An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification∗

doi:10.1080/00330120701724152

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An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification∗

Journal Article•DOI•

An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification∗

Rutherford V. Platt¹, Lauren M. Rapoza¹•Institutions (1)

Gettysburg College¹

31 Jan 2008-The Professional Geographer (Taylor & Francis Group)-Vol. 60, Iss: 1, pp 87-100

TL;DR: It is found that the combination of segmentation into image objects, the nearest neighbor classifier, and integration of expert knowledge yields substantially improved classification accuracy for the scene compared to a traditional pixel-based method.

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Abstract: Object-oriented image classification has tremendous potential to improve classification accuracies of land use and land cover (LULC), yet its benefits have only been minimally tested in peer-reviewed studies. We aim to quantify the benefits of an object-oriented method over a traditional pixel-based method for the mixed urban–suburban–agricultural landscape surrounding Gettysburg, Pennsylvania. To do so, we compared a traditional pixel-based classification using maximum likelihood to the object-oriented image classification paradigm embedded in eCognition Professional 4.0 software. This object-oriented paradigm has at least four components not typically used in pixel-based classification: (1) the segmentation procedure, (2) nearest neighbor classifier, (3) the integration of expert knowledge, and (4) feature space optimization. We evaluated each of these components individually to determine the source of any improvement in classification accuracy. We found that the combination of segmentation into image o...

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Journal Article•DOI•

Object based image analysis for remote sensing

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Thomas Blaschke¹•Institutions (1)

University of Salzburg¹

01 Jan 2010-Isprs Journal of Photogrammetry and Remote Sensing

TL;DR: This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way.

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Abstract: Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.

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3,809 citations

Cites background or result from "An Evaluation of an Object-Oriented..."

...Many studies have compared OBIA methods with human interpretation of high resolution imagery (Shackelford and Davis, 2003; Ivits et al., 2005; al Khudairy et al., 2005; Carleer et al., 2005; Mo et al., 2007; Stow et al., 2007; Johansen et al., 2007; Jacquin et al., 2008; Zhou and Troy, 2008; Platt and Rapoza, 2008) and revealed the progress being made in this respect....
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...…resolution imagery (Shackelford and Davis, 2003; Ivits et al., 2005; al Khudairy et al., 2005; Carleer et al., 2005; Mo et al., 2007; Stow et al., 2007; Johansen et al., 2007; Jacquin et al., 2008; Zhou and Troy, 2008; Platt and Rapoza, 2008) and revealed the progress being made in this respect....
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...Platt and Rapoza (2008) compared results from a Maximum Likelihood classification with results from OBIA for a mixed urban-suburban-agricultural landscape surrounding Gettysburg, Pennsylvania....
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..., 2009) and OBIA accuracy assessment (Liu and Zhou, 2004; Zhang et al., 2005a; Luscier et al., 2006; Möller et al., 2007; Albrecht, 2008; Platt and Rapoza, 2008; Grenier et al., 2008)....
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...…2008; Conchedda et al., 2008; Schöpfer et al., 2008; Bontemps et al., 2008; Weinke et al., 2008; Gamanya et al., 2009) and OBIA accuracy assessment (Liu and Zhou, 2004; Zhang et al., 2005a; Luscier et al., 2006; Möller et al., 2007; Albrecht, 2008; Platt and Rapoza, 2008; Grenier et al., 2008)....
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Journal Article•DOI•

Change detection from remotely sensed images: From pixel-based to object-based approaches

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Masroor Hussain¹, Dongmei Chen¹, Angela Cheng¹, Hui Wei, David Stanley - Show less +1 more•Institutions (1)

Queen's University¹

01 Jun 2013-Isprs Journal of Photogrammetry and Remote Sensing

TL;DR: This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context, followed by a review of object-basedchange detection techniques.

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Abstract: The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.

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1,159 citations

Cites background from "An Evaluation of an Object-Oriented..."

...Although it dates back to the 1970s, it was not widely used mainly due to its limitations in spatial data resolution and computation (Platt and Rapoza, 2008)....
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Journal Article•DOI•

A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery

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Dennis C. Duro¹, Steven E. Franklin², Steven E. Franklin¹, Monique G. Dubé•Institutions (2)

University of Saskatchewan¹, Trent University²

15 Mar 2012-Remote Sensing of Environment

TL;DR: In this paper, pixel-based and object-based image analysis approaches for classifying broad land cover classes over agricultural landscapes are compared using three supervised machine learning algorithms: decision tree (DT), random forest (RF), and the support vector machine (SVM).

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785 citations

Journal Article•DOI•

Social Sensing: A New Approach to Understanding Our Socioeconomic Environments

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Yu Liu¹, Xi Liu¹, Song Gao², Li Gong¹, Chaogui Kang¹, Ye Zhi¹, Guanghua Chi¹, Li Shi¹ - Show less +4 more•Institutions (2)

Peking University¹, University of California, Santa Barbara²

27 Apr 2015-Annals of The Association of American Geographers

TL;DR: In this article, the authors use the term social sensing for individual-level big geospatial data and the associat- tation of the data to understand the socioeconomic environments.

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Abstract: The emergence of big data brings new opportunities for us to understand our socioeconomic environments. We use the term social sensing for such individual-level big geospatial data and the associat...

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560 citations

Journal Article•DOI•

A new insight into land use classification based on aggregated mobile phone data

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Tao Pei¹, Stanislav Sobolevsky¹, Carlo Ratti¹, Shih-Lung Shaw², Ting Li, Chenghu Zhou - Show less +2 more•Institutions (2)

Massachusetts Institute of Technology¹, Wuhan University²

01 Sep 2014-International Journal of Geographical Information Science

TL;DR: An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.

