Developing a general post-classification framework for land-cover mapping improvement using high-spatial-resolution remote sensing imagery
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发布时间：2020-05-30
发表刊物：Remote Sensing Letters
摘要：In this letter, a general post-classification framework (GPCF) is proposed to enhance initial results. Traditional post-classification techniques usually improve classification accuracy by considering the contextual information in a single classified image. In contrast to traditional techniques, the proposed GPCF aims to integrate multi-source classified images obtained through different classification approaches. In the proposed framework, the label of a central pixel is determined by its surrounding voting in each classified image. In this manner, the GPCF can integrate the advantages of dif
合写作者：Lv Z Y, Zhang Xubing*, Benediktsson J
通讯作者：张绪冰
是否译文：否
收录刊物：SCI