scholarly journals Improvement of a mineral discrimination method using multispectral image and surrounding hyperspectral image

2021 ◽  
Vol 15 (04) ◽  
Author(s):  
Kazuki Nakayama ◽  
Hideyuki Tonooka
Keyword(s):  
Hyperspectral Image ◽  
Multispectral Image ◽  
Discrimination Method

Research on Fusion Approach for Hyperspectral Image and Multispectral Image of HJ-1A

2011 ◽  
Vol 356-360 ◽  
pp. 2897-2903
Author(s):  
Fen Fen Guo ◽  
Jian Rong Fan ◽  
Wen Qian Zang ◽  
Fei Liu ◽  
Huai Zhen Zhang
Keyword(s):  
Wavelet Transform ◽  
Spatial Resolution ◽  
Hyperspectral Image ◽  
3D Structure ◽  
Principal Component ◽  
Multispectral Image ◽  
Spectral Quality ◽  
Principal Component Transform ◽  
3D Wavelet Transform ◽  
Fusion Approach

The vacancy of hyperspectral image (HSI) in China is made up by HJ-1A satellite, which makes more study and application possible. But comparing with other HSI, low spatial resolution turns into a big limiting obstacle for application. In order to improve the HSI quality and make full use of the existing RS data, this paper proposed a fusion approach basing on 3D wavelet transform (3D WT) to fusing HJ-1A HSI and Multispectral image (MSI) using their 3D structure. Contrasting with the principal component transform (PCA) and Gram-Schmidt fusion approach, which are mature at present, 3D WT fusion approach use all bands of MSI to its advantage and the fusion result perform better in both spatial and spectral quality.

scholarly journals Hyperspectral and Multispectral Image Fusion Techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 467-471
Keyword(s):  
Image Fusion ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Qualitative Comparative Analysis ◽  
Infrared Region ◽  
Multispectral Image ◽  
Electromagnetic Spectrum ◽  
Fusion Methods

The hyper spectral image covers a broad range of wavelengths in electromagnetic spectrum, spanning from visible to near-infrared region. The basic objective of hyperspectral imaging is to attain the spectrum for each pixel in the image of a scene, with the aim of identifying objects in the scene and its classification. The hyperspectral images give detailed spectral information but their spatial resolution is very poor. So to enhance the visual quality of the hyperspectral image, we can perform image fusion with high spatial information multispectral image. This paper provides a complete description of hyperspectral imaging and image fusion methods of hyperspectral and multispectral images. A quantitative and qualitative comparative analysis on performance of various hyperspectral and multispectral image fusion techniques are also done.

scholarly journals A Novel Adversarial Based Hyperspectral and Multispectral Image Fusion

2019 ◽  
Vol 11 (5) ◽  
pp. 492 ◽  
Author(s):  
Xukun Luo ◽  
Jihao Yin ◽  
Xiaoyan Luo ◽  
Xiuping Jia
Keyword(s):  
Spatial Correlation ◽  
Spectral Resolution ◽  
Real World ◽  
Hyperspectral Image ◽  
Selection Process ◽  
Multispectral Image ◽  
High Spectral Resolution ◽  
Spectral Correlation ◽  
Spatial Quality ◽  
Band Image

In order to reconstruct a high spatial and high spectral resolution image (H2SI), one of the most common methods is to fuse a hyperspectral image (HSI) with a corresponding multispectral image (MSI). To effectively obtain both the spectral correlation of bands in HSI and the spatial correlation of pixels in MSI, this paper proposes an adversarial selection fusion (ASF) method for the HSI–MSI fusion problem. Firstly, the unmixing based fusion (UF) method is adopted to dig out the spatial correlation in MSI. Then, to acquire the spectral correlation in HSI, a band reconstruction-based fusion (BRF) method is proposed, regarding H2SI as the product of the optimized band image dictionary and reconstruction coefficients. Finally, spectral spatial quality (SSQ) index is designed to guide the adversarial selection process of UF and BRF. Experimental results on four real-world images demonstrate that the proposed strategy achieves smaller errors and better reconstruction results than other comparison methods.

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.

scholarly journals Fusion of Hyperspectral and Multispectral Image Data for Enhancement of Spectral and Spatial Resolution

2014 ◽  
Vol XL-8 ◽  
pp. 1099-1103 ◽  
Author(s):  
S. Chakravortty ◽  
P. Subramaniam
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Image Data ◽  
Multispectral Image ◽  
Spectral Signatures ◽  
Remote Sensor ◽  
Mixed Pixels ◽  
End Members ◽  
Hyperspectral Sensor

Hyperspectral image enhancement has been a concern for the remote sensing society for detailed end member detection. Hyperspectral remote sensor collects images in hundreds of narrow, continuous spectral channels, whereas multispectral remote sensor collects images in relatively broader wavelength bands. However, the spatial resolution of the hyperspectral sensor image is comparatively lower than that of the multispectral. As a result, spectral signatures from different end members originate within a pixel, known as mixed pixels. This paper presents an approach for obtaining an image which has the spatial resolution of the multispectral image and spectral resolution of the hyperspectral image, by fusion of hyperspectral and multispectral image. The proposed methodology also addresses the band remapping problem, which arises due to different regions of spectral coverage by multispectral and hyperspectral images. Therefore we apply algorithms to restore the spatial information of the hyperspectral image by fusing hyperspectral bands with only those bands which come under each multispectral band range. The proposed methodology is applied over Henry Island, of the Sunderban eco-geographic province. The data is collected by the Hyperion hyperspectral sensor and LISS IV multispectral sensor.

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

2021 ◽  
Vol 13 (15) ◽  
pp. 2930
Author(s):  
Marzieh Zare ◽  
Mohammad Sadegh Helfroush ◽  
Kamran Kazemi ◽  
Paul Scheunders
Keyword(s):  
Spatial Resolution ◽  
Matrix Factorization ◽  
Hyperspectral Image ◽  
Tensor Decomposition ◽  
Structural Features ◽  
Multispectral Image ◽  
Spectral Structure ◽  
Tensor Factorization ◽  
Structure Information ◽  
Factorization Problem

Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-spectral structure information when stacking the 3D data structure of a hyperspectral image into a matrix form. Moreover, the spectral, spatial, or their joint structural features have to be imposed from the outside as a constraint to well pose the matrix factorization problem. The proposed method has the advantage of preserving the spatio-spectral structure of hyperspectral images. In this paper, the NTD is directly imposed on the coupled tensors of the HSI and MSI. Hence, the intrinsic spatio-spectral structure of the HSI is represented without loss, and spatial and spectral information can be interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled with the core tensor of the Tucker tensor decomposition. The proposed method is straightforward and easy to implement. Unlike other state-of-the-art approaches, the complexity of the proposed approach is linear with the size of the HSI cube. Experiments on two well-known datasets give promising results when compared with some recent methods from the literature.

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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