Status of land cover classification accuracy assessment

doi:10.1016/S0034-4257(01)00295-4

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Status of land cover classification accuracy assessment

Journal Article•DOI•

Status of land cover classification accuracy assessment

Giles M. Foody¹•Institutions (1)

University of Southampton¹

01 Apr 2002-Remote Sensing of Environment (Elsevier)-Vol. 80, Iss: 1, pp 185-201

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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About: This article is published in Remote Sensing of Environment.The article was published on 2002-04-01. It has received 3800 citations till now. The article focuses on the topics: Confusion matrix.

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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•

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

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Mark A. Friedl¹, Damien Sulla-Menashe¹, Bin Tan², Annemarie Schneider³, Navin Ramankutty, Adam Sibley¹, Xiaoman Huang¹ - Show less +3 more•Institutions (3)

Boston University¹, Goddard Space Flight Center², University of Wisconsin-Madison³

15 Jan 2010-Remote Sensing of Environment

TL;DR: The datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4, are described, with a four-fold increase in spatial resolution and changes in the input data and classification algorithm.

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2,713 citations

Cites methods from "Status of land cover classification..."

...Proportion of pixels derived from each clas ature andwill not be discussed here, except to indicate thatwe followed well-established community protocols used for this type of analysis (Foody, 2002; Strahler et al., 2006)....
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Journal Article•DOI•

Good practices for estimating area and assessing accuracy of land change

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Pontus Olofsson¹, Giles M. Foody², Martin Herold³, Stephen V. Stehman⁴, Curtis E. Woodcock¹, Michael A. Wulder⁵ - Show less +2 more•Institutions (5)

Boston University¹, University of Nottingham², Wageningen University and Research Centre³, State University of New York System⁴, Natural Resources Canada⁵

25 May 2014-Remote Sensing of Environment

TL;DR: This work provides practitioners with a set of “good practice” recommendations for designing and implementing an accuracy assessment of a change map and estimating area based on the reference sample data.

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1,708 citations

Cites background from "Status of land cover classification..."

...While the notion of accuracy assessment is well-established within the remote sensing community (Foody, 2002; Strahler et al., 2006), studies of land change routinely fail to assess the accuracy of the final change maps and few published studies of land change make full use of the information…...
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Journal Article•DOI•

Recent Advances in Techniques for Hyperspectral Image Processing

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Antonio Plaza¹, Jon Atli Benediktsson², Joseph W. Boardman, J. Brazile³, Lorenzo Bruzzone⁴, Gustavo Camps-Valls⁵, Jocelyn Chanussot⁶, Mathieu Fauvel², Mathieu Fauvel⁶, Paolo Gamba⁷, Anthony J. Gualtieri⁸, Mattia Marconcini⁴, James C. Tilton⁸, G. Trianni⁷ - Show less +10 more•Institutions (8)

University of Extremadura¹, University of Iceland², University of Zurich³, University of Trento⁴, University of Valencia⁵, Grenoble Institute of Technology⁶, University of Pavia⁷, Goddard Space Flight Center⁸

01 Jan 2009-Remote Sensing of Environment

TL;DR: A seminal view on recent advances in techniques for hyperspectral image processing, focusing on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information.

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1,481 citations

Cites methods from "Status of land cover classification..."

...As representative classification metrics, we used the overall accuracy and the kappa coefficient (Foody, 2002)....
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...These values were extracted from the confusion matrix (Foody, 2002)....
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...Allmodels are compared numerically (using the overall accuracy) and statistically, using the kappa and Wilcoxon rank sum tests (Foody, 2002)....
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References

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Journal Article•DOI•

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

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Russell G. Congalton¹•Institutions (1)

University of California, Berkeley¹

01 Jul 1991-Remote Sensing of Environment

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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6,747 citations

"Status of land cover classification..." refers background in this paper

...…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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Book•

Introductory Digital Image Processing: A Remote Sensing Perspective

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John R. Jensen

01 Dec 1995

TL;DR: Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications.

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Abstract: For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Pearson Series Geographic Information Science. Now in full color, the Fourth Edition provides up-to-date information on analytical methods used to analyze digital remote sensing data. Each chapter contains a substantive reference list that can be used by students and scientists as a starting place for their digital image processing project or research. A new appendix provides sources of imagery and other geospatial information.

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5,478 citations

"Status of land cover classification..." refers background in this paper

...Understanding the significance of land cover and predicting the effects of land cover change is particularly limited by the paucity of accurate land cover data....
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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

"Status of land cover classification..." refers background in this paper

...The elements of this change detection confusion matrix represent individual from/to class change scenarios (Congalton & Green, 1999; Khorram, 1999)....
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...For example, it is generally assumed implicitly that each case (e.g., pixel) to be classified belongs fully to one of the classes in an exhaustively defined set of discrete and mutually exclusive classes (Congalton et al., 1998; Congalton & Green, 1999; Lewis & Brown, in press; Townsend, 2000)....
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...From each type of matrix, some of the basic measures of accuracy discussed above can be derived to express the accuracy of the change detection (Biging et al., 1999; Congalton & Green, 1999)....
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...In fact, the ground data are just another classification which may contain error (Congalton & Green, 1999; Khorram, 1999; Lunetta, Iiames, Knight, Congalton, & Mace, 2001; Zhou, Robson, & Pilesjo, 1998)....
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...In that way, practical issues should not reduce the credibility of the accuracy statement derived (Stehman & Czaplewski, 1998)....
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Other•DOI•

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

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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•

Consequences of changing biodiversity

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F. Stuart Chapin¹, Erika S. Zavaleta², Valerie T. Eviner³, Rosamond L. Naylor², Peter M. Vitousek², Heather L. Reynolds⁴, David U. Hooper⁵, Sandra Lavorel⁶, Osvaldo E. Sala⁷, Sarah E. Hobbie⁸, Michelle C. Mack¹, Sandra Díaz⁹ - Show less +8 more•Institutions (9)

University of Alaska Fairbanks¹, Stanford University², University of California, Berkeley³, Kalamazoo College⁴, Western Washington University⁵, SupAgro⁶, University of Buenos Aires⁷, University of Minnesota⁸, National University of Cordoba⁹

11 May 2000-Nature

TL;DR: The large ecological and societal consequences of changing biodiversity should be minimized to preserve options for future solutions to global environmental problems.

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Abstract: Human alteration of the global environment has triggered the sixth major extinction event in the history of life and caused widespread changes in the global distribution of organisms. These changes in biodiversity alter ecosystem processes and change the resilience of ecosystems to environmental change. This has profound consequences for services that humans derive from ecosystems. The large ecological and societal consequences of changing biodiversity should be minimized to preserve options for future solutions to global environmental problems.

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3,977 citations

"Status of land cover classification..." refers background in this paper

...The community often tends to use, unquestioningly, techniques based on the confusion matrix for which the correct application and interpretation requires the satisfaction of often untenable assumptions (e.g., perfect coregistration of data sets) and the provision of rarely conveyed information…...
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...…basic processes including biogeochemical cycling and thereby on global warming (Penner, 1994), the erosion of soils and thereby on sustainable land use (Douglas, 1999), and for at least the next 100 years is likely to be the most significant variable impacting on biodiversity (Chapin et al., 2000)....
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