Sub-pixel mapping of remotely sensed imagery based on maximum a posteriori estimation and fuzzy ARTMAP neural network

Mixed pixels in remotely sensed imagery degrade its value in practical use. Sub-pixel mapping is a promising technique to solve this problem. It can generate a fine resolution land cover map from coarse resolution fractional images by predicting spatial locations of land cover classes at sub-pixel scale. However, accuracy is often limited. When the scale factor is large, the sub-pixel distribution is complex. The traditional methods are carried out only by the fractions of land cover and the spatial dependence theory, which cannot satisfy the requirement of more accurate sub-pixel mapping. In this paper, a new observation model based on maximum a posteriori (MAP) estimation is proposed to improve the resolution of fractional images, followed by a fuzzy ARTMAP neural network to acquire a final sub-pixel mapping result. The proposed model is tested by a real remote sensed imagery, which can confirm the proposed method has better performance than the traditional algorithm, when the scale factor is large.