Hyperspectral and Multispectral Image Fusion via Deep Two-Branches Convolutional Neural Network
TLDR
A HSI-MSI fusion method by designing a deep convolutional neural network with two branches which are devoted to features of HSI and MSI, which demonstrates that the proposed method is competitive with other state-of-the-art fusion methods.Abstract:
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.read more
Citations
More filters
Journal ArticleDOI
Deep learning in remote sensing applications: A meta-analysis and review
TL;DR: This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping, and a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.
Journal ArticleDOI
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review.
TL;DR: The present review is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields and the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectrals data from a multidisciplinary perspective.
Journal ArticleDOI
Regularizing Hyperspectral and Multispectral Image Fusion by CNN Denoiser
TL;DR: A novel HSI and MSI fusion method based on the subspace representation and convolutional neural network (CNN) denoiser, i.e., a well-trained CNN for gray image denoising, which has superior performance over the state-of-the-art fusion methods.
Journal ArticleDOI
Spatial-Spectral Structured Sparse Low-Rank Representation for Hyperspectral Image Super-Resolution
TL;DR: Wang et al. as mentioned in this paper proposed a new subspace clustering method to represent the data samples as linear combinations of the bases in a given dictionary, where the sparse structure is induced by low-rank factorization for the affinity matrix.
Proceedings ArticleDOI
Unsupervised Adaptation Learning for Hyperspectral Imagery Super-Resolution
TL;DR: This work develops a two-stage SR network that leverages two consecutive modules: a fusion module and an adaptation module, to recover the latent HSI in a coarse-to-fine scheme and introduces a simple degeneration network to assist learning both the adaptation module and the degeneration in an unsupervised way.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.