Pansharpening via Subpixel Convolutional Residual Network

Li, Chunyu; Zheng, Yuhui

doi:10.1109/jstars.2021.3117944

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…For the third and fourth groups of experiments, we use the following three common objective evaluation indexes to evaluate the experimental results: quality without reference (QNR) index [ 25 ], and two components

and

to quantify the spectral distortion and spatial distortion, respectively [ 26 ].

where

represents the LRMS image and C represents the number of bands.…”

Section: Resultsmentioning

confidence: 99%

Remote Sensing Image Fusion Based on Morphological Convolutional Neural Networks with Information Entropy for Optimal Scale

Jia

Xing

et al. 2022

Sensors

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Remote sensing image fusion is a fundamental issue in the field of remote sensing. In this paper, we propose a remote sensing image fusion method based on optimal scale morphological convolutional neural networks (CNN) using the principle of entropy from information theory. We use an attentional CNN to fuse the optimal cartoon and texture components of the original images to obtain a high-resolution multispectral image. We obtain the cartoon and texture components using sparse decomposition-morphological component analysis (MCA) with an optimal threshold value determined by calculating the information entropy of the fused image. In the sparse decomposition process, the local discrete cosine transform dictionary and the curvelet transform dictionary compose the MCA dictionary. We sparsely decompose the original remote sensing images into a texture component and a cartoon component at an optimal scale using the information entropy to control the dictionary parameter. Experimental results show that the remote sensing image fusion method proposed in this paper can effectively retain the information of the original image, improve the spatial resolution and spectral fidelity, and provide a new idea for image fusion from the perspective of multi-morphological deep learning.

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and

to quantify the spectral distortion and spatial distortion, respectively [ 26 ].

where

represents the LRMS image and C represents the number of bands.…”

Section: Resultsmentioning

confidence: 99%

Remote Sensing Image Fusion Based on Morphological Convolutional Neural Networks with Information Entropy for Optimal Scale

Jia

Xing

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Over the last few decades, many pansharpening methods have been proposed, which can be divided into five branches: component substitution (CS) [6][7][8][9][10][11], multiresolution analysis (MRA) [12][13][14][15][16], variational optimization (VO) [17][18][19][20][21][22][23], geostatistical [24][25][26], and deep learning (DL) [27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

A Local and Nonlocal Feature Interaction Network for Pansharpening

Yin

Sun

et al. 2022

Remote Sensing

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Pansharpening based on deep learning (DL) has shown great advantages. Most convolutional neural network (CNN)-based methods focus on obtaining local features from multispectral (MS) and panchromatic (PAN) images, but ignore the nonlocal dependence on images. Therefore, Transformer-based methods are introduced to obtain long-range information on images. However, the representational capabilities of features extracted by CNN or Transformer alone are weak. To solve this problem, a local and nonlocal feature interaction network (LNFIN) is proposed in this paper for pansharpening. It comprises Transformer and CNN branches. Furthermore, a feature interaction module (FIM) is proposed to fuse different features and return to the two branches to enhance the representational capability of features. Specifically, a CNN branch consisting of multiscale dense modules (MDMs) is proposed for acquiring local features of the image, and a Transformer branch consisting of pansharpening Transformer modules (PTMs) is introduced for acquiring nonlocal features of the image. In addition, inspired by the PTM, a shift pansharpening Transformer module (SPTM) is proposed for the learning of texture features to further enhance the spatial representation of features. The LNFIN outperforms the state-of-the-art method experimentally on three datasets.

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Spectral-Spatial Interaction Network for Multispectral Image and Panchromatic Image Fusion

et al. 2022

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Recently, with the rapid development of deep learning (DL), an increasing number of DL-based methods are applied in pansharpening. Benefiting from the powerful feature extraction capability of deep learning, DL-based methods have achieved state-of-the-art performance in pansharpening. However, most DL-based methods simply fuse multi-spectral (MS) images and panchromatic (PAN) images by concatenating, which can not make full use of the spectral information and spatial information of MS and PAN images, respectively. To address this issue, we propose a spectral-spatial interaction Network (SSIN) for pansharpening. Different from previous works, we extract the features of PAN and MS, respectively, and then interact them repetitively to incorporate spectral and spatial information progressively. In order to enhance the spectral-spatial information fusion, we further propose spectral-spatial attention (SSA) module to yield a more effective spatial-spectral information transfer in the network. Extensive experiments on QuickBird, WorldView-4, and WorldView-2 images demonstrate that our SSIN significantly outperforms other methods in terms of both objective assessment and visual quality.

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Pansharpening via Subpixel Convolutional Residual Network

Cited by 3 publications

References 38 publications

Remote Sensing Image Fusion Based on Morphological Convolutional Neural Networks with Information Entropy for Optimal Scale

Remote Sensing Image Fusion Based on Morphological Convolutional Neural Networks with Information Entropy for Optimal Scale

A Local and Nonlocal Feature Interaction Network for Pansharpening

Spectral-Spatial Interaction Network for Multispectral Image and Panchromatic Image Fusion

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