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Conference on Earth Resources and Environmental Remote Sensing/GIS Applications V

作者： Boldt, Markus Thiele, Antje Cadario, Erich Schulz, Karsten Hinz, Stefan Fraunhofer Inst Optron Syst Technol & Image Explo Gutleuthausstr 1 D-76275 Ettlingen Germany Karlsruhe Inst Technol Inst Photogrammetry & Remote Sensing D-76131 Karlsruhe Germany

ISBN: (纸本)9781628413083

Change detection based on remote sensing imagery is a topic highly on demand with various fields of application. Probably, disaster management is the best known, where it is crucial to get fast and reliable results to enable a suitable supply of the affected region. Another important issue, for example in city or land-use planning, is the regular monitoring of specific regions of interest. For both scenarios, it would be significant to have information about the type or category of the detected changes. Since High-Resolution (HR) Synthetic Aperture Radar (SAR) is in opposite to optical sensors an active technique, it is well-capable for all change detection topics where a regular monitoring is intended. SAR sensors illuminate the investigated scene by their own microwave radiation and most applied microwave wavelengths make SAR nearly independent from atmospheric effects like dust, fog, and clouds. Moreover, the time of day makes no difference using SAR sensors. Acquired in HR SpotLight mode 300 (HS300) by the German satellite TerraSAR-X (TSX), images have a resolution of better than one meter, which allows to separate small objects placed close together. In this paper, a concept of change analysis focusing on small-sized areas is presented. Those change areas can be caused by man-made objects (e.g. vehicles, small construction sites) or natural events like phenologically based changes of the vegetation. Since the presented change analysis concept deals with the analysis of time series imagery, other seasonal also man-made caused changes (e.g. agriculture) can be detected. Furthermore, the concept comprises the categorization of the detected changes, which separates it from many of the existing change detection approaches. It includes five central components given by the change detection itself, the pre-categorization of change pixels, the feature extraction for change blobs, the analysis of their spatial context, and the final decision making forming a categorizati

关键词： Synthetic Aperture Radar TerraSAR-X high resolution change detection time series object-based image analysis Differential Attribute Profiles segmentation

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Feature Assessment in object-based Forest Classification using Airborne LiDAR Data and High Spatial Resolution Satellite imagery 3

Feature Assessment in Object-based Forest Classification usi...

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

作者： Zhang, Zhenyu Liu, Xiaoye Wright, Wendy Univ So Queensland Sch Civil Engn & Surveying Toowoomba Qld 4350 Australia Monash Univ Sch Appl Sci & Engn Churchill Vic 3842 Australia

ISBN: (纸本)9781479941841

The last decade has witnessed an increase in interest in the application of airborne LiDAR data and high spatial resolution satellite imagery for forest structure modelling, tree species identification and classification. The integration of LiDAR data and WorldView-2 satellite imagery produced different combinations of input data layers for image segmentations and a large number of variables derived from these data layers for object-based classifications. Assessment of different features (including the input data layers and subsequently derived variables) for object-based forest classification is important. In this study, five image segmentation schemes were explored to test the effectiveness of the different input data layers, in particular, the new WorldView-2 multispectral bands to object-based forest classification. object-based variables derived from these data layers were assessed to rank their importance before inputting into decision trees for forest classifications. It demonstrated that, using methods developed in this study, the integration of airborne LiDAR and eight WorldView-2 bands can significantly improve the accuracy of forest classification in our study area. The variable importance was ranked, indicating how important a variable contributes to the classification in a particular decision tree. The results showed that using LiDAR data alone or four conventional bands only, the overall accuracies achieved were 61.39% and 61.42% respectively, but the overall accuracy increased to 82.35% when all eight bands and the LiDAR data were used.

关键词： object-based image analysis LiDAR WorldView-2 decision tree forest classification

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A Knowledge-based Procedure for Remote Sensing image Classification 11

A Knowledge-based Procedure for Remote Sensing Image Classif...

