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

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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Estimating Class Dynamics for Fuzzy Markov Chain-based Multitemporal Cascade Classification

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2013年第4期10卷 908-912页

作者： Feitosa, Raul Q. Mota, Guilherme L. A. Alves, Andrei O. Costa, Gilson A. O. P. Pontificia Univ Catolica Rio de Janeiro Dept Elect Engn BR-22451900 Rio De Janeiro Brazil Univ Estado Rio De Janeiro Dept Comp Syst Engn BR-20550900 Rio De Janeiro Brazil Univ Estado Rio De Janeiro Inst Math & Stat BR-20550900 Rio De Janeiro Brazil Univ Estado Rio De Janeiro BR-20550900 Rio De Janeiro Brazil

The key component of a fuzzy Markov chain (FMC)-based multitemporal cascade classifier is the transition possibility matrix (TPM). Such matrix represents the temporal dynamics of the land use/land cover classes in the target site in a given time period. The choice of the TPM estimation approach is a crucial step in the design of FMC-based classifiers, as it strongly influences the final classification accuracy. Moreover, the task of collecting training data may involve considerable effort, since the number of transitions to be represented grows with the square of the number of classes in the application. In spite of their relevance, the TPM estimation has only been addressed superficially in previous publications about FCM-based classification methods. In this letter, we concern some of those aspects and investigate alternative ways of the TPM estimation. Experimental analysis on a multitemporal data set covering a 20-year period sheds light on the conditions under which those alternative estimation approaches may be used, as well as on their impact over the classification performance.

关键词： Fuzzy Markov chains (FMCs) multitemporal cascade classification object-based image analysis

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A Semisupervised Latent Dirichlet Allocation Model for object-based Classification of VHR Panchromatic Satellite images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2014年第4期11卷 863-867页

作者： Shen, Li Tang, Hong Chen, Yunhao Gong, Adu Li, Jing Yi, Wenbin Beijing Normal Univ State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China Beijing Normal Univ Key Lab Environm Change & Nat Disaster Beijing 100875 Peoples R China HSE Informat Ctr CNPC Inst Safety & Environm Technol Beijing 102206 Peoples R China

Typically, object-based classification methods are learned using training samples with labels attached to image objects. In this letter, a semisupervised object-based method in the framework of topic modeling is proposed to classify very high resolution panchromatic satellite images using partially labeled pixels. In the stage of training, both topics and their co-occurred distributions are learned in an unsupervised manner from segmented satellite images. Meanwhile, unlabeled pixels are allocated user-provided geo-object class labels based on the learned model. In the stage of classification, each segment is classified as a user-provided geo-object class label with the maximum posterior probability. Experimental results show that the proposed method outperforms several SVM-based supervised classification methods in terms of both spatial consistency and semantic consistency.

关键词： object-based image analysis probabilistic topic models semisupervised image classification

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Optimization of Segmentation Algorithms Through Mean-Shift Filtering Preprocessing

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2014年第3期11卷 622-626页

作者： Wang, Leiguang Liu, Guoying Dai, Qinling Southwest Forestry Univ Kunming 650224 Peoples R China Anyang Normal Univ Anyang 455002 Peoples R China

This letter proposes an improved mean-shift filtering method. The method is added as a preprocessing step for regional segmentation methods, which aims at benefiting segmentations in a more general way. Using this method, first, a logistic regression model between two edge cues and semantic object boundaries is established. Then, boundary posterior probabilities are predicted by the model and associated with weights in the mean-shift filtering iteration. Finally, the filtered image, instead of the original image, is put into segmentation methods. In experiments, the regression model is trained with an aerial image, which is tested with an aerial image and a QuickBird image. Two popular segmentation methods are adopted for evaluations. Both quantitative and qualitative evaluations reveal that the presented procedure facilitates a superior image segmentation result and higher classification accuracy.

