Work

MDSCNN: Remote Sensing Image Spatial–Spectral Fusion Method via Multi-Scale Dual-Stream Convolutional Neural Network

Year: 2024

Type: article

Abstract: Pansharpening refers to enhancing the spatial resolution of multispectral images through panchromatic images while preserving their spectral features. However, existing traditional methods or deep learning methods always have certain distortions in the spatial or spectral dimensions. This paper proposes a remote sensing spatial–spectral fusion method based on a multi-scale dual-stream convolutional neural network, which includes feature extraction, feature fusion, and image reconstruction modules for each scale. In terms of feature fusion, we propose a multi cascade module to better fuse image features. We also design a new loss function aim at enhancing the high degree of consistency between fused images and reference images in terms of spatial details and spectral information. To validate its effectiveness, we conduct thorough experimental analyses on two widely used remote sensing datasets: GeoEye-1 and Ikonos. Compared with the nine leading pansharpening techniques, the proposed method demonstrates superior performance in multiple key evaluation metrics.

Source: Remote Sensing

DOI: 10.3390/rs16193583

Authors: Wenqing Wang, Fei Jia, Yifei Yang, Kunpeng Mu, Han Liu

Institution: Xi'an University of Technology

Language: English

Topic: Advanced Image Fusion Techniques

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,069

Funder: National Natural Science Foundation of China