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The use of high-resolution remote sensing for plague surveillance in Kazakhstan

REMOTE SENSING OF ENVIRONMENT 2010年第3期114卷 674-681页

作者： Addink, E. A. De Jong, S. M. Davis, S. A. Dubyanskiy, V. Burdelov, L. A. Leirs, H. Univ Utrecht Dept Phys Geog NL-3508 TC Utrecht Netherlands Yale Univ Sch Med Epidemiol & Publ Hlth New Haven CT 06520 USA Univ Utrecht Dept Vet Med NL-3508 TD Utrecht Netherlands M Aikimbayevs Kazakh Sci Ctr Quarantine & Zoonot Anti Plague Inst Alma Ata 050074 Kazakhstan Univ Antwerp Dept Biol B-2020 Antwerp Belgium Univ Aarhus Dept Integrated Pest Management Danish Pest Infestat Lab DK-2800 Lyngby Denmark

Bubonic plague, caused by the bacteria Yersinia pestis, persists as a public health problem in many parts of the world, including central Kazakhstan. Bubonic plague occurs most often in humans through a flea bite, when a questing flea fails to find a rodent host. For many of the plague foci in Kazakhstan the great gerbil is the major host of plague, a social rodent well-adapted to desert environments. Intensive monitoring and prevention of plague in gerbils started in 1947, reducing the number of human cases and mortalities drastically. However, the monitoring is labour-intensive and hence expensive and is now under threat due to financial constraints. Previous research showed that the occupancy rate of the burrow systems of the great gerbil is a strong indicator for the probability of a plague outbreak. The burrow systems are around 30 m in diameter with a bare surface. This paper aims to demonstrate the automatic classification of burrow systems in satellite images using object-oriented analysis. We performed field campaigns in September 2007 and May and September 2008 and acquired corresponding QuickBird images of the first two periods. User's and producer's accuracy values of the classification reached 60 and 86%, respectively, providing proof of concept that automatic mapping of burrow systems using high-resolution satellite images is possible. Such maps, by better defining great gerbil foci, locating new or expanding foci and measuring the density of great gerbil burrow systems could play a major role in a renewed monitoring system by better directing surveillance and control efforts. Furthermore, if similar analyses can separate occupied burrow systems from empty ones, then very-high-resolution images stand to play a crucial role in plague surveillance throughout central Asia. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Bubonic plague object-based image analysis Quickbird

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Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality

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REMOTE SENSING 2014年第5期6卷 4515-4545页

作者： Waser, Lars T. Kuechler, Meinrad Juette, Kai Stampfer, Theresia Swiss Fed Res Inst WSL CH-8903 Birmensdorf Switzerland Landesforst Mecklenburg Vorpommern Fachgebiet Entwicklung & Betrieb IT Gestutzter Fa D-19061 Schwerin Germany

Forest disturbances in central Europe caused by fungal pests may result in widespread tree mortality. To assess the state of health and to detect disturbances of entire forest ecosystems, up-to-date knowledge of the tree species diversity is essential. The German state Mecklenburg-Vorpommern is severely affected by ash (Fraxinus excelsior) dieback caused by the fungal pathogen Hymenoscyphus pseudoalbidus. In this study, species diversity and the magnitude of ash mortality was assessed by classifying seven different tree species and multiple levels of damaged ash. The study is based on a multispectral WorldView-2 (WV-2) scene and uses object-based supervised classification methods based on multinomial logistic regressions. Besides the original multispectral image, a set of remote sensing indices (RSI) was derived, which significantly improved the accuracies of classifying different levels of damaged ash but only slightly improved tree species classification. The large number of features was reduced by three approaches, of which the linear discriminant analysis (LDA) clearly outperformed the more commonly used principal component analysis (PCA) and a stepwise selection method. Promising overall accuracies (83%) for classifying seven tree species and (73%) for classifying four different levels of damaged ash were obtained. Detailed tree damage and tree species maps were visually inspected using aerial images. The results are of high relevance for forest managers to plan appropriate cutting and reforestation measures to decrease ash dieback over entire regions.

