Kappa coefficient is another widely used evaluation standard, which is based on the confusion matrix to assess the precision of remote sensing classification. What is kappa coefficient, and how it can be calculated. Sd ia ne il sd accuracy assessments state level accuracies are very high. Analysis of urban warming based on remote sensing method reveals that the urban bias on minimum temperature is rising at a higher rate, 2. Based on the analysis of the characteristics of thin cloud images, a method for removing thin clouds based on wavelet coefficient substitution is proposed in this paper. The measurements that we will be finding are the kappa coefficient, the overall. The kappa coefficient is not an index of accuracy, indeed it is not an index of overall agreement but one of agreement beyond chance.
The kappa statistic not all agreement between the two images can necessarily be attributed to our proficiency in conducting the classification. This study gained the proposed threshold method using ndvi value able to identify and classify urban vegetation with the use of vhr. The kappa statistic or kappa coefficient is the most commonly used statistic for this purpose. More recently, olofsson, foody, herold, stehman, woodcock and wulder 2014, remote sensing of environment also advocated against kappa. Center for remote sensing and department of forestry, michigan state university, east lansing. Kappa kappa statistic estimated as reflects the difference between actual agreement and the agreement expected by chance kappa of 0. Based on the analysis of the characteristics of thin cloud images, a method for. Estimating the kappa coefficient and its variance under. However, due to objects scaledependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. Computational methods and nonasymptotic, nonnull distribution theory are discussed. Remote sensing image classification based on rbf neural. Accuracy assessment of land useland cover classification. The journal of applied remote sensing jars is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban landuse planning, environmental quality monitoring, ecological restoration, and numerous. Kappa allows assessing the accuracy of the classification based on a random method.
The values of threshold that used for dividing the results of statistical kappa coefficient degrees of agreement. There is controversy surrounding cohens kappa due to. A statistics measure of agreement, beyond chance, between two maps. It is generally thought to be a more robust measure than simple percent agreement calculation, as. Remotelysensed digital data may potentially help natural found to. Tau goetficients for accuracy assessment of classification of remote sensing data zhenkui ma and roland l. A perfect classification would produce a kappa value of one. Conclusion natureinspired algorithms prove to be efficient in image classification and can be effectively used in the field of remote sensing. The traditional remote sensing image segmentation method uses the same set of parameters for the entire image.
Land use and land cover lulc classification of satellite imagery is an important research area and studied exclusively in remote sensing. The calculation and interpretation of weighted kappa are demonstrated by two examples from forest surveys. One might ask why the remote sensing analysis is needed if the reference map to compare it to already exists. Redmond abstract the kappa coefficient is generally used to assess the accuracy of image classifications. A kappa coefficient will be used to verify the presence of the themes that were presented. Status of land cover classification accuracy assessment ucl. Kappa measures the percentage of data values in the main diagonal of the table and then adjusts. Accuracy assessment of the discrete classification of remotely. Hudsoll center for remote sensing and department of forestry, michigan state university, east lansing, mi 488241111. Users accuracy is like the producers accuracy in that it is the correctly classified cells for pasture, forest, water, and urban, but this time it is divided by the total points appearing in a given class. Kappa statistic estimated as reflects the difference between actual agreement and the. According to pontius 2011, kappa has not provided the useful information that it is supposed to bring.
Accuracy assessment is performed by comparing the map created by remote sensing analysis to a reference map based on a different information source. This study present essential source of information whereby planners and decision makers can use to sustainably plan the environment. Accuracy assessment goals portland state university. The proposed classification scheme is implemented with three remote sensing data sets, obtaining the highest overall accuracy and kappa coefficient in all cases 92. Use of the weighted kappa coefficient in classification. Fitzpatricklins 1986 provide additional details on applica tions of kappa in remote sensing. Now lets go back to the very top and find the overall accuracy and the kappa coefficient. Nature inspired algorithms in remote sensing image classification.