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Abstract: Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated m...

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330 citations

Cites background or methods from "An Evaluation of an Object-Oriented..."

...Because of this, more auxiliary information, such as contextual properties, field sizes and shapes, parcel information and expert knowledge, has been used to infer land-use patterns (De Wit and Clevers 2004, Platt and Rapoza 2008, Wu et al. 2009, Hu and Wang 2013)....
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...Because of this, more auxiliary 77 information, such as contextual properties, field sizes and shapes, parcel information, 78 and expert knowledge, has been used to infer land use patterns (De Wit and Clevers, 79 2004; Platt and Rapoza, 2008; Wu et al. 2009; Hu and Wang, 2013)....
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References

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Journal Article•DOI•

Textural Features for Image Classification

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Robert M. Haralick, K. Shanmugam¹, Its'hak Dinstein²•Institutions (2)

Wichita State University¹, University of Kansas²

01 Nov 1973

TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.

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Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

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20,442 citations

Book•DOI•

Assessing the accuracy of remotely sensed data : principles and practices

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Russell G. Congalton, Kass Green¹•Institutions (1)

Durham University¹

17 Sep 1998

TL;DR: This chapter discusses Accuracy Assessment, which examines the impact of sample design on cost, statistical Validity, and measuring Variability in the context of data collection and analysis.

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Abstract: Introduction Why Accuracy Assessment? Overview Historical Review Aerial Photography Digital Assessments Data Collection Considerations Classification Scheme Statistical Considerations Data Distribution Randomness Spatial Autocorrelation Sample Size Sampling Scheme Sample Unit Reference Data Collection Basic Collection Forms Basic Analysis Techniques Non-Site Specific Assessments Site Specific Assessments Area Estimation/Correction Practicals Impact of Sample Design on Cost Recommendations for Collecting Reference Data ASources of Variation in Reference Data Photo Interpretation vs. Ground Visitation Interpreter Variability Observations vs. Measurements What is Correct? Labeling Map vs. Labeling the Reference Data Qualitative vs. Quantitative Analysis Local vs. Regional vs. Global Assessments Advanced Topics Beyond the Error Matrix Modifying the Error Matrix Fuzzy Set Theory Measuring Variability Complex Data Sets Change Detection Multi-Layer Assessments California Hardwood Rangeland Monitoring Project Case Study Balancing Statistical Validity with Practical Reality Bibliography

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4,586 citations

"An Evaluation of an Object-Oriented..." refers methods in this paper

...Following Congalton and Green (1999), the random sample was supplemented by a stratified random sample of 250 points....
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Book•

Remote sensing digital image analysis

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J. A. Richards

01 Jan 1986

3,039 citations

"An Evaluation of an Object-Oriented..." refers background in this paper

...The maximum likelihood classifier calculates the probability that a pixel or object belongs to each class and then assigns the pixel or object to the class with the highest probability (Richards 1999)....
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Journal Article•DOI•

Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information

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Ursula C. Benz, Peter Hofmann, Gregor Willhauck, Iris Lingenfelder, Markus Heynen - Show less +1 more

01 Jan 2004-Isprs Journal of Photogrammetry and Remote Sensing

TL;DR: In this article, an object-oriented image analysis software, eCognition, is proposed to integrate remote sensing imagery and GIS for mapping, environmental monitoring, disaster management and civil and military intelligence.

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Abstract: Remote sensing from airborne and spaceborne platforms provides valuable data for mapping, environmental monitoring, disaster management and civil and military intelligence. However, to explore the full value of these data, the appropriate information has to be extracted and presented in standard format to import it into geo-information systems and thus allow efficient decision processes. The object-oriented approach can contribute to powerful automatic and semi-automatic analysis for most remote sensing applications. Synergetic use to pixel-based or statistical signal processing methods explores the rich information contents. Here, we explain principal strategies of object-oriented analysis, discuss how the combination with fuzzy methods allows implementing expert knowledge and describe a representative example for the proposed workflow from remote sensing imagery to GIS. The strategies are demonstrated using the first object-oriented image analysis software on the market, eCognition, which provides an appropriate link between remote sensing imagery and GIS.

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2,539 citations

"An Evaluation of an Object-Oriented..." refers background in this paper

...It is important to note that there is no such thing as optimal parameters for image segmentation (Benz et al. 2004)....
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...As the scale parameter increases, the size of the image objects also increases (Benz et al. 2004)....
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...Once these objects are derived, topological relationships with other objects (e.g., adjacent to, contains, is contained by, etc.), statistical summaries of spectral and textural values, and shape characteristics can all be employed in the classification procedures (Benz et al. 2004)....
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...FNEA is a pairwise clustering process that finds areas of minimum spectral and spatial heterogeneity given a set of scale, color, and shape parameters (Benz et al. 2004)....
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...High values indicate a complex geometrical structure of the object (Benz et al. 2004)....
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Multiresolution Segmentation-an optimization approach for high quality multi-scale image segmentation

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M. Baatz

01 Jan 2000

TL;DR: In this paper, a general segmentation algorithm based on homogeneity definitions in combination with local and global optimization techniques is proposed for object oriented image processing, which aims for an universal high-quality solution applicable and adaptable to many problems and data types.

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Abstract: A necessary prerequisite for object oriented image processing is successful image segmentation. The approach presented in this paper aims for an universal high-quality solution applicable and adaptable to many problems and data types. As each image analysis problem deals with structures of a certain spatial scale, the average image objects size must be free adaptable to the scale of interest. This is achieved by a general segmentation algorithm based on homogeneity definitions in combination with local and global optimization techniques. A scale parameter is used to control the average image object size. Different homogeneity criteria for image objects based on spectral and/or spatial information are developed and compared.

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1,672 citations