A review of assessing the accuracy of classifications of remotely sensed data

doi:10.1016/0034-4257(91)90048-B

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A review of assessing the accuracy of classifications of remotely sensed data

Journal Article•DOI•

A review of assessing the accuracy of classifications of remotely sensed data

Russell G. Congalton¹•Institutions (1)

University of California, Berkeley¹

01 Jul 1991-Remote Sensing of Environment (Elsevier)-Vol. 37, Iss: 1, pp 35-46

TL;DR: This paper reviews the necessary considerations and available techniques for assessing the accuracy of remotely sensed data including the classification system, the sampling scheme, the sample size, spatial autocorrelation, and the assessment techniques.

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About: This article is published in Remote Sensing of Environment.The article was published on 1991-07-01. It has received 6747 citations till now. The article focuses on the topics: Spatial analysis.

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Journal Article•DOI•

Status of land cover classification accuracy assessment

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Giles M. Foody¹•Institutions (1)

University of Southampton¹

01 Apr 2002-Remote Sensing of Environment

TL;DR: It is likely that it is unlikely that a single standardized method of accuracy assessment and reporting can be identified, but some possible directions for future research that may facilitate accuracy assessment are highlighted.

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3,800 citations

Cites background from "A review of assessing the accuracy ..."

...…of classification accuracy, however, disagreement between the derived land cover map and the ground data is typically, and unfairly, taken to indicate an error in the map derived from the remotely sensed data when other explanations exist (Congalton, 1991; Fitzgerald & Lees, 1994; Smedes, 1975)....
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...In the interpretation of classification accuracy, however, disagreement between the derived land cover map and the ground data is typically, and unfairly, taken to indicate an error in the map derived from the remotely sensed data when other explanations exist (Congalton, 1991; Fitzgerald & Lees, 1994; Smedes, 1975)....
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...While there is an obvious desire to balance statistical requirements with practicalities (Belward et al., 1999; Congalton, 1991; Edwards et al., 1998; Merchant et al., 1994), the choice of sampling design influences the reliability of an accuracy assessment (Muller et al....
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...Disagreements between the two data sets are typically interpreted as errors in the land cover map derived from the remotely sensed data (Congalton, 1991; Smedes, 1975)....
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...…are the provision of more than one measure of classification accuracy (Muller et al., 1998; Stehman, 1997a), with associated confidence limits (Stehman, 1997a; Thomas & Allcock, 1984), together with the confusion matrix (Stehman, 1997a), sometimes normalized (Congalton, 1991; Smits et al., 1999)....
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Journal Article•DOI•

Change detection techniques

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Dengsheng Lu¹, Paul Mausel², Emilio F. Moran¹•Institutions (2)

Indiana University¹, Indiana State University²

20 Jun 2004-International Journal of Remote Sensing

TL;DR: This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature and summarizes and reviews these techniques.

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Abstract: Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

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2,785 citations

Journal Article•DOI•

A survey of image classification methods and techniques for improving classification performance

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Dengsheng Lu¹, Qihao Weng²•Institutions (2)

Indiana University¹, Indiana State University²

15 Feb 2007-Journal of remote sensing

TL;DR: It is suggested that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map and the selection of a suitable classification method is especially significant for improving classification accuracy.

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Abstract: Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

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2,741 citations

Journal Article•DOI•

Evaluating resource selection functions

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Mark S. Boyce¹, Pierre R. Vernier², Scott E. Nielsen¹, Fiona K. A. Schmiegelow¹•Institutions (2)

University of Alberta¹, University of British Columbia²

30 Nov 2002-Ecological Modelling

TL;DR: A form of k -fold cross validation for evaluating prediction success is proposed for presence/available RSF models, which involves calculating the correlation between RSF ranks and area-adjusted frequencies for a withheld sub-sample of data.

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2,107 citations

Cites methods from "A review of assessing the accuracy ..."

...Statistical analysis We divided the data, by season (early hyperphagia and late hyperphagia), into cross-validation groups following a k -fold partitioning design (Fielding and Bell, 1997; Hastie et al., 2001). Huberty’s (1994) rule of thumb was used to determine the model training-to-testing ratio....
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...Remote-sensing classifications frequently use Kappa as a measure of accuracy assessment (Congalton, 1991; Lillesand and Kiefer, 1994)....
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Good Practice Guidance for Land Use, Land-Use Change and Forestry

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J. Penman, M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, R. Pipatti, L Buendia, K Miwa, T. Ngara, K Tanabe, Fabian Wagner - Show less +7 more

01 Jan 2003

2,059 citations

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References

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Journal Article•DOI•

A Coefficient of agreement for nominal Scales

[...]

Jacob Cohen¹•Institutions (1)

York University¹

01 Apr 1960-Educational and Psychological Measurement

TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.

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Abstract: CONSIDER Table 1. It represents in its formal characteristics a situation which arises in the clinical-social-personality areas of psychology, where it frequently occurs that the only useful level of measurement obtainable is nominal scaling (Stevens, 1951, pp. 2526), i.e. placement in a set of k unordered categories. Because the categorizing of the units is a consequence of some complex judgment process performed by a &dquo;two-legged meter&dquo; (Stevens, 1958), it becomes important to determine the extent to which these judgments are reproducible, i.e., reliable. The procedure which suggests itself is that of having two (or more) judges independently categorize a sample of units and determine the degree, significance, and

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34,965 citations

"A review of assessing the accuracy ..." refers methods in this paper

...The result of performing a KAPPA analysis is a KHAT statistic (an estimate of KAPPA), which is another measure of agreement or accuracy....
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...Despite the overall accuracy of the supervised approach being 6% higher than the unsupervised approach (84% -78% = 6%), the results of the KAPPA analysis show that these two approaches are not significantly different....
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...Another discrete multivariate technique of use in accuracy assessment is called KAPPA (Cohen, 1960)....
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...Table 8 presents the results of the KAPPA analysis that compares the error matrices two at a time to determine if they are significantly different....
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...In addition to being a third measure of accuracy , KAPPA is also a powerful technique in its ability to provide information about a single matrix as well as to statistically compare matrices....
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Book•

Discrete multivariate analysis: theory and practice

[...]

