coll Water Conservancy & Civil Engn Hohhot 010018 Peoples R China

In object-based image analysis, how to produce accurate segmentation is usually a very important issue that needs to be solved before image classification or target recognition. The study for segmentation evaluation method is key to solving this issue. Almost all of the existent evaluation strategies only focus on the global performance assessment. However, these methods are ineffective for the situation that two segmentation results with very similar overall performance have very different local error distributions. To overcome this problem, this paper presents an approach that can both locally and globally quantify segmentation incorrectness. In doing so, region-overlapping metrics are utilized to quantify each reference geo-object's over and under-segmentation error. These quantified error values are used to produce segmentation error maps which have effective illustrative power to delineate local segmentation error patterns. The error values for all of the reference geo-objects are aggregated through using area weighted summation, so that global indicators can be derived. An experiment using two scenes of very different high resolution images showed that the global evaluation part of the proposed approach was almost as effective as other two global evaluation methods, and the local part was a useful complement to comparing different segmentation results. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： object-based image analysis Remote sensing image segmentation Local evaluation Region over-lapping

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An object-based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India

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REMOTE SENSING 2016年第6期8卷 479页

作者： Singha, Mrinal Wu, Bingfang Zhang, Miao Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Div Digital Agr Olymp Village Sci PkWest Beichen Rd Beijing 100101 Peoples R China

Rice is the staple food for half of the world's population. Therefore, accurate information of rice area is vital for food security. This study investigates the effect of phenology for rice mapping using an object-based image analysis (OBIA) approach. Crop phenology is combined with high spatial resolution multispectral data to accurately classify the rice. Phenology was used to capture the seasonal dynamics of the crops, while multispectral data provided the spatial variation patterns. Phenology was extracted from MODIS NDVI time series, and the distribution of rice was mapped from China's Environmental Satellite (HJ-1A/B) data. Classification results were evaluated by a confusion matrix using 100 sample points. The overall accuracy of the resulting map of rice area generated by both spectral and phenology is 93%. The results indicate that the use of phenology improved the overall classification accuracy from 2%-4%. The comparison between the estimated rice areas and the State's statistics shows underestimated values with a percentage difference of -34.53%. The results highlight the potential of the combined use of crop phenology and multispectral satellite data for accurate rice classification in a large area.

关键词： object-based image analysis phenology data fusion paddy rice classification

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Estimation of Seismic Vulnerability Levels of Urban Structures With Multisensor Remote Sensing

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2016年第5期9卷 1913-1936页

作者： Geiss, Christian Jilge, Marianne Lakes, Tobia Taubenboeck, Hannes German Remote Sensing Data Ctr DFD German Aerosp Ctr DLR D-82234 Oberpfaffenhofen Germany Humboldt Univ Dept Geog D-12489 Berlin Germany

The ongoing global transformation of human habitats from rural villages to ever growing urban agglomerations induces unprecedented seismic risks in earthquake prone regions. To mitigate affiliated perils requires the seismic assessment of built environments. Numerous studies emphasize that remote sensing can play a valuable role in supporting the extraction of relevant features for preevent vulnerability analysis. However, the majority of approaches operate on building level. This induces the deployment of very high spatial resolution remote sensing data, which hampers, nowadays, utilization capabilities for larger areas due to data costs and processing requirements. In this paper, we alter the spatial scale of analysis and propose concepts and methods to estimate the seismic vulnerability level of homogeneous urban structures. A procedure is designed, which comprises four main steps dedicated to: 1) delineation of urban structures by means of a tailored unsupervised data segmentation procedure with scale optimization;2) characterization of urban structures by a joint exploitation of multisensor data;3) selection of most feasible features under consideration of in situ vulnerability information;and 4) estimation of seismic vulnerability levels of urban structures within a supervised learning framework. We render the prediction problem in three ways to address operational requirements that can evolve in real-life situations. 1) To discriminate two or more classes based on labeled samples of all classes present in the data under investigation, we use the framework of soft margin support vector machines (C-SVM). 2) To consider situations, where solely labeled samples are available for the class(es) of interest and not for all classes present in the data, we deploy ensembles of nu-one-class SVM (nu-OC-SVM). and 3) To fit data with a higher statistical level of measurement (interval or ratio scale), we utilize a support vector regression (SVR) approach to estimate a regr

关键词： Earthquakes Istanbul Landsat machine learning object-based image analysis RapidEye seismic vulnerability assessment support vector machines (SVM) TanDEM-X

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Detection and Delineation of Localized Flooding from World View-2 Multispectral Data

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REMOTE SENSING 2015年第11期7卷 14853-14875页