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11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhang, Limin Xu, Tao Zhang, Jianting Yu, Yingfu Naval Aeronaut & Astronaut Univ Dept Sci Res Yantai Peoples R China Naval Aeronaut & Astronaut Univ Inst Informat Fus Yantai Peoples R China

ISBN: (纸本)9781479951482

object-based image analysis (OBIA) in combination with domain knowledge is an important methodology for remote sensing image interpretation. It can deal with semantic gap effectively. In this paper, a knowledge-based procedure for remote sensing image classification is presented. Domain knowledge is represented with semantic network, which guides the initial classification of the image. A quantitative matching degree is used in the modification of the initial classification. The proposed procedure is much more efficient compared with method in literature.

关键词： object-based image analysis semantic network remote sensing image classification matching degree

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A Comparison of Pixel-based and object-based Land Cover Classification Methods in an Arid/Semi-arid Environment of Northwestern China 3

A Comparison of Pixel-Based and Object-Based Land Cover Clas...

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

作者： Zhang, Jingxiao Jia, Li Chinese Acad Sci State Key Lab Remote Sensing Sci Inst Remote Sensing & Digital Earth Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Joint Ctr Global Change Studies Beijing 100875 Peoples R China

ISBN: (纸本)9781479941841

Land cover classification provides useful information of vegetation for land surface models especially in arid areas. Due to the complex landscapes in the downstream area of the Heihe River basin in northwest China, accurate land cover classification plays a key role in better understanding of the eco-hydrological processes, identification of water consumption by different land cover types and effective use of scarce water resources in this region. The aim of this study is to map land cover classification in the arid environment in the downstream area of the Heihe River basin by employing both pixel-based and object-based image analysis methods using high spatial resolution data. The data used in this study were 2.5 m high spatial resolution imagery (SPOT-5) and field survey. Vegetation indices and land surface texture characteristics were calculated from SPOT-5 imagery and used as input variables for classifications. In this study, overall classification accuracies between pixel-based and object-based classification methods were statistically significant when the same Support Vector Machine algorithm was applied. object-based classification methods could avoid the salt-and-pepper noise existed in the pixel-oriented results and achieved more accurate depictions of land cover types in this region than the results using pixel-based algorithms. The accuracy assessment on the classification of single tree crown of Populus euphratica revealed more promising results by using object-based image analysis than using pixel-based classification methods. The low accuracy in identifying grassland class was due to the inadequate selection of samples for classifications in the study area. Inappropriate scale value of segmentation resulted in the low producer's accuracy of residential areas when using object-based algorithms, whereas the misclassification led to the low user's accuracy of residential areas when utilizing perpixel methods.

关键词： pixel-based image analysis object-based image analysis land cover classification SPOT-5 arid/semi-arid environment

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A Knowledge-based procedure for remote sensing image classification

A Knowledge-based procedure for remote sensing image classif...

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The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

作者： Limin Zhang Tao Xu Yingfu Yu Jianting Zhang Department of Scientific Research Naval Aeronautical and Astronautical University Yantai China Institute of Information Fusion Naval Aeronautical and Astronautical University Yantai China

ISBN: (纸本)9781479951499

object-based image analysis(OBIA)in combination with domain knowledge is an important methodology for remote sensing image *** can deal with semantic gap *** this paper,a knowledge-based procedure for remote sensing image classification is *** knowledge is represented with semantic network,which guides the initial classification of the image.A quantitative matching degree is used in the modification of the initial *** proposed procedure is much more efficient compared with method in literature.

关键词： object-based image analysis semantic network remote sensing image classification matching degree

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Remote Sensing image Retrieval by Scene Semantic Matching

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2013年第5期51卷 2874-2886页

作者： Wang, Min Song, Tengyi Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210046 Jiangsu Peoples R China

This paper proposes a remote sensing (RS) image retrieval scheme by using image scene semantic (SS) matching. The low-level image visual features (VFs) are first mapped into multilevel spatial semantics via VF extraction, object-based classification of support vector machines, spatial relationship inference, and SS modeling. Furthermore, a spatial SS matching model that involves the object area, attribution, topology, and orientation features is proposed for the implementation of the sample-scene-based image retrieval. Moreover, a prototype system that uses a coarse-to-fine retrieval scheme is implemented with high retrieval accuracy. Experimental results show that the proposed method is suitable for spatial SS modeling, particularly geographic SS modeling, and performs well in spatial scene similarity matching.

