Sub-pixel intelligence mapping considering spatial-temoporal attraction for remote sensing imagery

Mixed pixel is a ubiquitous phenomenon in remotely sensed imagery, especially in moderate and low spatial resolution imagery, which compromise the hard land cover classification since the dominant class will shadow the information of other vulnerable classes, bringing trouble to imagery interpretation. Since the past decades, sub-pixel mapping (SPM) approaches were developed to deal with the mixture problem, on the basis of soft classification, to retrieval the pure components and its geospatial distribution within mixed pixels. Recently, SPM integrated with auxiliary information is gradually been a state-of-the-art method for mixed pixel problem, and has been proved effectively. However, few works has been dedicated to explore the geostatistic inter-correlation between spatial and temporal among the time sequences imageries. In this paper, a novel SPM algorithm based on swarm intelligence theory, considering spatiotemporal geographical attraction among multi-temporal imageries, called spatiotemporal attraction based sub-pixel evolution mapping (SASEM), is proposed for remote sensing imagery, Experiments were carried out to verify the proposed algorithm, and the result illustrate that the proposed algorithm outperform the traditional SPM, achieving a fine spatial resolution thematic map for further applications.