科研之友
微信
新浪微博
Facebook
分享链接
摘要
In this letter, a novel method using both spectral and spatial information is proposed for hyperspectral image classification. Image pixels are partitioned into two sets: a labelled set and an unlabelled set. The goal of this method is to label all the unlabelled pixels. The proposed technique consists of two steps. In the first step, a similarity-based model, in the spectral domain, computes the probability that an unlabelled pixel has the same label as a labelled pixel. In order to improve the classification accuracy, we provide a powerful way to account for spatial information in the second step. Evaluation of the developed method is done on hyperspectral images. Experimental results are compared with those obtained using other hyperspectal image classification methods. The proposed approach performs better than the other ones in terms of classification accuracy.
- 出版日期2014-2-1
- 单位江南大学
