scholarly journals Hyperspectral Image Super-Resolution Inspired by Deep Laplacian Pyramid Network

2018 ◽  
Vol 10 (12) ◽  
pp. 1939 ◽  
Author(s):  
Zhi He ◽  
Lin Liu
Keyword(s):  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Spectral Characteristics ◽  
Super Resolution ◽  
High Spectral Resolution ◽  
Spectral Information ◽  
Laplacian Pyramid ◽  
Multiple Observations ◽  
Learned Dictionary ◽  
Coarse To Fine

Existing hyperspectral sensors usually produce high-spectral-resolution but low-spatial-resolution images, and super-resolution has yielded impressive results in improving the resolution of the hyperspectral images (HSIs). However, most of the super-resolution methods require multiple observations of the same scene and improve the spatial resolution without fully considering the spectral information. In this paper, we propose an HSI super-resolution method inspired by the deep Laplacian pyramid network (LPN). First, the spatial resolution is enhanced by an LPN, which can exploit the knowledge from natural images without using any auxiliary observations. The LPN progressively reconstructs the high-spatial-resolution images in a coarse-to-fine fashion by using multiple pyramid levels. Second, spectral characteristics between the low- and high-resolution HSIs are studied by the non-negative dictionary learning (NDL), which is proposed to learn the common dictionary with non-negative constraints. The super-resolution results can finally be obtained by multiplying the learned dictionary and its corresponding sparse codes. Experimental results on three hyperspectral datasets demonstrate the feasibility of the proposed method in enhancing the spatial resolution of the HSI with preserving the spectral information simultaneously.

scholarly journals Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation

2019 ◽  
Vol 11 (10) ◽  
pp. 1229 ◽  
Author(s):  
Jing Hu ◽  
Minghua Zhao ◽  
Yunsong Li
Keyword(s):  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Input Image ◽  
High Spectral Resolution ◽  
Spectral Information ◽  
Spectral Correlation ◽  
Fusion Operation ◽  
Image Super Resolution ◽  
Sr Method

Limited by the existing imagery sensors, hyperspectral images are characterized by high spectral resolution but low spatial resolution. The super-resolution (SR) technique aiming at enhancing the spatial resolution of the input image is a hot topic in computer vision. In this paper, we present a hyperspectral image (HSI) SR method based on a deep information distillation network (IDN) and an intra-fusion operation. Specifically, bands are firstly selected by a certain distance and super-resolved by an IDN. The IDN employs distillation blocks to gradually extract abundant and efficient features for reconstructing the selected bands. Second, the unselected bands are obtained via spectral correlation, yielding a coarse high-resolution (HR) HSI. Finally, the spectral-interpolated coarse HR HSI is intra-fused with the input HSI to achieve a finer HR HSI, making further use of the spatial-spectral information these unselected bands convey. Different from most existing fusion-based HSI SR methods, the proposed intra-fusion operation does not require any auxiliary co-registered image as the input, which makes this method more practical. Moreover, contrary to most single-based HSI SR methods whose performance decreases significantly as the image quality gets worse, the proposal deeply utilizes the spatial-spectral information and the mapping knowledge provided by the IDN, which achieves more robust performance. Experimental data and comparative analysis have demonstrated the effectiveness of this method.

scholarly journals Self-Dictionary Regression for Hyperspectral Image Super-Resolution

2018 ◽  
Vol 10 (10) ◽  
pp. 1574 ◽  
Author(s):  
Dongsheng Gao ◽  
Zhentao Hu ◽  
Renzhen Ye
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Cross Correlation ◽  
Hyperspectral Image ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Image Super Resolution

Due to sensor limitations, hyperspectral images (HSIs) are acquired by hyperspectral sensors with high-spectral-resolution but low-spatial-resolution. It is difficult for sensors to acquire images with high-spatial-resolution and high-spectral-resolution simultaneously. Hyperspectral image super-resolution tries to enhance the spatial resolution of HSI by software techniques. In recent years, various methods have been proposed to fuse HSI and multispectral image (MSI) from an unmixing or a spectral dictionary perspective. However, these methods extract the spectral information from each image individually, and therefore ignore the cross-correlation between the observed HSI and MSI. It is difficult to achieve high-spatial-resolution while preserving the spatial-spectral consistency between low-resolution HSI and high-resolution HSI. In this paper, a self-dictionary regression based method is proposed to utilize cross-correlation between the observed HSI and MSI. Both the observed low-resolution HSI and MSI are simultaneously considered to estimate the endmember dictionary and the abundance code. To preserve the spectral consistency, the endmember dictionary is extracted by performing a common sparse basis selection on the concatenation of observed HSI and MSI. Then, a consistent constraint is exploited to ensure the spatial consistency between the abundance code of low-resolution HSI and the abundance code of high-resolution HSI. Extensive experiments on three datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

scholarly journals Domain Transfer Learning for Hyperspectral Image Super-Resolution

