Hyperspectral Image Classification Based on Convolutional Neural Network

The deep learning is of great interest recently. MIT Technology Review ranked deep learning as the top 10 technolog y breakthrough in 2013. The multi-layered structure of Deep Lear ning Network enables it to learn deeper features of data and impr ove the accuracy of data identification and classification. Hypers pectral image classification technology is the most important rese arch direction in hyperspectral remote sensing, and has been wid ely used in military and civil fields. It has become a trend to appl y deep learning to hyperspectral image classification. But deep Ie arning is data-driven and requires a lot of data, however, the hyp erspectral data we deal with is a small data set. In the classificatio n of small data sets, compared to the traditional classification met hods, classification of the deep learning is more effective. In this p aper, we study the convolution neural network which is excellent in the field of image processing, and compare the effect of hypers pectral image classification with the traditional Classification met hod

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