作者： Malinowski, Radoslaw Groom, Geoff Schwanghart, Wolfgang Heckrath, Goswin Aarhus Univ Dept Agroecol DK-8830 Tjele Denmark Aarhus Univ Dept Biosci DK-8410 Ronde Denmark Univ Potsdam Inst Earth & Environm Sci D-14476 Potsdam Germany

Remote sensing technology serves as a powerful tool for analyzing geospatial characteristics of flood inundation events at various scales. However, the performance of remote sensing methods depends heavily on the flood characteristics and landscape settings. Difficulties might be encountered in mapping the extent of localized flooding with shallow water on riverine floodplain areas, where patches of herbaceous vegetation are interspersed with open water surfaces. To address the difficulties in mapping inundation on areas with complex water and vegetation compositions, a high spatial resolution dataset has to be used to reduce the problem of mixed pixels. The main objective of our study was to investigate the possibilities of using a single date WorldView-2 image of very high spatial resolution and supporting data to analyze spatial patterns of localized flooding on a riverine floodplain. We used a decision tree algorithm with various combinations of input variables including spectral bands of the WorldView-2 image, selected spectral indices dedicated to mapping water surfaces and vegetation, and topographic data. The overall accuracies of the twelve flood extent maps derived with the decision tree method and performed on both pixels and image objects ranged between 77% and 95%. The highest mapping overall accuracy was achieved with a method that utilized all available input data and the object-based image analysis. Our study demonstrates the possibility of using single date WorldView-2 data for analyzing flooding events at high spatial detail despite the absence of spectral bands from the short-waveform region that are frequently used in water related studies. Our study also highlights the importance of topographic data in inundation analyses. The greatest difficulties were met in mapping water surfaces under dense canopy herbaceous vegetation, due to limited water surface exposure and the dominance of vegetation reflectance.

关键词： decision tree floodplain inundation localized flooding object-based image analysis wetlands WorldView-2

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object-based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA

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REMOTE SENSING 2014年第11期6卷 11372-11390页

作者： Li, Xiaoxiao Shao, Guofan Arizona State Univ Julie Ann Wrigley Global Inst Sustainabil Tempe AZ 85287 USA Purdue Univ Dept Forestry & Nat Resources W Lafayette IN 47907 USA

There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km(2) land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods.

关键词： object-based image analysis OBIA orthophoto land cover classification urban landscape mapping Indiana

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object-based classification of semi-arid vegetation to support mine rehabilitation and monitoring

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JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷

作者： Bao, Nisha Lechner, Alex M. Johansen, Kasper Ye, Baoying Northeast Univ Inst Geoinformat & Digital Mine Res Shenyang 110819 Peoples R China Univ Queensland Sustainable Minerals Inst Ctr Mined Land Rehabil St Lucia Qld 4072 Australia Univ Tasmania Ctr Environm Hobart Tas 7005 Australia Univ Queensland Sch Geog Planning & Environm Management Biophys Remote Sensing Grp St Lucia Qld 4072 Australia China Univ Geosci Sch Land Sci & Technol Beijing 100083 Peoples R China

Mining activities result in significantly modified landscapes that require rehabilitation to mitigate the negative environmental impacts and restore ecological function. The aim of this study was to develop a remote sensing method suitable for monitoring the vegetation cover at mine rehabilitation sites. We used object-based image analysis (OBIA) methods and high-spatial resolution SPOT-5 imagery to identify discrete land-cover patterns that occur at fine spatial scales. These patterns relate to spatial processes that are important drivers of successful restoration of mine sites. SPOT-5 imagery of the Kidston Gold mine tailing dam in semi-arid tropical north Queensland was acquired in July 2005, comprising four 10-m spectral bands and a 2.5-m panchromatic (PAN) band. The classification scheme used in this study was adapted to the spatial scale of SPOT-5 imagery from mine closure criteria cover requirements, according to a mine rehabilitation plan. Four land-cover classes were identified: tree cover, dense grass, sparse grass, and bare ground. First, textural layers (contrast, dissimilarity, and homogeneity) were derived for each vegetation class except for bare ground from the PAN and multispectral bands. Of all textural layer combinations, homogeneity and contrast in the PAN band were identified using a Z-test as the most useful for differentiating between multiple land-cover classes. Next, an optimal segmentation scale parameter of 15 was identified using an analysis of spatial autocorrelation. Finally, the SPOT-5 image bands, derived textural layers, and normalized difference vegetation index (NDVI) were used in an OBIA fuzzy membership classification approach to map vegetation land-cover classes. The classification results were assessed with the traditional error matrix approach and the object-based accuracy assessment method. The overall classification accuracy using the error matrix was 92.5% and 81% using the object-based method. The relatively high-classific

关键词： remote sensing object-based image analysis rehabilitation restoration mining semi-arid spatial autocorrelation classification error assessment

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