☆ 4.7 Review

A comprehensive review on deep learning based remote sensing image super-resolution methods

EARTH-SCIENCE REVIEWS (2022)

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Unsupervised Remote Sensing Image Super-Resolution Guided by Visible Images

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Summary: In this paper, the authors propose a novel unsupervised cross-domain super-resolution method for reconstructing low-resolution remote sensing images using high-resolution visible natural images as guidance. They build a network with two branches: a visible image-guided branch and a remote sensing image-guided branch, which makes full use of the advantages of high-resolution visible images in reconstruction. They also introduce a domain-ruled discriminator to enforce domain consistency and a remote sensing domain inner study inspired by the zero-shot super-resolution network. Experimental results demonstrate that the proposed method achieves superior performance compared to state-of-the-art unpaired and remote sensing super-resolution methods on challenging remote sensing image datasets.

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TE-SAGAN: An Improved Generative Adversarial Network for Remote Sensing Super-Resolution Images

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Multiscale Feature Enhancement Network for Salient Object Detection in Optical Remote Sensing Images

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Summary: In this article, a novel multiscale feature enhancement network (MFENet) is proposed for salient object detection in optical remote sensing images. By incorporating global feature perception, feature enhancement, semantic feature guidance, and boundary optimization modules, our method outperforms existing state-of-the-art methods in terms of both quantitative and qualitative evaluation metrics.

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Multiattention Generative Adversarial Network for Remote Sensing Image Super-Resolution

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Summary: The article introduces a multiattention GAN (MA-GAN) based on generative adversarial networks for generating high-resolution remote sensing images. The network utilizes pyramid convolutional residual dense, attention-based upsampling, and attention-based fusion modules, as well as a loss function to achieve image super-resolution. Experimental results consistently demonstrate the effectiveness of the proposed MA-GAN approach in various remote sensing scenes.

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Remote Sensing Image Super-Resolution Using Novel Dense-Sampling Networks

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Pre-training of gated convolution neural network for remote sensing image super-resolution

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Summary: Many deep neural networks have been proposed for accurate super-resolution reconstruction of remote sensing images, but training difficulties increase with network depth. To address this, a novel algorithm named PGCNN is introduced, which utilizes gated convolution units to control the transmission of high and low-frequency information, leading to improved image quality in remote sensing applications.

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Enhanced image prior for unsupervised remoting sensing super-resolution

Jiaming Wang et al.

Summary: This paper introduces an unsupervised learning framework called EIP, which can achieve the SR task without low/high resolution image pairs, by transferring texture and structured information from a reference image to an enhanced image prior using a recurrent updating strategy. Experimental results show that EIP outperforms state-of-the-art unsupervised SR methods both quantitatively and qualitatively.

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Remote sensing image super-resolution using cascade generative adversarial nets

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End-to-End Super-Resolution for Remote-Sensing Images Using an Improved Multi-Scale Residual Network

Hai Huan et al.

Summary: The use of pyramidal multi-scale residual network (PMSRN) can effectively improve the spatial resolution of remote-sensing images. By enhancing the ability to detect context information through multi-scale dilation residual block (MSDRB) and achieving the complement of global and local features, the PMSRN achieved good experimental results.

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Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis

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Pyramid Information Distillation Attention Network for Super-Resolution Reconstruction of Remote Sensing Images

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Remote Sensing Image Super-Resolution Using Second-Order Multi-Scale Networks

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Summary: This article proposes a second-order multi-scale super-resolution network (SMSR) that achieves multi-scale information learning and supports reconstruction tasks through single-path feature reuse and a second-order learning mechanism. Experimental results demonstrate the superiority of SMSR over state-of-the-art methods in super-resolving complicated image patterns.

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Multi-scale Single Image Super-Resolution with Remote-Sensing Application Using Transferred Wide Residual Network

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Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images

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Summary: Enlighten-GAN is a generative adversarial network for SRR tasks on large-size optical mid-resolution remote sensing images. It improves performance by designing enlighten blocks and introducing self-supervised hierarchical perceptual loss to avoid unstable convergence and artifacts, and merging reconstructed patches using internal inconsistency loss and cropping-and-clipping strategy.