2019 ◽  
Vol 11 (6) ◽  
pp. 694 ◽  
Author(s):  
Xiaoyan Li ◽  
Lefei Zhang ◽  
Jane You
Keyword(s):  
High Resolution ◽  
Transfer Learning ◽  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Image Super Resolution ◽  
Domain Transfer ◽  
Domain Transfer Learning

A Hyperspectral Image (HSI) contains a great number of spectral bands for each pixel; however, the spatial resolution of HSI is low. Hyperspectral image super-resolution is effective to enhance the spatial resolution while preserving the high-spectral-resolution by software techniques. Recently, the existing methods have been presented to fuse HSI and Multispectral Images (MSI) by assuming that the MSI of the same scene is required with the observed HSI, which limits the super-resolution reconstruction quality. In this paper, a new framework based on domain transfer learning for HSI super-resolution is proposed to enhance the spatial resolution of HSI by learning the knowledge from the general purpose optical images (natural scene images) and exploiting the cross-correlation between the observed low-resolution HSI and high-resolution MSI. First, the relationship between low- and high-resolution images is learned by a single convolutional super-resolution network and then is transferred to HSI by the idea of transfer learning. Second, the obtained Pre-high-resolution HSI (pre-HSI), the observed low-resolution HSI, and high-resolution MSI are simultaneously considered to estimate the endmember matrix and the abundance code for learning the spectral characteristic. Experimental results on ground-based and remote sensing datasets demonstrate that the proposed method achieves comparable performance and outperforms the existing HSI super-resolution methods.

scholarly journals Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing

2020 ◽  
Vol 10 (16) ◽  
pp. 5583 ◽  
Author(s):  
Jun Li ◽  
Yuanxi Peng ◽  
Tian Jiang ◽  
Longlong Zhang ◽  
Jian Long
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Superior Performance ◽  
High Spectral Resolution ◽  
Data Sets ◽  
Group Sparsity ◽  
Sparsity Regularization ◽  
High Spatial Resolution Image

A hyperspectral image (HSI) contains many narrow spectral channels, thus containing efficient information in the spectral domain. However, high spectral resolution usually leads to lower spatial resolution as a result of the limitations of sensors. Hyperspectral super-resolution aims to fuse a low spatial resolution HSI with a conventional high spatial resolution image, producing an HSI with high resolution in both the spectral and spatial dimensions. In this paper, we propose a spatial group sparsity regularization unmixing-based method for hyperspectral super-resolution. The hyperspectral image (HSI) is pre-clustered using an improved Simple Linear Iterative Clustering (SLIC) superpixel algorithm to make full use of the spatial information. A robust sparse hyperspectral unmixing method is then used to unmix the input images. Then, the endmembers extracted from the HSI and the abundances extracted from the conventional image are fused. This ensures that the method makes full use of the spatial structure and the spectra of the images. The proposed method is compared with several related methods on public HSI data sets. The results demonstrate that the proposed method has superior performance when compared to the existing state-of-the-art.

scholarly journals Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  
Keyword(s):  
Land Cover ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Fuel Mapping ◽  
Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

scholarly journals Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution

2020 ◽  
Vol 12 (10) ◽  
pp. 1660 ◽  
Author(s):  
Qiang Li ◽  
Qi Wang ◽  
Xuelong Li
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Super Resolution ◽  
Superior Performance ◽  
Spectral Information ◽  
Great Success ◽  
Convolutional Network ◽  
Training Time ◽  
Image Super Resolution

Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, there are two main problems in the previous works. One is to use the typical three-dimensional convolution analysis, resulting in more parameters of the network. The other is not to pay more attention to the mining of hyperspectral image spatial information, when the spectral information can be extracted. To address these issues, in this paper, we propose a mixed convolutional network (MCNet) for hyperspectral image super-resolution. We design a novel mixed convolutional module (MCM) to extract the potential features by 2D/3D convolution instead of one convolution, which enables the network to more mine spatial features of hyperspectral image. To explore the effective features from 2D unit, we design the local feature fusion to adaptively analyze from all the hierarchical features in 2D units. In 3D unit, we employ spatial and spectral separable 3D convolution to extract spatial and spectral information, which reduces unaffordable memory usage and training time. Extensive evaluations and comparisons on three benchmark datasets demonstrate that the proposed approach achieves superior performance in comparison to existing state-of-the-art methods.

