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浏览/检索结果: 共1条，第1-1条 帮助 已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 On hyperspectral remotely sensed image classification based on MNF and AdaBoosting (EI CONFERENCE) 会议论文 2012 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 2012, July 16, 2012 - July 18, 2012, Shanghai, China Xu Y.; Yu P.; Guo B.; Gao X.; Guo Y. 收藏  |  浏览/下载：16/0  |  提交时间：2013/03/25 As an effective statistical learning tool  AdaBoosting has been widely used in the field of pattern recognition. In this paper  a new method is proposed to improve the classification performance of hyperspectral images by combining the minimum noise fraction (MNF) and AdaBoosting. Because the hyperspectral imagery has many bands which have strong correlation and high redundancy  the hyperspectral data are pre-processed by the minimum noise fraction to reduce the data's dimensionality  whilst to remove noise bands simultaneously. Then  we use an AdaBoost algorithm to conduct the classification of hyperspectral remotely sensed image. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.