This tutorial demonstrates how a confusion matrix is created to assess the accuracy of an image classification. On your confusion matrix, you can see that classes 5 and 6 are always wrong and class 2 is not very reliable. Estimating classification accuracy using arcgis youtube. This contingency table is necessary for pearsons chisquare test. I have a confusion matrix, from which i would like to calulate the producers and users accuracy, and also get the overall kappa and aic value. Accuracy assessment of satellite remote sensing for. Article pdf available in international journal of remote sensing. Although it may appear simple in concept, accuracy is a difficult property to measure and express. What will be accomplished with the assessmentselect a method confidencebuilding assessment. The authors present data quality features of welldefined and poorly defined object. Pdf accuracy assessment and validation of remotely sensed and. In predictive analytics, a table of confusion sometimes also called a confusion matrix, is a table with two rows and two columns that reports the number of false positives, false negatives, true positives, and true negatives. Classification accuracy is assessed by comparing ground truth classes with the predicted pixel class at each ground truth location. Potential users have to know about the reliability of the data when confronted with maps derived from remote sensing data.
Spatial analysis of remote sensing image classification. The derivation of global estimates from a confusion matrix. The total number of values is the number of values in either the truth or predictedvalue arrays. Map products, derived from remote sensing are usually critical components of a gis. In the example confusion matrix, the overall accuracy is computed as follows. The confusion matrix provides no information on the spatial distribution of errors.
Accuracy dimensions in remote sensing the international. Subpixel confusion uncertainty matrix for assessing soft. I am aware that r has several packages that allow the calulation of cohens kappa statistic, however i cannot find any which handles a confusion matrix or maybe i have not searched extensively enough. We then constructed a confusionerror matrix to analyze accuracy. The strength of a confusion matrix is that it identifies the nature of. Status of land cover classification accuracy assessment. Error matrix confusion matrix compares ground truth data with results of classification. Remote sensing literatu re is a good place to look, especially since this is a common method of accuracy assessment on remote sensing derived classific ations with unbiased ground reference information. How to create confusion matrix for change detection with remote sensing data. The confusion matrix of accuracy assessment was shown in table 3 glcm. Accuracy assessment is an important step in the process of analyzing remote sens ing data. Consequently, this study addresses the hypothesis that remote sensing classification of. The study had an overall classification accuracy of 81. Accuracy assessment model for classification result of.
So far, i have learnt numerous basic components of remote sensing technique, image processing, interpretation and analysis, and i want to learn more about remote sensing applications and to see how these components are involved in an application. Pdf a generalized confusion matrix for assessing area estimates. A python library for remote sensing including a new way to handle rasters journal of open source. We used confusion matrices, the kappa statistic and overall mapping accuracy congalton 2001 to assess the accuracy of image classifications. Just to show you that the random points generated using arcgis are indeed random, we completed a 2. While a confusion matrix contains all information of the outcome of a classifier, they are rarely used for reporting results in bci field because they are difficult to compare and discuss.
Kappa coeficient is a parameter which assesses how much better. On the contrary, users accuracy, producers accuracy, and overall accuracy should be recommended they are directly because interpretable as probabilities of correct classification. Interpreting errors in classified map remotelysensed data cannot capture classes classes are land use, not land cover classes not spectrally separable atmospheric effects mask subtle differences spatial scale of remote sensing instrument does. Geographically weighted models were used to map boolean and fuzzy accuracy. Expanded roles and goals for accuracy assessment are pos sible based on changes in the assumptions regarding the properties of. The spatial distribution of correspondence provides richer accuracy information. 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. The correctly classified values are located along the upperleft to lowerright diagonal of the confusion matrix. How to create confusion matrix for change detection with. At the top of the window, go to customize extensions 4. Attention based network for remote sensing scene classification shaoteng liu 1, qi wang.
Remote sensing products can serve as the basis for political as well as economical decisions. Remote sensing is an important technique to study both spatial and temporal phenomena monitoring. 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. National forest monitoring assessment working paper no.
