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论文类型：期刊论文

发表时间：2018-05-01

发表刊物：MULTIMEDIA TOOLS AND APPLICATIONS

收录刊物：SCIE

卷号：77

期号：9

页面范围：10419-10436

ISSN号：1380-7501

关键字：Hyperspectral image classification; KNN; Spectral-spatial; Joint model

摘要：Hyperspectral image (HSI) classification is a very active research topic in remote sensing and has numerous potential applications. This paper presents a simple but effective classification method based on spectral-spatial information and K-nearest neighbor (KNN). To be specific, we propose a spectral-spatial KNN (SSKNN) method to deal with the HSI classification problem, which effectively exploits the distances all neighboring pixels of a given test pixel and training samples. In the proposed SSKNN framework, a set-to-point distance is exploited based on least squares and a weighted KNN method is used to achieve stable performance. By using two standard HSI benchmark, we evaluate the proposed method by comparing it with eight competing methods. Both qualitative and quantitative results demonstrate our SSKNN method achieves better performance than other ones.