关键词： ash dieback variable selection multispectral remote sensing object-based image analysis remote sensing indices tree species tree mortality

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Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

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CANADIAN JOURNAL OF FOREST RESEARCH 2020年第10期50卷 1012-1024页

作者： Wang, Meimei Lin, Jiayuan Southwest Univ Sch Geog Sci Minist Educ Chongqing Jinfo Mt Field Sci Observat & Res Stn K Chongqing 400715 Peoples R China Southwest Univ Sch Geog Sci Chongqing Engn Res Ctr Remote Sensing Big Data Ap Chongqing 400715 Peoples R China

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R-2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

关键词： individual tree height unmanned aerial vehicle point cloud structure from motion object-based image analysis

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Determination of the age of oil palm from crown projection area detected from World View-2 multispectral remote sensing data: The case of Ejisu-Juaben district, Ghana

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2015年第Feb.期100卷 118-127页

作者： Chemura, Abel van Duren, Iris van Leeuwen, Louise M. Chinhoyi Univ Technol Dept Environm Sci & Technol Chinhoyi Zimbabwe Univ Twente Nat Resource Dept ITC NL-7500 AE Enschede Netherlands

Information about age of oil palm is important in sustainability assessments, carbon mapping, yield projections and precision agriculture. The aim of this study was to develop and test an approach to determine the age of oil palm plantations (years after planting) by combining high resolution multispectral remote sensing data and regression techniques using a case study of Ejisu-Juaben district of Ghana. Firstly, we determined the relationship between age and crown projection area of oil palms from sample fields. Secondly, we did hierarchical classification using object based image analysis techniques on WorldView-2 multispectral data to determine the crown projection areas of oil palms from remote sensing data. Finally, the crown projection areas obtained from the hierarchical classification were combined with the field-developed regression model to determine the age of oil palms at field level for a wider area. Field collected data showed a strong linear relationship between age and crown area of oil palm up to 13 years beyond which no relationship was observed. A user's accuracy of 80.6% and a producer's accuracy of 68.4% were obtained for the delineation of oil palm crowns while for delineation of non-crown objects a user's accuracy of 65.6% and a producer's accuracy of 78.6% were obtained, with an overall accuracy of 72.8% for the OBIA delineation. Automatic crown projection area delineation from remote sensing data produced crown projection areas which closely matched the field measured crown areas except for older oil palms (13+ years) where the error was greatest. Combining the remote sensing detected crown projection area and the regression model accurately estimated oil palm ages for 27.9% of the fields and had an estimation error of 1 year or less for 74.6% of the fields and an error of a maximum 2 years for 92.4% of the fields. The results showed that 6 and 11 year old oil palm stands were dominating age categories in the study area. Although the method

关键词： Age detection Oil palm World View-2 object-based image analysis

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Can segmentation evaluation metric be used as an indicator of land cover classification accuracy?

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JOURNAL OF APPLIED REMOTE SENSING 2016年第4期10卷

作者： Lenarcic, Andreja Svab Duric, Natasa Cotar, Klemen Ritlop, Klemen Ostir, Kristof Slovenian Ctr Excellence Space Sci & Technol Askerceva Cesta 12 SI-1000 Ljubljana Slovenia Univ Ljubljana Fac Civil & Geodet Engn Jamova Cesta 2 SI-1000 Ljubljana Slovenia Slovenian Acad Sci & Arts Res Ctr Novi Trg 2 SI-1000 Ljubljana Slovenia

It is a broadly established belief that the segmentation result significantly affects subsequent image classification accuracy. However, the actual correlation between the two has never been evaluated. Such an evaluation would be of considerable importance for any attempts to automate the object-based classification process, as it would reduce the amount of user intervention required to fine-tune the segmentation parameters. We conducted an assessment of segmentation and classification by analyzing 100 different segmentation parameter combinations, 3 classifiers, 5 land cover classes, 20 segmentation evaluation metrics, and 7 classification accuracy measures. The reliability definition of segmentation evaluation metrics as indicators of land cover classification accuracy was based on the linear correlation between the two. All unsupervised metrics that are not based on number of segments have a very strong correlation with all classification measures and are therefore reliable as indicators of land cover classification accuracy. On the other hand, correlation at supervised metrics is dependent on so many factors that it cannot be trusted as a reliable classification quality indicator. Algorithms for land cover classification studied in this paper are widely used;therefore, presented results are applicable to a wider area. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： object-based image analysis land cover segmentation quality segmentation evaluation metrics classification accuracy assessment correlation

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Improving hyperspectral image segmentation by applying inverse noise weighting and outlier removal for optimal scale selection

