Improving hyperspectral image classification through spectral-spatial feature reduction with a hybrid approach and deep learning

Abstract

ABSTRACT The Hyperspectral Image (HSI) is a great source of information for observing the earth’s elements due to its numerous narrow and continuous spectral wavelength bands. There are some key difficulties, such as the fact that the image bands are spectrally and spatially highly correlated, and the ‘curse of dimensionality’ in using the original HSI for classification. Band (dimensionality/feature) reduction is necessary to improve classification performance through feature extraction and selection. As such, this paper proposes a hybrid HSI classification paradigm, termed Hybrid-2DNET. Specifically, our proposed Hybrid-2DNET incorporates factor analysis-based feature extraction along the Minimum-Redundancy-Maximum-Relevance (mRMR)-based feature selection criterion and a 2D-wavelet Convolutional Neural Network (CNN) to reduce both spectral and spatial dimensionalities. The experimental analyses performed on three remote sensings HSI datasets, namely Indian Pines, University of Pavia, and Salinas Scene, manifest that our proposed Hybrid-2DNET outperforms the handcrafted and deep learning methods.