Cropland data layer program components satellite imagery. Hudsoll center for remote sensing and department of forestry. Weighted kappa is a useful measure of accuracy when the map classes are ordered, or when the relative seriousness of the different possible errors may vary. Stratified objectoriented image classification based on. A kappa of 1 indicates perfect agreement, whereas a kappa of 0 indicates agreement equivalent to chance. Classification accuracy assessment error matrix and kappa. What is your impression of the value of the kappa statistic. Correct formulation of the kappa coefficient of agreement. The kappa coefficient of agreement was introduced to the remote sensing community in the early 1980s as an index to express the accuracy of an image classification used to produce a thematic map congalton et al. Cohens kappa coefficient has been widely used for assessing classification results derived from remote sensing data. Kappa values range from 0 to 1, though they can be negative and range from 1 to 1. The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research. A limitation of kappa is that it is affected by the prevalence of the finding under observation. Change detection of optical remote sensing image disturbed by.
Cohens kappa when two binary variables are attempts by two individuals to measure the same thing, you can use cohens kappa often simply called kappa as a measure of agreement between the two individuals. Change detection of optical remote sensing image disturbed. A novel remote sensing image classification scheme based. Metrics such as kappa coefficient have been demonstrated to provide. Kappa coeficient is a parameter which assesses how much better the classification is than a random classification, subject to the observed marginals in the true classes value of 0,9 means that we avoid 90% of errors using a classification algorithm instead of random classification. Interpreting a kappa coefficient is difficult due especially to the effects of prevalence and bias.
For the image classification process to be successfully, several factors should be considered including availability of quality landsat imagery and secondary data, a precise classification process and users experiences and expertise of. Pete, the use of the kappa metric is currently discouraged in the remote sensing community. One of the major characteristics of a remotely sensed image is the wavelength region it represents in the ems. A limitation of kappa is that it is affected by the prevalence of the finding under. Cohens kappa can be easily calculated using a formula and the number of true positive, false positive, false negative and true positive cases from the confusion matrix. The kappa coefficient was introduced to the remote sensing community in the early. Article good practices for assessing accuracy and estimating area of. Download limit exceeded you have exceeded your daily download allowance. I do not agree on the fact that kappa is largely considered to be more robust than oa. Abstract the weighted kappa coefficient is applied to the comparison of thematic maps.
The potential user of the kappa coefficient of agreement is cautioned that a number of remote sensing articles contain er rors in the formula for the kappa statistic or its variance. The kappa coefficient is designed for application to data arising from two independent raters and provides a measure of the degree to which they agree in labelling. Congalton for suggesting the correction for locational uncertainty. Chance agreement is, however, irrelevant in an accuracy assessment and is anyway inappropriately modelled in the calculation of. First, the optical remote sensing image is subjected to thin cloud removal processing, and then the processed remote sensing image is subjected to image change detection. Correct formulation of the kappa coefficient of agreement asprs. It however presents several limitations, which are preventing both an efficient use as well as a generalisation of its use. Cs and abc are giving very good and accurate results with a kappa coefficient value of 0. Nature inspired algorithms in remote sensing image. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same scene. The classification results were assessed using the kappa coefficient. Assessment of the irrigation capacity during the dry. This is in part because the promoted standard methods such as the kappa coefficient are not always. Tau coefficients for accuracy assessment of classification.
Lulc, landsat8, dwt, minimum distance classifier, kappa coefficient abstract. Sfm 1 where f represents a function that is not necessarily linear. However, since there should be a positive correlation between the remotely sensed classification and the reference data, positive kappa values are expected. The kappa coefficient is a statistical measure of interrater reliability or agreement that is used to assess qualitative documents and determine agreement between two raters. We introduce the tau coefficient, which meesures the improvement of a classification. Introduction electromagnetic waves, interacting with a medium leave a signature dependent on the composition and thermal structure of the medium. Research on the pixelbased and objectoriented methods of. A total of nine mangrove types were identified with higher overall accuracy 89.
Some of the images represent reflected solar radiation. Explaining the unsuitability of the kappa coefficient in. How are kappa and overall accuracy related with respect to. The detection of changes in optical remote sensing images under the interference of thin clouds is studied for the first time in this paper. Identifying each random points value from aerial imagery. Remote sensing is one of the tool which is very important for the production of land use and land cover maps through a process called image classification. An extension of the kappa coefficient is proposed which is appropriate for use with multiple observations per subject not necessarily an equal number and for multiple response choices per observation. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same. Beginners remote sensing course objectives this course has three main objectives. Volume 14 issue 1 journal of applied remote sensing. Tau coefficients for accuracy assessment of classification of. Spatial scale of remote sensing instrument does not match classification scheme.
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