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

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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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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Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California

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REMOTE SENSING 2014年第8期6卷 7276-7302页

作者： Liau, Yan-Ting Univ Oklahoma Ctr Spatial Anal Norman OK 73019 USA

Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potential solution, more data requirements and large amounts of testing at high costs are required. In this study, I applied a three-level hierarchical vegetation framework for reducing those costs, and a three-step procedure was used to evaluate its effects on a digital orthophoto quadrangles with 1 m spatial resolution. Step one and step two were for image segmentation optimized for delineation of tree density, which involved global Otsu's method followed by the random walker algorithm. Step three was for detailed species delineations, which were derived from multiresolution segmentation, in two test areas. Step one and step two were able to delineating tree density segments and label species association robustly, compared to previous hierarchical frameworks. However, step three was limited by less image information to produce detailed, reasonable image objects with optimal scale parameters for species labeling. This hierarchical vegetation framework has potential to develop baseline data for evaluating climate change impacts on vegetation at lower cost using widely available data and a personal laptop.

关键词： object-based image analysis image segmentation optimized for delineation of tree density very high resolution imagery species association labeling the Z values of Moran's I

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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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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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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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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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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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