GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening

Zhang, Hao; Ma, Jiayi

doi:10.1016/j.isprsjprs.2020.12.014

Cited by 80 publications

(34 citation statements)

References 30 publications

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“…Deep learning such as CNN was already successfully introduced into Pan-sharpening. Most of them [46] [47]focus on generating the high-quality fusion image with both accurate spectral distributions and reasonable spatial structures. Usually, the spectrum of PAN images should cover the range of the combination spectrum of MS images.…”

Section: A Spatial-spectral Image Fusionmentioning

confidence: 99%

A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

Liu¹

2021

Preprint

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In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

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Section: A Spatial-spectral Image Fusionmentioning

confidence: 99%

A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

Liu¹

2021

Preprint

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“…The application of remote sensing images is more and more extensive in the current research. These applications include image fusion [ 1 , 2 , 3 , 4 , 5 , 6 ], image classification [ 7 , 8 , 9 , 10 , 11 ], change detection [ 12 , 13 , 14 , 15 , 16 , 17 ], etc. In particular, remote sensing image change detection is to calculate the changed region from the images obtained in two different periods, and this method plays a significant role in the change observation of land use change, flood disaster, earthquake, and fire.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Jia

2022

Entropy

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Remote sensing image change detection is widely used in land use and natural disaster detection. In order to improve the accuracy of change detection, a robust change detection method based on nonsubsampled contourlet transform (NSCT) fusion and fuzzy local information C-means clustering (FLICM) model is introduced in this paper. Firstly, the log-ratio and mean-ratio operators are used to generate the difference image (DI), respectively; then, the NSCT fusion model is utilized to fuse the two difference images, and one new DI is obtained. The fused DI can not only reflect the real change trend but also suppress the background. The FLICM is performed on the new DI to obtain the final change detection map. Four groups of homogeneous remote sensing images are selected for simulation experiments, and the experimental results demonstrate that the proposed homogeneous change detection method has a superior performance than other state-of-the-art algorithms.

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“…Recently, with the rapid development of deep learning and accessibility of highperformance computing hardware equipment, convolutional neural networks (CNNs) have shown outstanding performance in image processing fields, e.g., image resolution reconstruction [45][46][47][48][49], image segmentation [50][51][52], image fusion [53][54][55][56][57], image classification [58], image denoising [59], etc. Therefore, many methods [34][35][36][37][38]41,42,[58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75] based on deep learning have also been applied to solve the pansharpening problem. Benefiting from the powerful nonlinear fitting and feature extraction capabilities of CNNs and the availability of big data, these DL-based methods could perform better than the above three methods to a certain degree, i.e., CS-, MRA-, and VO-based methods.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Pansharpening by Full-Depth Feature Fusion

et al. 2022

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Pansharpening is an important yet challenging remote sensing image processing task, which aims to reconstruct a high-resolution (HR) multispectral (MS) image by fusing a HR panchromatic (PAN) image and a low-resolution (LR) MS image. Though deep learning (DL)-based pansharpening methods have achieved encouraging performance, they are infeasible to fully utilize the deep semantic features and shallow contextual features in the process of feature fusion for a HR-PAN image and LR-MS image. In this paper, we propose an efficient full-depth feature fusion network (FDFNet) for remote sensing pansharpening. Specifically, we design three distinctive branches called PAN-branch, MS-branch, and fusion-branch, respectively. The features extracted from the PAN and MS branches will be progressively injected into the fusion branch at every different depth to make the information fusion more broad and comprehensive. With this structure, the low-level contextual features and high-level semantic features can be characterized and integrated adequately. Extensive experiments on reduced- and full-resolution datasets acquired from WorldView-3, QuickBird, and GaoFen-2 sensors demonstrate that the proposed FDFNet only with less than 100,000 parameters performs better than other detail injection-based proposals and several state-of-the-art approaches, both visually and quantitatively.

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GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening

Cited by 80 publications

References 30 publications

A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Remote Sensing Pansharpening by Full-Depth Feature Fusion

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