Abstract
Restricted by technical and budget constraints, hyperspectral (HS) image which contains abundant spectral information generally has low spatial resolution. Fusion of hyperspectral and panchromatic (PAN) images can merge spectral information of the former and spatial information of the latter. In this paper, a new hyperspectral image fusion algorithm using structure tensor is proposed. An image enhancement approach is utilized to sharpen the spatial information of the PAN image, and the spatial details of the HS image is obtained by an adaptive weighted method. Since structure tensor represents structure and spatial information, a structure tensor is introduced to extract spatial details of the enhanced PAN image. Seeing that the HS and PAN images contain different and complementary spatial information for a same scene, a weighted fusion method is presented to integrate the extracted spatial information of the two images. To avoid artifacts at the boundaries, a guided filter is applied to the integrated spatial information image. The injection matrix is finally constructed to reduce spectral and spatial distortion, and the fused image is generated by injecting the complete spatial information. Comparative analyses validate the proposed method outperforms the state-of-art fusion methods, and provides more spatial details while preserving the spectral information.
Highlights
Hyperspectral (HS) remote sensing is an emerging discipline
The HS imagery has very high spectral resolution, and is a three-dimensional data cube, of which two spatial dimensions contain the space information, and one spectral dimension at each pixel includes the high-dimensional reflectance vectors [2,3]. Such HS image with abundant spectral information has been widely utilized in many domains, such as military surveillance [4], environmental monitoring [5], mineral exploration [6,7], and agriculture [8,9]
The high spectral resolution is crucial for identifying the materials, high spatial resolution is important for locating the objects with high accuracy
Summary
Hyperspectral (HS) remote sensing is an emerging discipline. Traditional remote sensing sensors obtain the image in a few discrete bands, and lose a large amount of useful information. The CS includes algorithms such as intensity-hue-saturation (IHS) [12,13,14], principal component analysis (PCA) [15,16,17], Gram-Schmidt (GS) [18], adaptive GS (GSA) [19], Brovey transform (BT) [20], and partial replacement adaptive CS (PRACS) [21] These CS based methods work well from a spatial aspect [19], and have fast and simple implementation [13]. In order to accomplish this goal, this paper presents a new hyperspectral image fusion algorithm based on structure tensor. Traditional methods extract the spatial details only from the PAN image without considering the structure information of the HS image, and cause spectral distortion or deficient spatial enhancement.
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