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Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2012年第1期67卷 134-147页

作者： Hoefle, Bernhard Hollaus, Markus Hagenauer, Julian Heidelberg Univ Inst Geog Chair GISci D-69120 Heidelberg Germany Vienna Univ Technol Inst Photogrammetry & Remote Sensing A-1040 Vienna Austria

This paper introduces a new GIS workflow for urban vegetation mapping from high-density (50 pts./m(2)) full-waveform airborne LiDAR data, combining the advantages of both raster and point cloud based analysis. Polygon segments derived by edge-based segmentation of the normalized digital surface model are used for classification. A rich set of segment features based on the point cloud and derived from full-waveform attributes is built, serving as input for a decision tree and artificial neural network (ANN) classifier. Exploratory data analysis and detailed investigation of the discriminative power of selected point cloud and full-waveform LiDAR observables indicate a high value of the occurrence of multiple distinct targets in a laser beam (i.e. 'echo ratio') for vegetation classification (98% correctness). The radiometric full-waveform observables (e.g. backscattering coefficient) do not suffice as single discriminators with low correctness values using a decision tree classifier (<= 72% correctness) but higher values with ANN (<= 95% correctness). Tests using reduced point densities indicate that the derived segment features and classification accuracies remain relatively stable even up to a reduction factor of 10 (5 pts./m(2)). In a representative study area in the City of Vienna/Austria the applicability of the developed object-based GIS workflow is demonstrated. The unique high density full-waveform LiDAR data open a new scale in 3D object characterization but demands for novel joint strategies in object-based raster and 3D point cloud analysis. (C) 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

关键词： Laser scanning LiDAR Calibration Vegetation object based image analysis Full-waveform

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Multitemporal change detection of urban trees using localized region-based active contours in VHR images

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REMOTE SENSING OF ENVIRONMENT 2012年 124卷 413-426页

作者： Ardila, Juan P. Biker, Wietske Tolpekin, Valentyn A. Stein, Alfred Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AA Enschede Netherlands

This study introduces active contours for multitemporal monitoring of urban trees using fine resolution imagery. In our implementation active contours locally identify tree crown image regions using a localized data energy term and competition of energy forces between adjacent contours. The simultaneous evolution of n contours represents an equivalent number of tree crowns which are initialized based on a fast and efficient image fitting procedure. We incorporate prior information into the multitemporal analysis, as optimized contours are propagated through a sequence of images. We demonstrate the applicability of the method to monitor individual trees and tree groups in two residential areas in the Netherlands using multispectral aerial images acquired over a period of five years. The method allowed the identification of abrupt and gradual tree crown changes in both areas and performed superior to an alternative region growing segmentation approach. We conclude that the method extracts meaningful image objects that facilitate tree crown monitoring and is effective to update tree crown spatial-databases in urban areas. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Tree detection Active contours Change detection Level sets object based image analysis Multitemporal analysis

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A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2012年第1期15卷 70-78页

作者： Laliberte, A. S. Browning, D. M. Rango, A. New Mexico State Univ Las Cruces NM 88003 USA USDA Agr Res Serv Las Cruces NM 88003 USA

The availability of numerous spectral, spatial, and contextual features with object-based image analysis (OBIA) renders the selection of optimal features a time consuming and subjective process. While several feature selection methods have been used in conjunction with OBIA, a robust comparison of the utility and efficiency of approaches would facilitate broader and more effective implementation. In this study, we evaluated three feature selection methods, (1) Jeffreys-Matusita distance (JM), (2) classification tree analysis (CTA), and (3) feature space optimization (FSO) for object-based vegetation classifications with sub-decimeter digital aerial imagery in arid rangelands of the southwestern U.S. We assessed strengths, weaknesses, and best uses for each method using the criteria of ease of use, ability to rank and/or reduce input features, and classification accuracies. For the five sites tested. JM resulted in the highest overall classification accuracies for three sites, while CTA yielded highest accuracies for two sites. FSO resulted in the lowest accuracies. CTA offered ease of use and ability to rank and reduce features, while JM had the advantage of assessing class separation distances. FSO allowed for determining features relatively quickly, because it operates within the OBIA software used in this analysis (eCognition). However, the feature ranking in FSO is not transparent and accuracies were relatively low. While all methods offered an objective approach for determining suitable features for classifications of sub-decimeter resolution aerial imagery, we concluded that CTA was best suited for this particular application. We explore the limitations, assumptions, and appropriate uses for this and other datasets. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Feature selection object based image analysis Aerial imagery Very high resolution Classification

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Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2012年第1期15卷 57-69页

作者： Ardila, Juan P. Bijker, Wietske Tolpekin, Valentyn A. Stein, Alfred Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AA Enschede Netherlands

