Land use and land cover classification base on image saliency map cooperated coding

The land use and land cover classification is an important and hot research topic in remote sensing image processing. How to use information effectively in remote sensing data to categorize different land use and land cover scenes needs urgent attention. In this paper, we analysis the Bag-of-Word model based feature extracting method systematically, and propose the Saliency Map Cooperated (SMC) coding strategy according characters of remote sensing images. The proposed SMC takes into account both the primary objects and partial of large scale objects in remote sensing images, with little effecting on texture dominated images. Extensive experimental results show the efficiency of the proposed SMC coding strategy.