An Image-Level Classification Framework for Hyperspectral Image with CNNs

Abstract

Hyperspectral images include richer spectral and spatial information than common images, which are widely used in military, agricultural fields, etc. With the development of sensor technology, the spatial resolution and spectral resolution of hyperspectral images have been improved significantly. However, the disadvantage that there may contain only one part of one object which has different spectral information in hyperspectral images. This will lead to unsatisfactory performance in traditional pixel-level hyperspectral image classification. Thus, a new hyperspectral image classification framework based on convolutional neural network is proposed. First, band selection is adopted to obtain multiple sets of false color images for small sample hyperspectral data. Then, parallel CNNs are introduced to get the classification results of different band combinations. Finally, statistical analysis strategy is performed to obtain the final output result. Experiments show that the classification accuracy of this method is better than that of the previous algorithm on the same dataset.