The central part of the study is the land cover mapping, describing its. Click the check mark for spatial analyst first, we will load the classified image. Here is another paper by foody in which talks about the problems in accuracy assessment. Besides the continuing criticism in remote sensing literature, much more discussions can be found in psychologand sociology literature y where kappa originated. Remote sensing literatu re is a good place to look, especially since this is a common method of accuracy assessment on remotesensingderived classific ations with unbiased ground reference information. Abstract this letter suggests a way in which a confusion matrix can be used to correct image classification results to provide estimates of area statistics under different cover types. Narayana reddy published on 20191210 download full article with reference data and citations.
Accuracy assessment goals portland state university. The practical use of the scm and derived indices was demonstrated in assessing an invasive species detection method and a fuzzy classification of urban land useland cover through remote sensing. Table of contents and the random points to the map document. Accuracy assessment of supervised and unsupervised. Notice, the confusion matrix is also set up in a similar manner as a statistical contingency table. Before the goodnessoffit test that the chisquare calculates can be completed, observed and expected frequencies must be calculated for insertion into the formula.
Experience with gis, working with raster and point data, as well as in. Manual for remote sensing image analysis in r including. Remote sensing and gis accuracy assessment request pdf. Match spatial scale of reference plots and remotely. In a confusion matrix, your classification results are compared to additional ground truth information. Instead, some parameters generally extracted from the confusion matrix are commonly used. This document provides the methodology and practical implementation for the procedure for estimating area and assessing accuracy of a land cover map from a single period in time or for change between two time. In contrast, in the case of occ the full confusion matrix cannot be derived from the reference data available during the training stage because labeled samples are only available for the class of interest, i.
How are kappa and overall accuracy related with respect to. To assess the accuracy of an image classification, it is common practice to create a confusion matrix. This group of statistics differs from overall accuracy since it takes into account the socalled chance. Remote sensing and gis have made huge impacts on how those in the agricultural planners are monitoring and managing croplands, and predicting biomass or yields. Accuracy assessment of land useland cover classification. How to create a confusion matrix using the output of crossval function in matlab svm classifier. Users accuracy 110 115 96% is the proportion of the pixels of class i which have been correctly classified on the total pixel assigned to the class i ground truth reference data c l a s s i f i c a t i o n class forest field water urban. Lewis and mike brown, journalinternational journal of remote sensing, year2001, volume22, pages3223 3235.
Accuracy assessment is performed by comparing the map created by remote sensing analysis to a reference map based on a different information source. The remote sensing activities within this project aims at the following three main. Pdf a generalized confusion matrix for assessing area. The new matrix is referred to as subpixel confusionuncertainty matrix scm. Accuracy assessment assess accuracy of a remote sensing output is one of the most important steps in any classification exercise without an accuracy assessment the output or results is of little value 3. Error matrix for map comparison or accuracy assessment. How are kappa and overall accuracy related with respect to thematic raster data. Jensen 1996 is the reference applied to this example fo r testing the relationship between two models. Although remote sensing has been extensively used to detect wetlands, few studies have been conducted that examine the accuracy of satellite remote sensing methods to identify and classify wetlands as small as vernal pools. A generalized confusion matrix for assessing area estimates from remotely sensed data. Accuracy assessment of supervised and unsupervised classification using noaa data in andhra pradesh region written by m. Accuracy assessment of an image classification in arcmap. Subpixel accuracy measures were also derived from this matrix.
Fuzzy land information from environmental remote sensing aims to increase the accuracy of land cover. Accuracy assessment geography 581 mather p245249 jenny mckay accuracy assessment the comparison of a classification with groundtruth data to evaluate how well the classification represents the real world. This allows more detailed analysis than mere proportion of correct classifications accuracy. An asymmetric confusion matrix can reveal a biased classifier. Status of land cover classification accuracy assessment ucl. Stehmanb a department of environmental resources engineering, state university of new york, college of environmental science and forestry, 1 forestry drive, syracuse, ny 210, united states b department of forest and natural resources management, state university of. One might ask why the remote sensing analysis is needed if the reference map to compare it to already exists.
434 355 1526 858 326 1248 600 1433 452 23 751 342 1009 1283 1064 134 1413 451 907 707 13 63 316 996 1524 645 988 122 838 829 522 927 691