Municipalities need accurate and updated inventories of urban vegetation in order to manage green resources and estimate their return on investment in urban forestry activities. Earlier studies have shown that semi-automatic tree detection using remote sensing is a challenging task. This study aims to develop a reproducible geographic object-based image analysis (GEOBIA) methodology to locate and delineate tree crowns in urban areas using high resolution imagery. We propose a GEOBIA approach that considers the spectral, spatial and contextual characteristics of tree objects in the urban space. The study presents classification rules that exploit object features at multiple segmentation scales modifying the labeling and shape of image-objects. The GEOBIA methodology was implemented on QuickBird images acquired over the cities of Enschede and Delft (The Netherlands), resulting in an identification rate of 70% and 82% respectively. False negative errors concentrated on small trees and false positive errors in private gardens. The quality of crown boundaries was acceptable, with an overall delineation error <0.24 outside of gardens and backyards. (C) 2011 Elsevier B.V. All rights reserved.

关键词： GEOBIA object based image analysis Tree mapping Tree crown identification Context classification object classification

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SATELLITE image SEGMENTATION: A NOVEL ADAPTIVE MEAN-SHIFT CLUSTERING based APPROACH

SATELLITE IMAGE SEGMENTATION: A NOVEL ADAPTIVE MEAN-SHIFT CL...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Banerjee, Biplab Varma, Surender G. Buddhiraju, Krishna Mohan Indian Inst Technol Ctr Studies Resources Engn Satellite Image Anal Lab Bombay 400076 Maharashtra India

ISBN: (纸本)9781467311595

Segmentation of satellite images using a novel adaptive non parametric mean-shift clustering algorithm is proposed in this paper. image segmentation refers to the process of splitting up an image into its constituent objects. It is also an important step in bridging the semantic gap between low level image interpretation and high level visual analysis. Mean-shift technique is based on the concept of kernel density estimation. It has been applied successfully in diverse vision related tasks including segmentation. The performance of the mean shift algorithm is greatly affected by the size of the parzen window and the terminating criteria. These two issues have been taken care of here in a purely statistical framework. The efficiency of this newly developed adaptive clustering has been judged for segmentation of any initially oversegmented satellite image. The notion of object based image analysis is preserved by initially over segmenting the image by watershed technique. Extensive experiments on several multispectral satellite images have confirmed the effectivity of this proposed approach in comparison to some widely used state of the art segmentation methods.

关键词： Segmentation Clustering Unsupervised learning Mean-Shift object based image analysis

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IMPACT OF LAND USE DYNAMICS ON STREAM FLOW: A CASE STUDY IN UPPER BHIMA BASIN, MAHARASHTRA, INDIA

IMPACT OF LAND USE DYNAMICS ON STREAM FLOW: A CASE STUDY IN ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Samal, Dipak R. Gedam, Shirish S. Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781467311595

The process of urbanization and industrialization always have an impact on the hydrological regime and alternate land use pattern of the area. The present study is being carried out for the upper Bhima basin in western India. The basin has undergone various land use changes during last decade and before due to various anthropogenic activities. An object based image analysis approach along with rainfall and resulting stream flow records for last two decades (1985-2004) were analyzed to study the impact of land use on steam flow in the basin. The study revealed that rainfall pattern was nearly constant during study period, whereas a significant reduction in stream flow has been observed. Conversion of wasteland to agricultural land, construction of new dams/reservoirs in the high rainfall region, increase in domestic and industrial water demand are the major factors responsible for stream flow reduction in the basin.

关键词： Bhima basin Land use Stream flow object based image analysis

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Soft image Segmentation Model

Soft Image Segmentation Model

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International Conference on Computer Vision in Remote Sensing (CVRS)

作者： Chen, Xuehong Chen, Jin Yamaguchi, Yasushi Beijing Normal Univ State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China Nagoya Univ Grad Sch Environm Studies Nagoya Aichi Japan

ISBN: (纸本)9781467312745;9781467312721

With the spatial resolution of remote sensing data increases, the information of spatial features become more and more important for interpretation of remote sensing images. Therefore, object based image analysis methods receive more and more attentions. image segmentation is the most important step for object based methods. Many segmentation algorithms have been developed, however, there are always uncertainties or errors in image segmentation. Unfortunately, such uncertainty information was largely neglected in previous studies. In this paper, a soft segmentation model aiming to describe the uncertainties in the segmentation procedure was developed based on the multi-resolution segmentation, which is a bottom-up approach and follows a pair-wise object merging process. At each merging step, the soft segmentation model calculates probabilities of several adjacent sub-objects merged into super-objects on the next level. By combining the probability at each merging step, the final probability of each pixel merged into the super-objects on the top level can be acquired. A case study of an IKON OS image was conducted for validating the effectiveness of the proposed model. The result shows that the soft image segmentation model is able to represent the reliability of the hard segmentation and the existence of mixed pixels on the borders of the adjacent objects. The uncertainty information of segmentation may help to further understand the segmented result

关键词： soft segmentation hard segmentation object based image analysis

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Forest Inventory Parameters And Carbon Mapping From Airborne Lidar

Forest Inventory Parameters And Carbon Mapping From Airborne...

