Hyperspectral Image Classification Using Factor Analysis and Convolutional Neural Networks

Abstract

Hyperspectral image sensors can provide valuable data for land covers, oceans, and the earth atmosphere at various spatial and spectral scales. Rich spectral and spatial information of a location makes hyperspectral image (HSI) an excellent way to work with materials, identify them, or define their properties. However, computer-automated analysis and classification of hyperspectral image is a challenging problem. Most of the spectral information in hyperspectral image is correlated, containing redundant information. High number of bands in input image contributes to the curse of dimensionality problem that reduces classifier performance. In many applications, the amount of labelled hyperspectral data that can be acquired is minimal. The complexities associated with HSI motivate us to propose a method named FA-CNN. We have used factor analysis (FA) dimension reduction technique to remove band correlation while maintaining useful spectral information in a lower number of bands. Then, we have applied convolutional neural network (CNN) for combining spectral and spatial features of HSI. Finally, multilayer perceptron classifier is used for classifying each of the input pixels in HSI. Our proposed method achieved 99.59% overall accuracy and 99.75% average accuracy on Indian Pines dataset; 99.95% overall accuracy and 99.90% average accuracy on Pavia University dataset while requiring a lower number of trainable parameters and training data compared to other methods.