On hyperspectral remotely sensed image classification based on MNF and AdaBoosting

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.