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作者： Vinod Kumar University of Twente

学位级别：硕士

Sustainable decision making incorporating biological, ecological, social and economic component of forestry is reliant on precision forestry. As forest covers extensive landholdings, accurate, timely, repeatable, detailed and spatially explicit forest inventory characterisation and structural information are highly desirable for precision forestry needs. Light detection and ranging (LiDAR), a relatively recent active remote sensing technology, can provide accurate appraisal of vertical forest canopy structure. Individual tree and stand-level physical attributes such as height, vertical structure, canopy closure, and density can be retrieved from LiDAR data. In the present research, a novel method to precisely detect individual trees from high density airborne LiDAR point cloud data has been tested. Tree Canopies are delineated using object based image analysis and a new approach of Thiessen polygons. Further an array of important tree parameters such as tree height, canopy projection area (CPA), canopy base height, canopy volume, canopy density, canopy gaps, local tree density and canopy inclination have been extracted from the LiDAR point cloud data to prepare geospatial forest inventory. The research also deals with tree species classification based on query method on structural tree parameters in inventory database. Lastly, the sequestered forest carbon in the study area has been assessed by developing regression equation from the extracted parameters. Tree peaks were detected with high accuracy of 96 %, while best crown segmentation accuracy for Region growing segmentation approach was 84 % with 93.5 % one to one (1:1) correspondence. Thiessen polygon segmentation approach proved to be a good estimator of CPA with 94.2 % 1:1 correspondence and it could explain reference CPA with R 2 =0.9, RMSE=3.2 m 2. Tree height was extracted with R 2 =0.86, RMSE=0.86 m while canopy base height was extracted with an accuracy of R 2 =0.73, RMSE=0.86 m. Species classification wa

关键词： Forest inventory Tree crown delineation Region growing Thiessen polygon object based image analysis

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object IDENTIFICATION USING TEXTURE based CLASSIFICATION

OBJECT IDENTIFICATION USING TEXTURE BASED CLASSIFICATION

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作者： SIDDHARTHA KHARE INDIAN INSTITUTE OF TECHNOLOGY ROORKEE

学位级别：硕士

Identification of different type of objects present on the earth surface is an important aspect in remote sensing application. Depending upon the applications different type of objects can be identified. Traditional land use mapping by visual image-interpretation is expensive, time consuming and often subjective. Researchers have been searching for automatic or semi- automatic approaches for many years. For decades, large-scale aerial photos have been employed to extract building for mapping application. With the successively launching of high-resolution commercial satellites (e. g. IKONOS and QuickBird, WorldView-2), high-resolution satellite imagery has been shown to be a cost-effective alternative to aerial photography in many applications. The overall objective of this research is the development of semiautomatic approach for object identification based on high resolution WorldView-2 image and development of methods which incorporate texture information of objects. This research utilizes object based image analysis (OBIA) approach for a Roorkee region using WorldView-2 imagery, to identify trees and buildings. The methodology involves the texture information and also ancillary information such as shape, size, color, shadow etc. of objects. The method segments the image pixels into objects and utilizes texture and contextual information of the object rather than only using spectral information relied upon by traditional methods. IMAGINE objective tool from ERDAS Imagine software were used to define feature models for trees and buildings identification. In addition, the results were compared using visual interpretation, by overlaying vector file on the original subset images. Overall, the use of OBIA based on texture information holds great promise to identify and extract individual trees and buildings in Roorkee area.

关键词： object based image analysis WorldView-2 texture

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Soft image Segmentation Model

Soft Image Segmentation Model

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2012年遥感中的计算机视觉国际会议

作者： Xuehong Chen Jin Chen Yasushi Yamaguchi State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Beiji Graduate School of Environmental Studies Nagoya University Nagoya Japan

ISBN: (纸本)9781467312721

With the spatial resolution of remote sensing data increases,the information of spatial features become more and more important for interpretation of remote sensing ***,object based image analysis methods receive more and more *** segmentation is the most important step for object based *** segmentation algorithms have been developed,however,there are always uncertainties or errors in image ***,such uncertainty information was largely neglected in previous *** this paper,a soft segmentation model aiming to describe the uncertainties in the segmentation procedure was developed based on the multi-resolution segmentation,which is a bottom-up approach and follows a pair-wise object merging *** each merging step,the soft segmentation model calculates probabilities of several adjacent sub-objects merged into super-objects on the next *** combining the probability at each merging step,the final probability of each pixel merged into the super-objects on the top level can be acquired.A case study of an IKONOS image was conducted for validating the effectiveness of the proposed *** result shows that the soft image segmentation model is able to represent the reliability of the hard segmentation and the existence of mixed pixels on the borders of the adjacent *** uncertainty information of segmentation may help to further understand the segmented result.

关键词： soft segmentation hard segmentation object based image analysis

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