A sub-pixel mapping method based on an attraction model for multiple shifted remotely sensed images

Abstract

Sub-pixel mapping is a technique designed to obtain the spatial distribution of different classes in mixed pixels at the sub-pixel scale by transforming fraction images into a classification map. Traditional sub-pixel mapping algorithms only utilize a low-resolution image, and sub-pixel mapping is an ill-posed problem as information in a single low-resolution image is not enough to obtain a high-resolution land-cover map. The accuracy can be improved by incorporating auxiliary datasets to provide more land-cover information, such as multiple shifted images from the same area. In this paper, an attraction model is used to utilize multiple shifted remotely sensed images which have complementary information to each other at the sub-pixel scale. For multiple shifted images, one is selected as the base image, and the shifts of the other images and the base image can be calculated. The improved attraction model can obtain the impacts of the base image and auxiliary images, respectively, and integrate them to achieve a better result. The proposed algorithm was tested on synthetic real imagery, and the experimental results demonstrate that the proposed approach outperforms two traditional sub-pixel mapping algorithms based on a single image, and the another multiple shifted images based sub-pixel mapping method.