Multi-image saliency analysis via histogram and spectral feature clustering for satellite images
Libao Zhang, Qiaoyue Sun, Jie Chen · 2016
Saliency analysis is an effective method to extract interesting target regions from satellite images. However, when the satellite image contains salient background information, it is difficult to eliminate this information accurately only using single image saliency analysis. In this paper, a novel multiimage saliency analysis (MSA) model based on multiple multispectral images clustering saliency analysis (MMCS) and panchromatic image co-occurrence histogram saliency analysis (PCHS) is proposed. We obtain the MMCS maps based on contrast principle, while effectively depressing the background information that is salient only within its own image. PCHS maps are obtained by co-occurrence histogram of panchromatic images that aims to enhance the saliency of target regions. Finally, multi-image saliency maps are computed by a novel fusion strategy, which can depress the background information and highlight the target regions. Experimental results show that the MSA model outperforms other state-of-art saliency analysis methods.