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2021年 171卷 348-366页

作者： Dao, Phuong D. Mantripragada, Kiran He, Yuhong Qureshi, Faisal Z. Univ Toronto Dept Geog Geomat & Environm 3359 Mississauga Rd Mississauga ON L5L 1C6 Canada Univ Toronto Sch Environm 33 Willcocks St Toronto ON M5S 3E8 Canada Ontario Tech Univ Fac Sci 2000 Simcoe St North Oshawa ON L1G OC5 Canada

Optimal scale selection for image segmentation is an essential component of the object-based image analysis (OBIA) and interpretation. An optimal segmentation scale is a scale at which image objects, overall, best represent real-world ground objects and features across the entire image. At this scale, the intra-object variance is ideally lowest and the inter-object spatial autocorrelation is ideally highest, and a change in the scale could cause an abrupt change in these measures. Unsupervised parameter optimization methods typically use global measures of spatial and spectral properties calculated from all image objects in all bands as the target criteria to determine the optimal segmentation scale. However, no studies consider the effect of noise in image spectral bands on the segmentation assessment and scale selection. Furthermore, these global measures could be affected by outliers or extreme values from a small number of objects. These issues may lead to incorrect assessment and selection of optimal scales and cause the uncertainties in subsequent segmentation and classification results. These issues become more pronounced when segmenting hyperspectral data with large spectral variability across the spectrum. In this study, we propose an enhanced method that 1) incorporates the band's inverse noise weighting in the segmentation and 2) detects and removes outliers before determining segmentation scale parameters. The proposed method is evaluated on three well-established segmentation approaches k-means, mean-shift, and watershed. The generated segments are validated by comparing them with reference polygons using normalized over-segmentation (OS), under-segmentation (US), and the Euclidean Distance (ED) indices. The results demonstrate that this proposed scale selection method produces more accurate and reliable segmentation results. The approach can be applied to other segmentation selection criteria and are useful for automatic multi-parameter tuning and opti

关键词： Inverse noise weighting Outlier detection Optimal scale selection image segmentation object-based image analysis Hyperspectral image classification

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Hybrid region merging method for segmentation of high-resolution remote sensing images

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2014年第Dec.期98卷 19-28页

作者： Zhang, Xueliang Xiao, Pengfeng Feng, Xuezhi Wang, Jiangeng Wang, Zuo Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing Peoples R China Nanjing Univ Key Lab Satellite Mapping Technol & Applicat Stat Nanjing Peoples R China Nanjing Univ Dept Geog Informat Sci Nanjing Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Atmospher Phys Nanjing Peoples R China

image segmentation remains a challenging problem for object-based image analysis. In this paper, a hybrid region merging (HRM) method is proposed to segment high-resolution remote sensing images. HRM integrates the advantages of global-oriented and local-oriented region merging strategies into a unified framework. The globally most-similar pair of regions is used to determine the starting point of a growing region, which provides an elegant way to avoid the problem of starting point assignment and to enhance the optimization ability for local-oriented region merging. During the region growing procedure, the merging iterations are constrained within the local vicinity, so that the segmentation is accelerated and can reflect the local context, as compared with the global-oriented method. A set of high-resolution remote sensing images is used to test the effectiveness of the HRM method, and three region-based remote sensing image segmentation methods are adopted for comparison, including the hierarchical stepwise optimization (HSWO) method, the local-mutual best region merging (LMM) method, and the multiresolution segmentation (MRS) method embedded in eCognition Developer software. Both the supervised evaluation and visual assessment show that HRM performs better than HSWO and LMM by combining both their advantages. The segmentation results of HRM and MRS are visually comparable, but HRM can describe objects as single regions better than MRS, and the supervised and unsupervised evaluation results further prove the superiority of HRM. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： High-resolution remote sensing image segmentation Region merging Graph model object-based image analysis

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Supremal multiscale signal analysis

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SIAM JOURNAL ON MATHEMATICAL analysis 2004年第1期36卷 94-120页

作者： Braga-Neto, U Goutsias, J Univ Texas MD Anderson Canc Ctr Sect Clin Canc Genet Houston TX 77030 USA Texas A&M Univ Dept Elect Engn College Stn TX 77840 USA Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

