Splitting the hyperspectral-multispectral image fusion problem autonomously into weighted pan-sharpening tasks — The spectral grouping concept

Grohnfeldt, Claas; Zhu, Xiao Xiang; Bamler, Richard

doi:10.1109/whispers.2015.8075412

Cited by 2 publications

(4 citation statements)

References 10 publications

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“…The spectrum of multispectral image has local discontinuities, and the spectral ranges of HSI and MSI do not completely overlap. The fusion of HSI and MSI in the non-overlapping spectral range usually causes spectral distortion [33].Therefore, some studies proposed the HSI-MSI grouping fusion framework [2,15,16], which can retain the local spectral information of images and minimize spectral distortion by fusing the images of each overlapping spectral interval one by one [15].…”

Section: Grouping Fusion Frameworkmentioning

confidence: 99%

“…HSIs with detailed spectral information are particularly important in the analysis of the land-cover for coastal environmental monitoring, disaster monitoring, precision agriculture, forestry surveying and urban planning [1], because HSIs with high spectral resolution can provide better performance for qualitative and quantitative analysis of geographic entities. However, limited by the sensitivity of photoelectric sensors and transmission capability, the spatial resolution of HSIs is not sufficient for some applications [2], such as the monitoring of air pollution [3], land and sea surface temperatures [4,5], heavy metals in soil and vegetation [6], water quality [7], land cover [8,9] and lithological mapping [10]. In recent years, the development of accurate remote sensing applications has increased the requirement for images with both high spatial and spectral resolution.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, an HSI-MSI fusion framework was proposed in [15] which divided HSIs into multiple groups of images according to spectrum and fused the groups with their corresponding channels of MSIs. A similar idea was also proposed in [2], in which the HSI-MSI fusion was automatically decomposed into multiple groups of weighted pan-sharping problems. Selva et al also proposed a framework called hyper-sharpening that effectively applied the MRA-based pan-sharpening methods to HSI-MSI fusion [16].…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, in terms of the strategy for fusing HSI, MSI and PAN images, existing studies in the literature have generally adopted the integrated fusion strategy [27,28], and have not explicitly proposed the concept of stepwise fusion. Since the spectral grouping strategy has been successfully applied in the literature [2,15,16] and many MSI-PAN fusion algorithms have been proposed, can HSI-MSI-PAN fusion be theoretically simplified into several groups of sequential HSI-PAN fusion problems? Therefore, the aim of this study is to explore the effectiveness of fusing HSI, MSI and PAN images using stepwise and spectral grouping strategies with existing pan-sharping algorithms, and also to compare the performances of different algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Stepwise Fusion of Hyperspectral, Multispectral and Panchromatic Images with Spectral Grouping Strategy: A Comparative Study Using GF5 and GF1 Images

Huang

Luo

et al. 2022

Remote Sensing

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Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping over urban and coastal areas. The fusion of HSIs with high spatial resolution multispectral images (MSIs) and panchromatic (PAN) images could be a solution. To address the challenging work of fusing HSIs, MSIs and PAN images, a novel easy-to-implement stepwise fusion approach was proposed in this study. The fusion of HSIs and MSIs was decomposed into a set of simple image fusion tasks through spectral grouping strategy. HSI, MSI and PAN images were fused step by step using existing image fusion algorithms. According to different fusion order, two strategies ((HSI+MSI)+PAN and HSI+(MSI+PAN)) were proposed. Using simulated and real Gaofen-5 (GF-5) HSI, MSI and PAN images from the Gaofen-1 (GF-1) PMS sensor as experimental data, we compared the proposed stepwise fusion strategies with the traditional fusion strategy (HSI+PAN), and compared the performances of six fusion algorithms under three fusion strategies. We comprehensively evaluated the fused results through three aspects: spectral fidelity, spatial fidelity and computation efficiency evaluation. The results showed that (1) the spectral fidelity of the fused images obtained by stepwise fusion strategies was better than that of the traditional strategy; (2) the proposed stepwise strategies performed better or comparable spatial fidelity than traditional strategy; (3) the stepwise strategy did not significantly increase the time complexity compared to the traditional strategy; and (4) we also provide suggestions for selecting image fusion algorithms using the proposed strategy. The study provided us with a reference for the selection of fusion strategies and algorithms in different application scenarios, and also provided an easy-to-implement solution and useful references for fusing HSI, MSI and PAN images.

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