关键词： Attributed relational graph (ARG) image retrieval object-based image analysis remote sensing (RS) image scene matching semantic semantic gap

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Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images

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REMOTE SENSING OF ENVIRONMENT 2013年 130卷 51-61页

作者： Arnesen, Allan S. Silva, Thiago S. F. Hess, Laura L. Novo, Evlyn M. L. M. Rudorff, Conrado M. Chapman, Bruce D. McDonald, Kyle C. Inst Nacl Pesquisas Espaciais Div Sensoriamento Remoto BR-12201970 Sao Jose Dos Campos Brazil Univ Calif Santa Barbara Earth Res Inst Santa Barbara CA 93106 USA Univ Calif Santa Barbara Bren Sch Environm Sci & Management Santa Barbara CA 93106 USA CALTECH Jet Prop Lab Pasadena CA 91109 USA CUNY City Coll Dept Earth & Atmospher Sci CUNY Environm Crossrd Initiat New York NY 10031 USA CUNY City Coll CUNY CREST Inst New York NY 10031 USA

The Amazon River floodplain is subject to large seasonal variations in water level and flood extent, due to the large size and low relief of the basin, and the large amount of precipitation in the region. Synthetic Aperture Radar (SAR) data can be used to map flooded area in these wetlands, given its ability to provide continuous information without being heavily affected by cloud cover. As part of JAXA's Kyoto & Carbon Initiative, extensive wide-swath, multi-temporal SAR coverage of the Amazon basin has been obtained using the ScanSAR mode of ALOS PALSAR This study presents a method for monitoring flood extent variation using ALOS ScanSAR images, tested at the Curuai Lake floodplain, in the lower Amazon River, Brazil. Twelve ScanSAR scenes were acquired between 2006 and 2010, including seven during the 2007 hydrological year. Water level records, field photographs, optical images (Landsat-5/TM and MODIS/Ferra and Aqua) and topographic data were used as auxiliary information. A data mining algorithm allowed the implementation of a hierarchical, object-based classification algorithm, able to map land cover types and flooding status in the study area for all available dates. land cover based on the entire time series (classification levels 1 and 2) had overall accuracies of 90% and 83%, respectively. Level 3 classifications (one map per image date) were validated only for the lowest and highest water stages, with overall accuracies of 76% and 78%, respectively. Total flood extent (Level 4) was mapped with 84% and 94% accuracies, for the low and high water stages, respectively. Regression models were fitted between mapped flooded area and water levels at the Curuai gauge to predict flood extent. A polynomial model had R-2=0.95 (p<0.05) and an overall root mean square error (RMSE) of 241 km(2), while a logistic model had R-2=0.98 (p<0.05) and RMSE = 127 km(2). (C) 2012 Elsevier Inc. All rights reserved.

关键词： object-based image analysis Multi-temporal analysis Incidence angle Wetlands Synthetic aperture radar Kyoto & Carbon Initiative

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Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2013年第8期185卷 6261-6275页

作者： Meneguzzo, Dacia M. Liknes, Greg C. Nelson, Mark D. US Forest Serv No Res Stn USDA St Paul MN 55108 USA

Discrete trees and small groups of trees in nonforest settings are considered an essential resource around the world and are collectively referred to as trees outside forests (ToF). ToF provide important functions across the landscape, such as protecting soil and water resources, providing wildlife habitat, and improving farmstead energy efficiency and aesthetics. Despite the significance of ToF, forest and other natural resource inventory programs and geospatial land cover datasets that are available at a national scale do not include comprehensive information regarding ToF in the United States. Additional ground-based data collection and acquisition of specialized imagery to inventory these resources are expensive alternatives. As a potential solution, we identified two remote sensing-based approaches that use free high-resolution aerial imagery from the National Agriculture imagery Program (NAIP) to map all tree cover in an agriculturally dominant landscape. We compared the results obtained using an unsupervised per-pixel classifier (independent component analysis-[ICA]) and an object-based image analysis (OBIA) procedure in Steele County, Minnesota, USA. Three types of accuracy assessments were used to evaluate how each method performed in terms of: (1) producing a county-level estimate of total tree-covered area, (2) correctly locating tree cover on the ground, and (3) how tree cover patch metrics computed from the classified outputs compared to those delineated by a human photo interpreter. Both approaches were found to be viable for mapping tree cover over a broad spatial extent and could serve to supplement ground-based inventory data. The ICA approach produced an estimate of total tree cover more similar to the photo-interpreted result, but the output from the OBIA method was more realistic in terms of describing the actual observed spatial pattern of tree cover.

