Infrared ultraspectral signature classification based on a restricted Boltzmann machine with sparse and prior constraints

摘要

The state-of-the-art ultraspectral technology brings a new hope for the high precision applications due to its high spectral resolution. However, it comes with new challenges brought by the improvement of spectral resolution such as the Hughes phenomenon and over-fitting issue, and our work is aimed at addressing these problems. As new Markov random field (MRF) models, the restricted Boltzmann machines (RBMs) have been used as generative models for many different pattern recognition and artificial intelligence applications showing promising and outstanding performance. In this article, we propose a new method for infrared ultraspectral signature classification based on the RBMs, which adopt the regularization-based techniques to improve the classification accuracy and robustness to noise compared to traditional RBMs. First, we add an arctan-like term to the objective function as a sparse constraint to improve the classification accuracy. Second, we utilize a Gaussian prior to avoid the over-fitting problem. Third, to further improve the classification performance, a multi-layer RBM model, a deep belief network (DBN), is adopted for infrared ultraspectral signature classification. Experiments using different spectral libraries provided by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Environmental Protection Agency (EPA) were performed to evaluate the performance of the proposed method by comparing it with other traditional methods, including spectral coding-based classifiers (binary coding (BC), spectral feature-based binary coding (SFBC), and spectral derivative feature coding (SDFC) matching methods), a novel feature extraction method termed crosscut feature extraction matching (CF), and three machine learning methods (artificial deoxyribonucleic acid (DNA)-based spectral matching (ADSM), DBN, and sparse deep belief network (SparseDBN)). Experimental results demonstrate that the proposed method is superior to the other methods with which it was compared and can simultaneously improve the accuracy and robustness of classification.