By Peter Atkinson, Giles M. Foody, Steven E. Darby, Fulong Wu
Papers from a September 2003 convention, provided the following, deal with distant sensing, spatially allotted dynamic modeling of land floor methods, and concrete dynamics. individuals in geocomputation, spatial research, GIS, and environmental modeling study contemporary advancements in assembling geographical details, and speak about the administration, advantages, and demanding situations of modeling dynamic techniques in 3 dimensions. Papers are in sections on distant sensing, actual strategies, and human techniques. The paintings accumulated right here illustrates the significance of incorporating interdisciplinary sciences to hone GIS functions, percentage information and representations, and speak successfully via visualization.
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Extra info for GeoDynamics (2004)(en)(440s)
One major source of error noted with land cover maps derived from remote sensing, especially in maps derived from coarse spatial resolution sensors such as those commonly used in the derivation of global maps, is associated with mixed pixels. , the area represented by each pixel comprises a single land cover class), mixed pixels (representing an area covered by more than one land cover class) cannot be appropriately or accurately mapped. Since the use of conventional (hard) classification techniques in mapping land cover from remote sensor imagery must introduce error when mixed pixels are present, researchers have sought alternative approaches for mapping.
2 15 Crisp Classification Accuracy Measures Accuracy Metric Formulation Overall accuracy 1 N User’s accuracy nii /Ni Story and Congalton (1986) Producer’s accuracy nii /Mi Story and Congalton (1986) Average accuracy (user’s) 1 c c Average accuracy (producer’s) 1 c c Combined accuracy (user’s) 1 [OA + AA ] u 2 Fung and LeDrew (1988) Combined accuracy (producer’s) 1 [OA + AA ] P 2 Fung and LeDrew (1988) Kappa coefficient of agreement Po − Pe 1 − Pe Congalton et al. (1983) Weighted Kappa 1− Conditional Kappa (user’s) Po(i +) − Pe(i +) Conditional Kappa (producer’s) Po(+i ) − Pe(+i ) c n i =1 ii Base Reference Story and Congalton (1986) nii i =1 Ni Fung and LeDrew (1988) nii i =1 Mi Fung and LeDrew (1988) vij Poij vij Pcij 1 − Pe(i +) 1 − Pe(+i ) Rosenfield and Fitzpatrick-Lins (1986) Rosenfield and Fitzpatrick-Lins (1986) Rosenfield and Fitzpatrick-Lins (1986) Tau coefficient (equal probability) Po − (1/c) 1 − (1/c) Ma and Redmond (1995) Tau coefficient (unequal probability) P o − Pr 1 − Pr Ma and Redmond (1995) Conditional Tau (user’s) Po(i +) − Pi Conditional Tau (producer’s) Po(+i ) − Pi 1 − Pi 1 − Pi Naesset (1996) Naesset (1996) Definition of terms: N is total number of testing pixels; nii is the number of samples correctly classified; Ni and Mi are the row and column totals for class i , respectively; Po = (1/N ) ci=1 nii is the observed proportion of agreement Pe = (1/N 2 ) ci=1 Ni Mi is the expected chance agreement; vij is the weight; Poij is the observed cell proportion; Peij is the expected cell proportion; Po(i +) is the observed agreement according to user’s approach computed from all columns in i th row of the error matrix; Pe(i +) is the agreement expected by chance for i th row; Po(+i ) is the observed agreement according to producer’s approach computed from all rows in i th column of the error matrix; Pe(+i ) is the agreement expected by chance for i th column; Pr = (1/N ) ci=1 ni + xi , where xi is the unequal a priori probability of class membership; Pi is the a priori probability of class membership.
A conditional GILE: “2837_c002” — 2004/11/17 — 13:22 — page 16 — #6 CRISP AND FUZZY CLASSIFICATION ACCURACY MEASURES 17 Tau coefficient may be used to indicate the accuracy of an individual class (Naesset, 1996b). It may thus be seen that there are a number of measures that may be computed from an error matrix. Each measure may, however, be based on different assumptions about the data and thus may evaluate different components of accuracy (Lark, 1995; Stehman, 1997). Therefore, in general, it may be expedient to provide an error matrix with the classified image and report more than one measure of accuracy to fully describe the quality of that classification (Stehman, 1997).