Abstract
Abstract. With the widespread national survey of geographic conditions, object-based data has already became the most common data organization pattern in the area of land cover research. Assessing the accuracy of object-based land cover data is related to lots of processes of data production, such like the efficiency of inside production and the quality of final land cover data. Therefore，there are a great deal of requirements of accuracy assessment of object-based classification map. Traditional approaches for accuracy assessment in surveying and mapping are not aimed at land cover data. It is necessary to employ the accuracy assessment in imagery classification. However traditional pixel-based accuracy assessing methods are inadequate for the requirements. The measures we improved are based on error matrix and using objects as sample units, because the pixel sample units are not suitable for assessing the accuracy of object-based classification result. Compared to pixel samples, we realize that the uniformity of object samples has changed. In order to make the indexes generating from error matrix reliable, we using the areas of object samples as the weight to establish the error matrix of object-based image classification map. We compare the result of two error matrixes setting up by the number of object samples and the sum of area of object samples. The error matrix using the sum of area of object sample is proved to be an intuitive, useful technique for reflecting the actual accuracy of object-based imagery classification result.
Highlights
Land is one of the most important resources in economy development
Based on traditional method of accuracy assessment, we propose the concept of the area weight accuracy, and we use this concept to evaluate the accuracy of object-based classification result
The purpose of accuracy assessment of classification result of land cover map is to obtain the percentage of correctly classified area, which can be described by the percentage of total number of pixels labeled to the right attribute
Summary
Land is one of the most important resources in economy development. Reasonable uses of land resources affect the mode of society and economy development. Using these high resolution remote sensing data can obtain higher levels of detailed features of land use and land cover. Many researchers believe that Object-based classification can overcome the shortcoming of high resolution image data, and can make a better use of the profusion of details to improve the accuracy of classification (Blaschke 2010, Yan, 2006). Land cover refers to every corner on the ground This situation brings out a great deal of requirements of accuracy assessment of object-based classification map. The sample units of object-based classification result are pixels (Qiu, 2010) and polygons (Grenier, 2008, Kozak, 2006). We use polygons as sample units and error matrix to assess the attribute accuracy, and consider the sampling number. We set up an error matrix without area of weight as comparison
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