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Summary: Remote Sensing Image Super-resolution aims to improve the resolution of satellite images, using techniques such as Channel attention-based framework and transfer learning strategy to extract features and enhance performance. Proposed methods, including residual channel attention block and post-upsampling architecture, have shown competitive quantitative and qualitative results on different datasets.

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Remote Sensing Image Super-Resolution via Residual Aggregation and Split Attentional Fusion Network

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Summary: The article introduces a residual aggregation and split attentional fusion network (RASAF) for high-quality super-resolution of remote sensing images. RASAF utilizes split attentional fusion and residual aggregation mechanisms to fully exploit multiscale image information for improved performance. It also demonstrates practicality in remote sensing image classification tasks.

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Satellite Imagery Super-Resolution Using Squeeze-and-Excitation-Based GAN

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Summary: This study introduces the SCSE-GAN model in the field of SISR, optimizing the traditional Generative Adversarial Network with SE modules to achieve more realistic and clear super-resolution image results. Through systematic evaluation on two open-source datasets, the proposed method shows a significant improvement in terms of PSNR and SSIM.

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Lightweight Feedback Convolution Neural Network for Remote Sensing Images Super-Resolution

Jin Wang et al.

Summary: In this paper, a new super-resolution method FGRDN is proposed, which improves feature extraction efficiency through feedback mechanism and GMs module, and adds a SCM module at the end of RDB to learn more useful information. Compared with other lightweight algorithms, this method can converge faster, has fewer parameters, and significantly improves performance in image texture and object contour reconstruction.

IEEE ACCESS (2021)

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Object detection in optical remote sensing images: A survey and a new benchmark

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Coupled Adversarial Training for Remote Sensing Image Super-Resolution

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Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors

Yingfei Xiong et al.

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Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

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Scene-Adaptive Remote Sensing Image Super-Resolution Using a Multiscale Attention Network

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Squeeze-and-Excitation Networks

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Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method

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An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network

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Non-Locally up-Down Convolutional Attention Network for Remote Sensing Image Super-Resolution

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Super-Resolution Reconstruction Method of Remote Sensing Image Based on Multi-Feature Fusion

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Achieving Super-Resolution Remote Sensing Images via the Wavelet Transform Combined With the Recursive Res-Net

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Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network

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Remote Sensing Image Super-Resolution Using Sparse Representation and Coupled Sparse Autoencoder

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Edge-Enhanced GAN for Remote Sensing Image Superresolution

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Deep Residual Squeeze and Excitation Network for Remote Sensing Image Super-Resolution

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Super-Resolution of Single Remote Sensing Image Based on Residual Dense Backprojection Networks

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Remote Sensing Image Superresolution Using Deep Residual Channel Attention

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Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution

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Transferred Multi-Perception Attention Networks for Remote Sensing Image Super-Resolution

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Super-Resolution of Remote Sensing Images via a Dense Residual Generative Adversarial Network

Wen Ma et al.

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Deep Learning for Single Image Super-Resolution: A Brief Review

Wenming Yang et al.

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Aerial Image Super Resolution via Wavelet Multiscale Convolutional Neural Networks

Tingwei Wang et al.

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A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution

Juan Mario Haut et al.

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Single-frame super resolution of remote-sensing images by convolutional neural networks

Caglayan Tuna et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2018)

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Image super-resolution via a novel cascaded convolutional neural network framework

Fu Zhang et al.

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A Progressively Enhanced Network for Video Satellite Imagery Superresolution

Kui Jiang et al.

IEEE SIGNAL PROCESSING LETTERS (2018)

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Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network

Charis Lanaras et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2018)

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Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution

Kui Jiang et al.

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Super-Resolution for Remote Sensing Images via Local-Global Combined Network

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2017)

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Learning a no-reference quality metric for single-image super-resolution

Chao Ma et al.

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Image Super-Resolution Using Deep Convolutional Networks

Chao Dong et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2016)

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Deep Learning Based Feature Selection for Remote Sensing Scene Classification

Qin Zou et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2015)

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Making a Completely Blind Image Quality Analyzer

Anish Mittal et al.

IEEE SIGNAL PROCESSING LETTERS (2013)

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High-resolution satellite scene classification using a sparse coding based multiple feature combination

Guofeng Sheng et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2012)

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FSIM: A Feature Similarity Index for Image Quality Assessment

Lin Zhang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2011)

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Multispectral and panchromatic data fusion assessment without reference

Luciano Alparone et al.

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