We introduce a novel approach to nonlinear signal analysis, which is referred to as supremal multiscale analysis. The proposed approach provides a rigorous mathematical foundation for a class of nonlinear multiscale signal analysis schemes and leads to a decomposition that can effectively be used in signal processing and analysis. Moreover, it is related to the supremal scale-spaces proposed by Heijmans and van den Boomgaard and is similar in flavor to the well-known linear multiresolution theory of Mallat and Meyer. In this framework, linear concepts such as vector spaces, projections, and linear operators are replaced by conceptually analogous nonlinear notions. We use supremal multiscale analysis to construct a multiscale image decomposition scheme based on two mathematical concepts that play a key role in the analysis and interpretation of images by vision systems, namely, regional maxima and connectivity. The resulting scheme is referred to as skyline supremal multiscale analysis and satisfies several useful properties desired by any multiscale image analysis tool. It is grayscale invariant, as well as translation and scale invariant. Moreover, it progressively removes connected components from the level sets of an image without introducing new ones. But, most importantly, it decomposes the regional maxima of an image in a natural causal hierarchy by gradually removing these maxima without introducing new ones. image decomposition by skyline supremal multiscale analysis can be used to construct nonlinear tools for image processing and analysis that provide solutions to problems where traditional linear techniques are ineffective. We discuss one such tool and illustrate its use in object-based extraction and denoising.

关键词： complete lattices connectivity connected operators mathematical morphology multiscale signal approximation multiscale signal analysis nonlinear signal analysis scale-spaces object-based image analysis

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Unsupervised image segmentation evaluation and refinement using a multi-scale approach

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2011年第4期66卷 473-483页

作者： Johnson, Brian Xie, Zhixiao Florida Atlantic Univ Dept Geosci Boca Raton FL 33431 USA

In this study, a multi-scale approach is used to improve the segmentation of a high spatial resolution (30 cm) color infrared image of a residential area. First, a series of 25 image segmentations are performed in Definiens Professional 5 using different scale parameters. The optimal image segmentation is identified using an unsupervised evaluation method of segmentation quality that takes into account global intra-segment and inter-segment heterogeneity measures (weighted variance and Moran's I, respectively). Once the optimal segmentation is determined, under-segmented and over-segmented regions in this segmentation are identified using local heterogeneity measures (variance and Local Moran's I). The under- and over-segmented regions are refined by (1) further segmenting under-segmented regions at finer scales, and (2) merging over-segmented regions with spectrally similar neighbors. This process leads to the creation of several segmentations consisting of segments generated at three different segmentation scales. Comparison of single- and multi-scale segmentations shows that identifying and refining underand over-segmented regions using local statistics can improve global segmentation results. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

关键词： object-based image analysis Multi-scale segmentation image segmentation evaluation Under-segmentation Over-segmentation

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object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2016年第17期37卷 4059-4083页

作者： Wieland, Marc Torres, Yolanda Pittore, Massimiliano Benito, Belen GFZ German Res Ctr Geosci Ctr Early Warning Syst D-14473 Potsdam Germany Tech Univ Madrid Earthquake Engn Res Grp Madrid Spain

This study evaluates the potential of object-based image analysis in combination with supervised machine learning to identify urban structure type patterns from Landsat Thematic Mapper (TM) images. The main aim is to assess the influence of several critical choices commonly made during the training stage of a learning machine on the classification performance and to give recommendations for classifier-dependent intelligent training. Particular emphasis is given to assess the influence of size and class distribution of the training data, the approach of training data sampling (user-guided or random) and the type of training samples (squares or segments) on the classification performance of a Support Vector Machine (SVM). Different feature selection algorithms are compared and segmentation and classifier parameters are dynamically tuned for the specific image scene, classification task, and training data. The performance of the classifier is measured against a set of reference data sets from manual image interpretation and furthermore compared on the basis of landscape metrics to a very high resolution reference classification derived from light detection and ranging (lidar) measurements. The study highlights the importance of a careful design of the training stage and dynamically tuned classifier parameters, especially when dealing with noisy data and small training data sets. For the given experimental set-up, the study concludes that given optimized feature space and classifier parameters, training an SVM with segment-shaped samples that were sampled in a guided manner and are balanced between the classes provided the best classification results. If square-shaped samples are used, a random sampling provided better results than a guided selection. Equally balanced sample distributions outperformed unbalanced training sets.

关键词： Machine learning object-based image analysis urban structure types Landsat Haiti

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