Optimum Spatial Resolution for Multispectral Classification

Abstract

Optimum spatial resolution which makes the heighest classification accuracy is determined from spatial frequency components, spectral features of objects and classification method.
Since variance of pixels correspond to that in the feature space increases in accordance with improvement of spatial resolution, classification accuracy will be gotten worse in accordance with improvement of spatial resolution under the limitations of variety of objects and class categories.
On the other hand, classification accuracy get better in accordance with improvement of spatial resolution because of decreasing of a ratio of "mixels" which are pixels composing with plural class categories. Since aforementioned two effects contribute to classification accuracy multiplicatively, it seems that there exist an optimum spatial resolution.
First, in this paper, based on the relationship between variance of pixels and classification accuracy, classification accuracy for MSS images with various Instantaneous Field of View(IFOV) will be shown. In their connection, variance of pixel values for images with various IFOV will be clarified.
Second, assuming the shape of boundary line between adjacent categories is circle, relationship among IFOV, ratio of mixels and classification accuracy will be cleared under the supposition that the number of mixels equals to that of misclassified pixels.
Finaly, it will be also shown that aforementioned relationships and optimum spatial resolution have been confirmed by using airborne MSS data of Sayama district in Japan.