Journal Article

Hyperspectral and Multispectral Image Fusion via Deep Two-Branches Convolutional Neural Network

Yang, Jingxiang, Chan, Jonathan Cheung-Wai, Zhao, Yong-Qiang

2018

Overview

Enhancing the spatial resolution of hyperspectral image (HSI) is of significance for applications. Fusing HSI with a high resolution (HR) multispectral image (MSI) is an important technology for HSI enhancement. Inspired by the success of deep learning in image enhancement, in this paper, we propose a HSI-MSI fusion method by designing a deep convolutional neural network (CNN) with two branches which are devoted to features of HSI and MSI. In order to exploit spectral correlation and fuse the MSI, we extract the features from the spectrum of each pixel in low resolution HSI, and its corresponding spatial neighborhood in MSI, with the two CNN branches. The extracted features are then concatenated and fed to fully connected (FC) layers, where the information of HSI and MSI could be fully fused. The output of the FC layers is the spectrum of the expected HR HSI. In the experiment, we evaluate the proposed method on Airborne Visible Infrared Imaging Spectrometer (AVIRIS), and Environmental Mapping and Analysis Program (EnMAP) data. We also apply it to real Hyperion-Sentinel data fusion. The results on the simulated and the real data demonstrate that the proposed method is competitive with other state-of-the-art fusion methods.

• Publisher: MDPI AG
• Subject: Artificial neural networks / AVIRIS / Computer simulation / Computer vision / convolutional neural network / Data integration / Data processing / deep learning / Feature extraction / fusion / hyperspectral / Image enhancement / Image processing / Image resolution / Infrared analysis / Infrared imaging / Infrared spectrometers / Machine learning / Multisensor fusion / multispectral / Neural networks / Remote sensing / Spatial resolution / Spectral correlation