Hyperspectral Image Classification Using CNN-Enhanced Multi-Level Haar Wavelet Features Fusion Network
Wenhui Guo, Guixun Xu, Baodi Liu, Yanjiang Wang
Abstract
Convolutional neural networks (CNNs) are widely utilized in hyperspectral image (HSI) classification due to their powerful capability to automatically learn features. However, ordinary CNN mainly captures the spatial characteristics of HSI and ignores the spectral information. To alleviate the issue, this work proposes a CNN-enhanced multi-level Haar wavelet features fusion network (CNN-MHWF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> N), which combines the spatial features obtained through 2-D-CNN with the Haar wavelet decomposition features to obtain sufficient spectral–spatial features. Specifically, factor analysis is first used to reduce the HSI dimension. Then, four-level decomposition features are obtained through the Haar wavelet decomposition algorithm, which of them are, respectively, concatenated with four-layer convolution features for combining spatial with spectral information. In this way, spectral–spatial features achieve better information interaction. Besides, a double filtrating feature fusion module is designed, which is operated following each level spectral–spatial features to obtain finer characteristics. Finally, those recognizable features are merged via a fusion operator. The whole designed model is conducive to enhancing the final HSI classification performance. In addition, experiments also reveal that the designed model is superior on three benchmark databases compared with the state-of-the-art approaches.