关键词： image segmentation mean-shift filtering multiresolution segmentation object-based image analysis segmentation accuracy

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A supervised approach for simultaneous segmentation and classification of remote sensing images

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 142卷 162-173页

作者： Zanotta, Daniel Capella Zortea, Maciel Ferreira, Matheus Pinheiro Natl Inst Sci Educ & Technol R Engn Alfredo Huch 475 BR-96201460 Rio Grande RS Brazil IBM Res Ave Pasteur 138-146 BR-22290240 Rio De Janeiro RJ Brazil Natl Inst Space Res INPE Remote Sensing Div BR-12227010 Sao Jose Dos Campos SP Brazil

object-based image classification is recognized as one of the best strategies to analyze high spatial resolution remote sensing images. This process includes defining scale parameters to form regions sharing similar characteristics such as color, texture, or shape. Traditionally, in an object-based supervised classification setting the image is classified only after the segmentation process is completed. However, when the imaged objects on the ground are heterogeneous and of different sizes, some resulting segments can be appropriate for classification while others are over or under-segmented. This may cause partial failure of the subsequent classification. In this paper, we introduce a simultaneous approach based on the interception of the segmentation stage by provisional classification of under-growing segments. Our proposal is to optimize the classification process by iteratively updating the labels of previously generated regions only if the estimated posterior probabilities of the winning classes in the new segments increase. Experiments with three multispectral datasets acquired by Landsat-5 TM, QuickBird-II, and WorldView-3 in rural and urban areas compare traditional object-based approach based on region growing with the proposed method using well-established classifiers. Our results show that the proposed method becomes much less sensitive to the choice of segmentation parameters and reaches similar, or even better, classification accuracies.

关键词： object-based image analysis Segmentation Supervised classification Multispectral imaging

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An open source object-based framework to extract landform classes

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第1期39卷 541-554页

作者： Camargo, F. F. Almeida, C. M. Costa, G. A. O. P. Feitosa, R. Q. Oliveira, D. A. B. Heipke, C. Ferreira, R. S. Natl Inst Space Res INPE Remote Sensing Div Sao Jose Dos Campos SP Brazil Catholic Univ Rio de Janeiro PUC Rio Dept Elect Engn Rio De Janeiro Brazil Leibniz Univ Hannover Inst Photogrammetry & GeoInformat D-30167 Hannover Germany

This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called Interimage. This framework exhibits a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of a semantic network, which accounts for a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in Interimage are presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic to drive the interpretation semantic network. object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The Interimage interpretation strategy yielded a classification result with strong agreement and proved to be effective for the extraction of landforms. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Cognitive approaches object-based image analysis Semantic network Interimage Geomorphology

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Differentiating tower karst (fenglin) and cockpit karst (fengcong) using DEM contour, slope, and centroid

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ENVIRONMENTAL EARTH SCIENCES 2014年第2期72卷 407-416页

作者： Huang, Wei Deng, Chengbin Day, Michael J. Univ Wisconsin Dept Geog Milwaukee WI 53201 USA SUNY Binghamton Dept Geog Binghamton NY 13902 USA

The complex geometry of tropical karst landforms poses particular challenges for morphometric analysis, morphological classification and evolutionary assessment. Detailed geomorphological studies of the two most spectacular forms-tower karst (fenglin) and cockpit karst (fengcong)-have employed time consuming and labor intensive field surveys, which have yielded inconclusive and ambiguous results. This paper tests a novel discriminatory approach utilizing contour, slope and centroid derived from the ASTER Global Digital Elevation Model to differentiate between cockpit and tower karst in the Guilin area of southern China. Morphological indices are calculated to compare and contrast geomorphic variations using object-based image analysis (OBIA). The results suggest that the method provides a feasible means of differentiating between tower and cockpit landforms, and that OBIA offers a fast and semi-automatic way to extract morphological parameters.

