A novel adaptive fast IHS transform fusion method driven by regional spectral characteristics for Gaofen-2 imagery. Issue 4 (16th February 2020)

• Record Type: Journal Article
• Authors: Luo, Shuyue; Zhou, Shangbo; Qiang, Baohua
• Is Part Of: International journal of remote sensing. Volume 41:Issue 4(2020)
• Page Start: 1321
• Page End: 1337
• Publication Date: 2020-02-16
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1666315
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: With the increasing diversity of applications based on the Gaofen-2 satellite imagery, broadly applicable methods to generate high quality fused images is a significant problem to investigate. To obtain an image with high spatial and spectral resolutions from given panchromatic (Pan) images and multispectral (MS) images, most existing fusion algorithms adopt a unified strategy for the whole image. However, regions have distinct characteristics that impact the spatial and spectral resolution processing, on account of their varying regional features. In this article, to satisfy the diverse needs of different regions, a novel fast IHS (Intensity-Hue-Saturation) transform fusion method driven by regional spectral characteristics is proposed to fuse Gaofen-2 imagery. First, by the fast IHS transform framework, the original intensity component is obtained from the upsampled MS imagery. Then, numerous independent regions of upsampled MS imagery are generated by a novel superpixel merging strategy, and the spectral characteristics of these regions are utilized for generating a fusion factor. Next, to acquire a new fused intensity component, the fusion factor is applied to guide the injection of details in the fusion procedure. This fusion factor adapts the method to meet the spatial and spectral resolution needs for each region. Finally, the difference between the new fused intensity component and the original one is regarded as the detail that needs to be injected; these are added equally to the different bands of the upsampled MS imagery to yield the final fused multispectral image. In comparison with other classical algorithms, the visual and statistical analysis reveal that our proposed method can provide better results in improving spatial detail and preserving spectral information.