Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery

Abstract

Mixed pixels are widely existent in remote-sensing imagery. Although the proportion occupied by each class in mixed pixels can be determined by spectral unmixing, the spatial distribution of classes remains unknown. Sub-pixel mapping (SPM) addresses this problem and a sub-pixel/pixel spatial attraction model (SPSAM) has been introduced to realize SPM. However, this algorithm fails to adequately consider the correlation between sub-pixels. Consequently, the SPM results created by SPSAM are noisy and the accuracy is limited. In this article, a method based on particle swarm optimization is proposed as post-processing on the SPM results obtained with SPSAM. It searches the most likely spatial distribution of classes in each coarse pixel to improve the SPSAM. Experimental results show that the proposed method can provide higher accuracy and reduce the noise in the results created by SPSAM. When compared with the available modified pixel-swapping algorithm, the proposed method often yields higher accuracy results.