Hyperspectral Classification Using Deep Belief Networks Based on Conjugate Gradient Update and Pixel-Centric Spectral Block Features
Chen Chen, Yi Ma, Guangbo Ren
Abstract
This article describes the use of deep belief networks (DBNs) based on the conjugate gradient (CG) update algorithm for hyperspectral classification. DBNs perform two processes: unsupervised pretraining and supervised fine-tuning. The parameter update method in the fine-tuning stage plays a key role in optimizing the classification model. The proposed method employs CG-based fine-tuning to avoid the “zig-zagging” problem with the gradient descent algorithm and to accelerate the DBN convergence. First, the spectral features and pixel-centric spectral block features are extracted from hyperspectral images for use as the input vectors. The update variables are then calculated based on a CG algorithm and the 2-norm, and the parameters are updated during the backpropagation step of the proposed CGDBN. Two models with different CG methods are applied to a public hyperspectral image benchmark for classification experiments and analysis, and the results are compared with those from several classification methods that are currently in use. The experimental results show that the proposed classification models have advantages in terms of model convergence and low sensitivity to certain parameters. In addition, application to a hyperspectral image of coastal wetlands in the Yellow River Delta produces a satisfactory classification. The results of this study demonstrate that the proposed CG-update-based DBN provides a new approach for hyperspectral dataset classification.