Hyperspectral and multispectral image fusion techniques for high resolution applications: a review

2021 ◽  
Author(s):  
Dioline Sara ◽  
Ajay Kumar Mandava ◽  
Arun Kumar ◽  
Shiny Duela ◽  
Anitha Jude
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Multispectral Image

scholarly journals Lunar Image Fusion using Wavelet Transform

2013 ◽  
pp. 211-215
Author(s):  
Dr.Vani. K ◽  
Anto. A. Micheal
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Lunar Surface ◽  
High Spatial Resolution ◽  
Multispectral Image ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Mineral Mapping

This paper is an attempt to combine high resolution panchromatic lunar image with low resolution multispectral lunar image to produce a composite image using wavelet approach. There are many sensors that provide us image data about the lunar surface. The spatial resolution and spectral resolution is unique for each sensor, thereby resulting in limitation in extraction of information about the lunar surface. The high resolution panchromatic lunar image has high spatial resolution but low spectral resolution; the low resolution multispectral image has low spatial resolution but high spectral resolution. Extracting features such as craters, crater morphology, rilles and regolith surfaces with a low spatial resolution in multispectral image may not yield satisfactory results. A sensor which has high spatial resolution can provide better information when fused with the high spectral resolution. These fused image results pertain to enhanced crater mapping and mineral mapping in lunar surface. Since fusion using wavelet preserve spectral content needed for mineral mapping, image fusion has been done using wavelet approach.

scholarly journals Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner

2021 ◽  
Vol 13 (16) ◽  
pp. 3226
Author(s):  
Jianhao Gao ◽  
Jie Li ◽  
Menghui Jiang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Image Fusion ◽  
Multispectral Image ◽  
Hyperspectral Images ◽  
Training Dataset ◽  
Diffusion Method ◽  
Simple Diffusion ◽  
Fusion Methods

Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral images. An intelligent idea to obtain high-resolution hyperspectral images is hyperspectral and multispectral image fusion. In recent years, many studies have found that deep learning-based fusion methods outperform the traditional fusion methods due to the strong non-linear fitting ability of convolution neural network. However, the function of deep learning-based methods heavily depends on the size and quality of training dataset, constraining the application of deep learning under the situation where training dataset is not available or of low quality. In this paper, we introduce a novel fusion method, which operates in a self-supervised manner, to the task of hyperspectral and multispectral image fusion without training datasets. Our method proposes two constraints constructed by low-resolution hyperspectral images and fake high-resolution hyperspectral images obtained from a simple diffusion method. Several simulation and real-data experiments are conducted with several popular remote sensing hyperspectral data under the condition where training datasets are unavailable. Quantitative and qualitative results indicate that the proposed method outperforms those traditional methods by a large extent.

New texture-based pansharpening method using wavelet packet transform and PCA

2020 ◽  
Vol 18 (04) ◽  
pp. 2050025
Author(s):  
Asma Abdolahpoor ◽  
Peyman Kabiri
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Spatial Resolution ◽  
Wavelet Packet ◽  
Principal Component ◽  
Wavelet Packet Transform ◽  
Multispectral Image ◽  
Multispectral Images ◽  
Low Resolution ◽  
Panchromatic Image

Image fusion is an important concept in remote sensing. Earth observation satellites provide both high-resolution panchromatic and low-resolution multispectral images. Pansharpening is aimed on fusion of a low-resolution multispectral image with a high-resolution panchromatic image. Because of this fusion, a multispectral image with high spatial and spectral resolution is generated. This paper reports a new method to improve spatial resolution of the final multispectral image. The reported work proposes an image fusion method using wavelet packet transform (WPT) and principal component analysis (PCA) methods based on the textures of the panchromatic image. Initially, adaptive PCA (APCA) is applied to both multispectral and panchromatic images. Consequently, WPT is used to decompose the first principal component of multispectral and panchromatic images. Using WPT, high frequency details of both panchromatic and multispectral images are extracted. In areas with similar texture, extracted spatial details from the panchromatic image are injected into the multispectral image. Experimental results show that the proposed method can provide promising results in fusing multispectral images with high-spatial resolution panchromatic image. Moreover, results show that the proposed method can successfully improve spectral features of the multispectral image.

scholarly journals Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Inter-Image Variability

2021 ◽  
pp. 1-1
Author(s):  
Ricardo Augusto Borsoi ◽  
Clemence Prevost ◽  
Konstantin Usevich ◽  
David Brie ◽  
Jose Carlos M. Bermudez ◽  
...  
Keyword(s):  
Image Fusion ◽  
Tensor Decomposition ◽  
Multispectral Image ◽  
Image Variability

scholarly journals HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level

2020 ◽  
Vol 13 ◽  
pp. 4607-4626
Author(s):  
Jiale Jiang ◽  
Qiaofeng Zhang ◽  
Xia Yao ◽  
Yongchao Tian ◽  
Yan Zhu ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Fusion Method ◽  
Image Fusion Method

Edge-Conditioned Feature Transform Network for Hyperspectral and Multispectral Image Fusion

2021 ◽  
pp. 1-15
Author(s):  
Yuxuan Zheng ◽  
Jiaojiao Li ◽  
Yunsong Li ◽  
Jie Guo ◽  
Xianyun Wu ◽  
...  
Keyword(s):  
Image Fusion ◽  
Multispectral Image ◽  
Feature Transform
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