Abstract:The use of multi-source remote sensing data to obtain urban impervious surface has become a popular research topic. Multi-source remote sensing data fusion techniques can provide object interpretation with a higher accuracy. However, most decision-level fusion methods make insufficient use of the complementary information and degree of association between similar object data. To fill this gap, in this paper, we propose a dual-view learning fusion classification method (DvLF) based on multi-view learning. First… Show more
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