Wen Xincan, Ma Hongbing, Li Liangliang
School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China.
Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China.
Sensors (Basel). 2024 Sep 18;24(18):6039. doi: 10.3390/s24186039.
In the field of remote sensing image processing, pansharpening technology stands as a critical advancement. This technology aims to enhance multispectral images that possess low resolution by integrating them with high-spatial-resolution panchromatic images, ultimately producing multispectral images with high resolution that are abundant in both spatial and spectral details. Thus, there remains potential for improving the quality of both the spectral and spatial domains of the fused images based on deep-learning-based pansharpening methods. This work proposes a new method for the task of pansharpening: the Multi-Stage Progressive Pansharpening Network with Detail Injection with Redundancy Reduction Mechanism (MSPPN-DIRRM). This network is divided into three levels, each of which is optimized for the extraction of spectral and spatial data at different scales. Particular spectral feature and spatial detail extraction modules are used at each stage. Moreover, a new image reconstruction module named the DRRM is introduced in this work; it eliminates both spatial and channel redundancy and improves the fusion quality. The effectiveness of the proposed model is further supported by experimental results using both simulated data and real data from the QuickBird, GaoFen1, and WorldView2 satellites; these results show that the proposed model outperforms deep-learning-based methods in both visual and quantitative assessments. Among various evaluation metrics, performance improves by 0.92-18.7% compared to the latest methods.
在遥感图像处理领域，全色锐化技术是一项关键进展。该技术旨在通过将低分辨率的多光谱图像与高空间分辨率的全色图像进行融合，来增强多光谱图像，最终生成在空间和光谱细节上都很丰富的高分辨率多光谱图像。因此，基于深度学习的全色锐化方法在融合图像的光谱和空间域质量提升方面仍有潜力。这项工作提出了一种用于全色锐化任务的新方法：具有细节注入和冗余减少机制的多阶段渐进全色锐化网络（MSPPN-DIRRM）。该网络分为三个层次，每个层次针对不同尺度的光谱和空间数据提取进行了优化。每个阶段都使用了特定的光谱特征和空间细节提取模块。此外，这项工作还引入了一个名为DRRM的新图像重建模块；它消除了空间和通道冗余，提高了融合质量。使用QuickBird、高分1号和WorldView2卫星的模拟数据和真实数据进行的实验结果进一步支持了所提出模型的有效性；这些结果表明，所提出的模型在视觉和定量评估方面均优于基于深度学习的方法。在各种评估指标中，与最新方法相比，性能提高了0.92%-18.7%。