Yvonne M. M. Bishop, Paul W. Holland, Stephen E. Fienberg

01 Jan 1975

TL;DR: Discrete Multivariate Analysis is a comprehensive text and general reference on the analysis of discrete multivariate data, particularly in the form of multidimensional tables, and contains a wealth of material on important topics.

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Abstract: "At last, after a decade of mounting interest in log-linear and related models for the analysis of discrete multivariate data, particularly in the form of multidimensional tables, we now have a comprehensive text and general reference on the subject. Even a mediocre attempt to organize the extensive and widely scattered literature on discrete multivariate analysis would be welcome; happily, this is an excellent such effort, but a group of Harvard statisticians taht has contributed much to the field. Their book ought to serve as a basic guide to the analysis of quantitative data for years to come." --James R. Beninger, Contemporary Sociology "A welcome addition to multivariate analysis. The discussion is lucid and very leisurely, excellently illustrated with applications drawn from a wide variety of fields. A good part of the book can be understood without very specialized statistical knowledge. It is a most welcome contribution to an interesting and lively subject." --D.R. Cox, Nature "Discrete Multivariate Analysis is an ambitious attempt to present log-linear models to a broad audience. Exposition is quite discursive, and the mathematical level, except in Chapters 12 and 14, is very elementary. To illustrate possible applications, some 60 different sets of data have been gathered together from diverse fields. To aid the reader, an index of these examples has been provided. ...the book contains a wealth of material on important topics. Its numerous examples are especially valuable." --Shelby J. Haberman, The Annals of Statistics

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5,309 citations

"A review of assessing the accuracy ..." refers background in this paper

...i=1 where r is the number of rows in the matrix, xii is the number of observations in row i and column i, x i+ and x +i are the marginal totals of row i and column i, respectively, and N is the total number of observations (Bishop et al., 1975)....
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...x i+ and x +i are the marginal totals of row i and column i, respectively, and N is the total number of observations (Bishop et al., 1975). The KHAT equation is published in this paper to clear up some confusion caused by a typographical error in Congalton et al. (1983), who originally proposed the use of this statistic for remotely sensed data....
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...x i+ and x +i are the marginal totals of row i and column i, respectively, and N is the total number of observations (Bishop et al., 1975)....
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Other•DOI•

A land use and land cover classification system for use with remote sensor data

[...]

James Richard Anderson, Ernest E. Hardy, John T. Roach, Richard E. Witmer

01 Jan 1976

TL;DR: The framework of a national land use and land cover classification system is presented for use with remote sensor data and uses the features of existing widely used classification systems that are amenable to data derived from re-mote sensing sources.

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Abstract: The framework of a national land use and land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to data from satellite and aircraft remote sensors. The pro-posed system uses the features of existing widely used classification systems that are amenable to data derived from re-mote sensing sources. It is intentionally left open-ended so that Federal, regional, State, and local agencies can have flexibility in developing more detailed land use classifications at the third and fourth levels in order to meet their particular needs and at the same time remain compatible with each other and the national system. Revision of the land use classification system as presented in US Geological Survey Circular 671 was undertaken in order to incorporate the results of extensive testing and review of the categorization and definitions.

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4,154 citations

Journal Article•DOI•

Introduction to remote sensing

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James B. Campbell

24 Jul 1987-Geocarto International

TL;DR: The history and scope of remote sensing is described in detail in this paper, where the authors present a detailed overview of the field of Remote Sensing and its application in agriculture, land use and land cover.

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Abstract: Preface. Part I: Foundations. History and Scope of Remote Sensing. Electromagentic Radiation. Part II: Image Acquisition. Photographic Sensors. Digital Data. Image Interpretation. Land Observation Satellites. Active Microwave and Lidar. Thermal Radiation. Image Resolution. Part III: Analysis. Preprocessing. Image Classification. Field Data. Accuracy Assessment. Hyperspectral Remote Sensing. Part IV: Applications. Geographic Information Systems. Plant Sciences. Earth Sciences. Hydrospheric Sciences. Land Use and Land Cover. Global Remote Sensing.

...read moreread less

3,445 citations

"A review of assessing the accuracy ..." refers methods in this paper

...Starting with Congalton et al. (1983), discrete multivariate techniques have been used for performing statistical tests on the classification accuracy of digital remotely sensed data....
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...Since that time many others have adopted these techniques as the standard accuracy assessment tools (e.g., Rosenfield and Fitzpatrick-Lins, 1986; Hudson and Ramm, 1987; Campbell, 1987)....
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Journal Article•

Accuracy assessment: a user's perspective

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M. Story, Russell G. Congalton

01 Jan 1986-Photogrammetric Engineering and Remote Sensing

1,260 citations

"A review of assessing the accuracy ..." refers background in this paper

...This measure, called "user's accuracy" or reliability, is indicative of the probability that a pixel classified on the map/image actually represents that category on the ground (Story and Congalton, 1986)....
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