Remote Sensing Image Fusion Based on Nonnegative Dictionary Learning

Wang, Junhua; Jiang, Yuan; Qi, Yongjun; Zhai, Yongping

doi:10.1109/access.2021.3131268

Cited by 5 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, model-based methods have been widely used in pan-sharpening. such as bidimensional empirical mode decomposition (BEMD) [ 14 ], tensor-based sparse modeling and hyper-Laplacian prior [ 15 ], and non-negative dictionary learning [ 16 ]. These methods aim to integrate the high-frequency components of panchromatic images into multispectral images, preserving the spectral content of the original multispectral images while improving spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Efficacy of Pixel-Level Fusion Techniques for Ultra-High-Resolution Imagery: A Case Study of BJ-3A

Wang,

Mao,

Xin

et al. 2024

Sensors

View full text Add to dashboard Cite

Beijing Satellite 3 is a high-performance optical remote sensing satellite with a spatial resolution of 0.3–0.5 m. It can provide timely and independent ultra-high-resolution spatial big data and comprehensive spatial information application services. At present, there is no relevant research on the fusion method of BJ-3A satellite images. In many applications, high-resolution panchromatic images alone are insufficient. Therefore, it is necessary to fuse them with multispectral images that contain spectral color information. Currently, there is a lack of research on the fusion method of BJ-3A satellite images. This article explores six traditional pixel-level fusion methods (HPF, HCS, wavelet, modified-IHS, PC, and Brovey) for fusing the panchromatic image and multispectral image of the BJ-3A satellite. The fusion results were analyzed qualitatively from two aspects: spatial detail enhancement capability and spectral fidelity. Five indicators, namely mean, standard deviation, entropy, correlation coefficient, and average gradient, were used for quantitative analysis. Finally, the fusion results were comprehensively evaluated from three aspects: spectral curves of ground objects, absolute error figure, and object-oriented classification effects. The findings of the research suggest that the fusion method known as HPF is the optimum and appropriate technique for fusing panchromatic and multispectral images obtained from BJ-3A. These results can be utilized as a guide for the implementation of BJ-3A panchromatic and multispectral data fusion in real-world scenarios.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessing the Efficacy of Pixel-Level Fusion Techniques for Ultra-High-Resolution Imagery: A Case Study of BJ-3A

Wang,

Mao,

Xin

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Considering the interference of clouds on the spectral features, in this study, we propose a cloud-oriented optical and SAR fusion method for ULC classification. As dictionary learning methods have shown exemplary performance in remote sensing field, such as multisource/multi-modality data fusion [23][24][25][26][27], image classification [28][29][30][31][32][33], image recovery [34][35][36], and interferometric phase restoration [37], an innovatively pixel-wise cloud dictionary learning method that considers the interference of clouds is proposed for better discriminating ULC in the cloudy environment. Compared with previous research, the main contributions of this study are summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

WCDL: A Weighted Cloud Dictionary Learning Method for Fusing Cloud-Contaminated Optical and SAR Images

Ling

Zhang

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Abstract-Cloud cover hinders accurate and timely monitoring of urban land cover (ULC). The combination of synthetic aperture radar (SAR) and optical data without cloud contamination has demonstrated promising performance in previous research. However, ULC studies on cloud-prone areas are scarce despite the inevitability of cloud cover, especially in the tropics and subtropics. This study proposes a novel weighted cloud dictionary learning method (WCDL) for fusing optical and SAR data for the ULC classification in cloud-prone areas. We innovatively propose a cloud probability weighting model and a pixel-wise cloud dictionary learning method that take the interference disparities at various cloud probability levels into account to mitigate cloud interference. Experiments reveal that the overall accuracy (OA) of fused data rises by more than 6% and 20% compared to single SAR and optical data, respectively. This method considerably improved by 3% in OA compared with other methods that directly stitch optical and SAR data together regardless of cloud interference. It improves almost all land covers producer's accuracy (PA) and user's accuracy (UA) by up to 9%. Ablation studies further show the cloud probability weighting model improves the OA of all classifiers by up to 5%. And the pixel-wise cloud dictionary learning model improves by more than 2% in OA for all cloud conditions, and the UA and PA are enhanced by up to 9% and 10%. The proposed WCDL method will serve as a reference for fusing cloud-contaminated optical and SAR data and timely, continuous, and accurate land surface monitoring in cloudy areas.

show abstract

From Conventional Approach to Machine Learning and Deep Learning Approach: An Experimental and Comprehensive Review of Image Fusion Techniques

Choudhary

Sethi²

2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

Remote Sensing Image Fusion Based on Nonnegative Dictionary Learning

Cited by 5 publications

References 26 publications

Assessing the Efficacy of Pixel-Level Fusion Techniques for Ultra-High-Resolution Imagery: A Case Study of BJ-3A

Assessing the Efficacy of Pixel-Level Fusion Techniques for Ultra-High-Resolution Imagery: A Case Study of BJ-3A

WCDL: A Weighted Cloud Dictionary Learning Method for Fusing Cloud-Contaminated Optical and SAR Images

From Conventional Approach to Machine Learning and Deep Learning Approach: An Experimental and Comprehensive Review of Image Fusion Techniques

Contact Info

Product

Resources

About