关键词： Trees outside forests Forest inventory Tree cover Aerial photography object-based image analysis Independent component analysis

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Semi-automated analysis of high-resolution aerial images to quantify docks in glacial lakes

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2013年 81卷 60-69页

作者： Beck, Marcus W. Vondracek, Bruce Hatch, Lorin K. Vinje, Jason Univ Minnesota Conservat Biol Grad Program St Paul MN 55108 USA US Geol Survey Minnesota Cooperat Fish & Wildlife Res Unit St Paul MN 55108 USA Univ Minnesota Dept Fisheries Wildlife & Conservat Biol St Paul MN 55108 USA Minnesota Dept Nat Resources Div Fish & Wildlife Detroit Lakes MN 56501 USA

Lake resources can be negatively affected by environmental stressors originating from multiple sources and different spatial scales. Shoreline development, in particular, can negatively affect lake resources through decline in habitat quality, physical disturbance, and impacts on fisheries. The development of remote sensing techniques that efficiently characterize shoreline development in a regional context could greatly improve management approaches for protecting and restoring lake resources. The goal of this study was to develop an approach using high-resolution aerial photographs to quantify and assess docks as indicators of shoreline development. First, we describe a dock analysis workflow that can be used to quantify the spatial extent of docks using aerial images. Our approach incorporates pixel-based classifiers with object-based techniques to effectively analyze high-resolution digital imagery. Second, we apply the analysis workflow to quantify docks for 4261 lakes managed by the Minnesota Department of Natural Resources. Overall accuracy of the analysis results was 98.4% (87.7% based on (K) over cap) after manual post-processing. The analysis workflow was also 74% more efficient than the time required for manual digitization of docks. These analyses have immediate relevance for resource planning in Minnesota, whereas the dock analysis workflow could be used to quantify shoreline development in other regions with comparable imagery. These data can also be used to better understand the effects of shoreline development on aquatic resources and to evaluate the effects of shoreline development relative to other stressors. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

关键词： Aquatic resources Docks object-based image analysis Shoreline development Unsupervised classification

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An object-based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2013年第4期10卷 928-931页

作者： Zhou, Weiqi Chinese Acad Sci Ecoenvironm Sci Res Ctr State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China

Digital surface models (DSMs) derived from light detection and ranging (LiDAR) data have been increasingly integrated with high-resolution multispectral satellite/aerial imagery for urban land cover classification. Fewer studies, however, have investigated the usefulness of LiDAR intensity in aid of urban land cover classification, particularly in highly developed urban settings. In this letter, we use an object-based classification approach to investigate whether a combination of LiDAR height and intensity data can accurately map urban land cover. We further compare the approach to a method that uses multispectral imagery as the primary data source, but LiDAR DSM as ancillary data to aid in classification. The study site is a suburban area in Baltimore County, MD. The LiDAR data were acquired in March 2005, from which DSM and two intensity layers (first and last returns), with 1-m spatial resolution were generated, respectively. Four classes were included: 1) buildings;2) pavement;3) trees and shrubs;and 4) grass. Our results indicated that the object-based approach provided flexible and effective means to integrate LiDAR height and intensity data for urban land cover classification. A combination of the LiDAR height and intensity data proved to be effective for urban land cover classification. The overall accuracy of the classification was 90.7%, and the overall Kappa statistics equaled 0.872, with the user's and producer's accuracies ranging from 86.8% to 93.6%. The accuracy of the results were far better than those using multispectral imagery alone, and comparable to using DSM data in combination with high-resolution multispectral satellite/aerial imagery.

关键词： Baltimore high-resolution imagery intensity light detection and ranging (LiDAR) normalized digital surface model (nDSM) object-based image analysis urban land cover classification

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