关键词： Tower karst (fenglin) Cockpit karst (fengcong) Morphology Morphometry Contour Slope Centroid object-based image analysis

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Can you see green? Assessing the visibility of urban forests in cities

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LANDSCAPE AND URBAN PLANNING 2009年第2期91卷 97-104页

作者： Yang, Jun Zhao, Linsen Mcbride, Joe Gong, Peng Temple Univ Dept Hort & Landscape Architecture Ambler PA 19002 USA SW Forestry Univ Fac Landscape Architecture Kunming 650224 Yunnan Province Peoples R China Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA 94720 USA Chinese Acad Sci Inst Remote Sensing Applicat State Key Lab Sensing Sci Beijing 100101 Peoples R China Beijing Normal Univ Beijing 100101 Peoples R China

The Green View index was developed to evaluate the visibility of urban forests. It was estimated through a combination of field surveys and photography interpretation and tested in Berkeley, California. The results showed that the overall Green View value in Berkeley was 24.79%. It has a strong correlation (R-2 = 0.86) with the canopy cover of tree/shrubs, which was 31.49%. Besides the absolute amount of canopy cover, Green View is also influenced by the distribution pattern of trees. The ANOVA analysis indicated that the size of trees, the distance between trees and viewers, the existence of vegetation other than trees and shrubs all had a significant impact on values of Green View. The study shows that the use of large-size trees is important for creating a view with more greenery in a city. Green View can be used to evaluate the visual impact of various planning and management practices on urban forests. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Urban forestry Sensory functions Structure indices Vertical profile object-based image analysis

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Technical design and system implementation of region-line primitive association framework

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年 130卷 202-216页

作者： Wang, Min Xing, Jinjin Wang, Jie Lv, Guonian Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China

Apart from regions, image edge lines are an important information source, and they deserve more attention in object-based image analysis (OBIA) than they currently receive. In the region-line primitive association framework (RLPAF), we promote straight-edge lines as line primitives to achieve powerful OBIAs. Along with regions, straight lines become basic units for subsequent extraction and analysis of OBIA features. This study develops a new software system called remote-sensing knowledge finder (RSFinder) to implement RLPAF for engineering application purposes. This paper introduces the extended technical framework, a comprehensively designed feature set, key technology, and software implementation. To our knowledge, RSFinder is the world's first OBIA system based on two types of primitives, namely, regions and lines. It is fundamentally different from other well-known region-only-based OBIA systems, such as eCogntion and ENVI feature extraction module. This paper has important reference values for the development of similarly structured OBIA systems and line-involved extraction algorithms of remote sensing information. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Region-line primitive association framework object-based image analysis image segmentation Region Straight line High-resolution remote sensing Information extraction

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Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type classification

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2014年 94卷 169-182页

作者： Rasanen, Aleksi Kuitunen, Markku Tomppo, Erkki Lensu, Anssi Univ Jyvaskyla Dept Biol & Environm Sci FI-40014 Jyvaskyla Finland Finnish Forest Res Inst FI-01301 Vantaa Finland

We developed a classification workflow for boreal forest habitat type mapping. In object-based image analysis framework, Fractal Net Evolution Approach segmentation was combined with random forest classification. High-resolution WorldView-2 imagery was coupled with ALS based canopy height model and digital terrain model. We calculated several features (e.g. spectral, textural and topographic) per image object from the used datasets. We tested different feature set alternatives;a classification accuracy of 78.0% was obtained when all features were used. The highest classification accuracy (79.1%) was obtained when the amount of features was reduced from the initial 328 to the 100 most important using Boruta feature selection algorithm and when ancillary soil and land-use GIS-datasets were used. Although Boruta could rank the importance of features, it could not separate unimportant features from the important ones. Classification accuracy was bit lower (78.7%) when the classification was performed separately on two areas: the areas above and below 1 m vertical distance from the nearest stream. The data split, however, improved the classification accuracy of mire habitat types and streamside habitats, probably because their proportion in the below 1 m data was higher than in the other datasets. It was found that several types of data are needed to get the highest classification accuracy whereas omitting some feature groups reduced the classification accuracy. A major habitat type in the study area was mesic forests in different successional stages. It was found that the inner heterogeneity of different mesic forest age groups was large and other habitat types were often inside this heterogeneity. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Habitat type mapping Multispectral imagery ALS object-based image analysis Random forest classifier Feature selection

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