Developing a general post-classification framework for land-cover mapping improvement using high-spatial-resolution remote sensing imagery

作者：Lv, ZhiYong^*; Zhang, Xubing^*; Benediktsson, Jon Atli

来源：Remote Sensing Letters, 2017, 8(7): 607-616.

DOI：10.1080/2150704X.2017.1306137

• CONTEXTUAL INFORMATION
• MORPHOLOGICAL PROFILES
• HYPERSPECTRAL IMAGES
• EXTRACTION
• FUSION

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 different classification approaches. In our experiments, a hyperspectral image and an aerial image with high spatial resolution (HSR) are used to evaluate the proposed GPCF. Compared with two relevant post-classification approaches, the proposed framework can provide a land-cover map with lower noise in visual comparison and achieve higher classification accuracies. Therefore, the proposed GPCF presents better performance in HSR image classification.

• 出版日期2017
• 单位西安理工大学; 中国地质大学（北京）