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

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 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. However, with the exception of feature space optimization, little or no improvement in classification accuracy is achieved by each of these strategies individually.

[1]  R. M. Haralick,et al.  Textural features for image classification. IEEE Transaction on Systems, Man, and Cybernetics , 1973 .

[2]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[3]  C. Woodcock,et al.  The factor of scale in remote sensing , 1987 .

[4]  C. J. Oddy,et al.  Automatic interpretation strategies for synthetic aperture radar images , 1988, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[5]  D. M. McKeown, Jr.  Building knowledge-based systems for detecting man-made structures from remotely sensed imagery , 1988, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[6]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[7]  Jean-Paul Donnay.  Use of Remote Sensing Information in Planning , 1999 .

[8]  U. Ammer,et al.  OBJECT-BASED CLASSIFICATION AND APPLICATIONS IN THE ALPINE FOREST ENVIRONMENT , 1999 .

[9]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[10]  G. Willhauck,et al.  Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. , 2000 .

[11]  Limin Yang,et al.  COMPLETION OF THE 1990S NATIONAL LAND COVER DATA SET FOR THE CONTERMINOUS UNITED STATES FROM LANDSAT THEMATIC MAPPER DATA AND ANCILLARY DATA SOURCES , 2001 .

[12]  I. Gitas,et al.  Object-oriented classification modelling for fuel type mapping in the Mediterranean, using LANDSAT TM and IKONOS imagery- preliminary results , 2002 .

[13]  D. Civco,et al.  A COMPARISON OF LAND USE AND LAND COVER CHANGE DETECTION METHODS , 2002 .

[14]  A Comparison of AVIRIS and Synthetic Landsat Data for Land Use Classification at the Urban Fringe , 2002 .

[15]  D. Flanders,et al.  Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction , 2003 .

[16]  J. Franklin,et al.  Rationale and conceptual framework for classification approaches to assess forest resources and properties , 2003 .

[17]  A. Marangoz,et al.  COMPARISON OF PIXEL-BASED AND OBJECT-ORIENTED CLASSIFICATION APPROACHES USING LANDSAT-7 ETM SPECTRAL BANDS , 2004 .

[18]  Volker Walter,et al.  Object-based classification of remote sensing data for change detection , 2004 .

[19]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[20]  A. Rango,et al.  Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico , 2004 .

[21]  A. Goetz,et al.  A comparison of AVIRIS and Landsat for land use classification at the urban fringe , 2004 .

[22]  B. Koch,et al.  Landscape structure assessment with image grey‐values and object‐based classification at three spatial resolutions , 2005 .

[23]  Waqar Ahmad,et al.  A COMPARISON OF OBJECT-ORIENTED AND PIXEL-BASED CLASSIFICATION METHODS FOR MAPPING LAND COVER IN NORTHERN AUSTRALIA. , 2005 .

[24]  R. G. Pontlus.  Quantification Error Versus Location Error in Comparison of Categorical Maps , 2006 .