scholarly journals Hyperspectral Image Super-Resolution via Adaptive Dictionary Learning and Double l1 Constraint

2019 ◽  
Vol 11 (23) ◽  
pp. 2809 ◽  
Author(s):  
Tang ◽  
Xu ◽  
Huang ◽  
Huang ◽  
Sun
Keyword(s):  
Sparse Representation ◽  
Spatial Resolution ◽  
Quality Evaluation ◽  
Hyperspectral Image ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
Training Data ◽  
Evaluation Indexes ◽  
Image Super Resolution ◽  
The Relationship

Hyperspectral image (HSI) super-resolution (SR) is an important technique for improving the spatial resolution of HSI. Recently, a method based on sparse representation improved the performance of HSI SR significantly. However, the spectral dictionary was learned under a fixed size, empirically, without considering the training data. Moreover, most of the existing methods fail to explore the relationship among the sparse coefficients. To address these crucial issues, an effective method for HSI SR is proposed in this paper. First, a spectral dictionary is learned, which can adaptively estimate a suitable size according to the input HSI without any prior information. Then, the proposed method exploits the nonlocal correlation of the sparse coefficients. Doubleregularized sparse representation is then introduced to achieve better reconstructions for HSI SR. Finally, a high spatial resolution HSI is generated by the obtained coefficients matrix and the learned adaptive size spectral dictionary. To evaluate the performance of the proposed method, we conduct experiments on two famous datasets. The experimental results demonstrate that it can outperform some relatively state-of-the-art methods in terms of the popular universal quality evaluation indexes.

scholarly journals Sensor Simulation based Hyperspectral Image Enhancement with Minimal Spectral Distortion

2014 ◽  
Vol II-8 ◽  
pp. 179-185 ◽  
Author(s):  
A. Khandelwal ◽  
K. S. Rajan
Keyword(s):  
Image Enhancement ◽  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Rank Correlation ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Composition Analysis ◽  
Remotely Sensed Data ◽  
Multispectral Data ◽  
Spectral Signatures

In the recent past, remotely sensed data with high spectral resolution has been made available and has been explored for various agricultural and geological applications. While these spectral signatures of the objects of interest provide important clues, the relatively poor spatial resolution of these hyperspectral images limits their utility and performance. In this context, hyperspectral image enhancement using multispectral data has been actively pursued to improve spatial resolution of such imageries and thus enhancing its use for classification and composition analysis in various applications. But, this also poses a challenge in terms of managing the trade-off between improved spatial detail and the distortion of spectral signatures in these fused outcomes. This paper proposes a strategy of using vector decomposition, as a model to transfer the spatial detail from relatively higher resolution data, in association with sensor simulation to generate a fused hyperspectral image while preserving the inter band spectral variability. The results of this approach demonstrates that the spectral separation between classes has been better captured and thus helped improve classification accuracies over mixed pixels of the original low resolution hyperspectral data. In addition, the quantitative analysis using a rank-correlation metric shows the appropriateness of the proposed method over the other known approaches with regard to preserving the spectral signatures.

scholarly journals Blind Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition

2021 ◽  
Author(s):  
Marzieh Zare
Keyword(s):  
Spatial Resolution ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Super Resolution ◽  
Tensor Decomposition ◽  
Multispectral Image ◽  
Tensor Factorization ◽  
Structure Information ◽  
Joint Structure ◽  
Art Methods

Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI) to produce a fused high spatio-spectal resolution one, referred to as HSI super-resolution, has recently attracted increasing research interests. In this paper, a new method based on coupled non-negative tensor decomposition (CNTD) is proposed. The proposed method uses tucker tensor factorization for low resolution hyperspectral image (LR-HSI) and high resolution multispectral image (HR-MSI) under the constraint of non-negative tensor ecomposition (NTD). The conventional non-negative matrix factorization (NMF) method essentially loses spatio-spectral joint structure information when stacking a 3D data into a matrix form. On the contrary, in NMF-based methods, the spectral, spatial, or their joint structures must be imposed from outside as a constraint to well pose the NMF problem, The proposed CNTD method blindly brings the advantage of preserving the spatio-spectral joint structure of HSIs. In this paper, the NTD is imposed on the coupled tensor of HIS and MSI straightly. Hence the intrinsic spatio-spectral joint structure of HSI can be losslessly expressed and interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled by means of the core tensor of the Tucker tensor decomposition. The proposed method is completely straight forward and easy to implement. Unlike the other state-of-the-art methods, the complexity of the proposed CNTD method is quite linear with the size of the HSI cube. Compared with the state-of-the-art methods experiments on two well-known datasets, give promising results with lower complexity order.

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