Abstract
Pixels, blocks (i.e., grouping of pixels), and polygons are the fundamental choices for use as assessment units for validating per-pixel image classification. Previous research conducted by the authors of this paper focused on the analysis of the impact of positional accuracy when using a single pixel for thematic accuracy assessment. The research described here provided a similar analysis, but the blocks of contiguous pixels were chosen as the assessment unit for thematic validation. The goal of this analysis was to assess the impact of positional errors on the thematic assessment. Factors including the size of a block, labeling threshold, landscape characteristics, spatial scale, and classification schemes were also considered. The results demonstrated that using blocks as an assessment unit reduced the thematic errors caused by positional errors to under 10% for most global land-cover mapping projects and most remote-sensing applications achieving a half-pixel registration. The larger the block size, the more the positional error was reduced. However, there are practical limitations to the size of the block. More classes in a classification scheme and higher heterogeneity increased the positional effect. The choice of labeling threshold depends on the spatial scale and landscape characteristics to balance the number of abandoned units and positional impact. This research suggests using the block of pixels as an assessment unit in the thematic accuracy assessment in future applications.
Highlights
Land cover is a fundamental variable to depict the earth’s physical surface, which has been extensively used in ecological, agricultural, and environmental modeling [1]
The first two rows show the results of applying a block size of 3 × 3 pixels, while the last two rows display the results of the block size of 5 × 5 pixels
What is the remaining positional effect utilizing a block as the assessment unit combined with multiple labeling rules? Second, how do these results differ when altering the spatial pattern of landscape, the classification scheme, and the spatial scale of classification map? The results were examined in terms of overall accuracy (OA)-error and abandoned proportion of blocks (APB)
Summary
Land cover is a fundamental variable to depict the earth’s physical surface, which has been extensively used in ecological, agricultural, and environmental modeling [1]. The results of the comparison are reflected in an error matrix in which a number at the ith row and jth column indicates how many samples are classified as label i but belong to reference label j [8,12] Accuracy measures such as overall accuracy (OA), kappa coefficient (Kappa), user’s accuracy (Ua), and producer’s accuracy (Pa) can be estimated from the error matrix [10]. Implementing the validation introduces many uncertainties, such as choosing the assessment unit, sampling errors, inaccessible samples, and positional errors [18,19]. These uncertainties would significantly deteriorate the derived thematic accuracies and subsequently reduce the usefulness and applicability of the land-cover products [14,20]
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