Abstract
Validating or accessing the accuracy of soft classification maps has rapidly developed over the past few years. This assessment employs a soft error matrix as generalized from the traditional, hard classification error matrix. However, the impact of positional error on the soft classification is uncertain and whether the well-accepted half-pixel registration accuracy is suitable for the soft classification accuracy assessment is unknown. In this paper, a simulation analysis was conducted to examine the influence of positional error on the overall accuracy (OA) and kappa in soft classification accuracy assessment under different landscape conditions (i.e., spatial characteristics and spatial resolutions). Results showed that with positional error ranging from 0 to 3 soft pixels, the OA-error varied from 0 to 44.6 percent while the kappa-error varied from 0 to 93.7 percent. Landscape conditions with smaller mean patch size (MPS) and greater fragmentation produced greater positional error impact on the accuracy measures at spatial resolutions of 1 and 2 unit distances. However, this trend did not hold for spatial resolutions of 5 and 10 unit distances. A half of a pixel was not sufficient to keep the overall accuracy error and kappa error under 10 percent. The results indicate that for soft classification accuracy assessment the requirement for registration accuracy is higher and depends greatly on the landscape characteristics. There is a great need to consider positional error for validating soft classification maps of different spatial resolutions.
Highlights
In the past few decades, earth observation satellites (EOS) have provided large amounts of remotely sensed data depicting the earth’s surface at a variety of spatial and temporal scales
The bottom abscissa value is the relative distance based on the soft pixel associated with a given spatial resolution while the top abscissa value is the absolute distance based on the unit distance corresponding to the relative distance
This paper conducted an analysis of the impact of positional error on thematic accuracy for soft accuracy assessment using simulated maps of various landscape patterns at different spatial resolutions
Summary
In the past few decades, earth observation satellites (EOS) have provided large amounts of remotely sensed data depicting the earth’s surface at a variety of spatial and temporal scales. Global or regional land cover maps from these remotely sensed data are typically based on image classification and a great number of classification methods have been developed, ranging from classical ones such as minimum distance [1] to more advanced ones such as support vector machine [2]. The hard classification methods classify the remotely sensed data into maps where each pixel belongs to a single land cover/vegetation/thematic class, whereas the soft classification methods categorize each pixel into several classes simultaneously [3]. The entire pixel is no longer either right or wrong, but rather soft classification labels parts of the pixel into different classes Given this issue, much effort has been made to generalize the conventional error matrix and adapt it for use in soft classification. In practice, no map is free of positional errors and the assessment of thematic accuracy must include or incorporate some measure of positional accuracy [9,10,11]
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