Hyperspectral pansharpening via deep detail injection network

Minghua Zhao; Tingting Li; Jing Hu; Jiawei Ning

doi:10.1117/12.2623564

16 February 2022 Hyperspectral pansharpening via deep detail injection network

Minghua Zhao, Tingting Li, Jing Hu, Jiawei Ning

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120830E (2022) https://doi.org/10.1117/12.2623564
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

Limited by the imagery sensors, hyperspectral images (HSIs) are characterized by their rich spectral information but poor spatial information. With the guidance of the panchromatic (PAN) images, hyperspectral pansharpening aims at achieving a HSI with both the fine spatial detail and the high spectral discrimination ability. Although many deep learning-based methods have gained great attention in recent years, it is still challenging for obtaining an appealing performance. In this paper, we propose a novel detail injection network for the hyperspectral pansharpening, which fully exploits the hierarchical features in both the low resolution HSI and the high-resolution PAN. Specifically, the lowresolution HSIs are firstly upsampled to the desired size, and make a concatenation with the PAN image to formulate a new HSI. The new HSI is sent into a residual dense network, in which residual dense block are designed to extract the abundant local features. Finally, details are injected in hierarchical levels for achieving the acceptable performance. Experimental results and data analysis on two datasets which include both indoor and outdoor scenarios have demonstrated the effectiveness of the proposed method.

Citation Download Citation

Minghua Zhao, Tingting Li, Jing Hu, and Jiawei Ning "Hyperspectral pansharpening via deep detail injection network", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120830E (16 February 2022); https://doi.org/10.1117/12.2623564

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KEYWORDS

Feature extraction

Hyperspectral imaging

Image fusion

Convolution

Lawrencium

Visualization

Spatial resolution

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