In this paper, we address the issue of hyperspectral pansharpening, which consists in fusing a (low spatial resolution) hyperspectral image HX and a (high spatial resolution) panchromatic image P to obtain a high spatial resolution hyperspectral image. The problem is addressed under a convex variational constrained formulation. The fit-to-P data term favors high resolution hyperspectral images ...

The difference and complementarity of spatial spectral information between multispectral (MS) image panchromatic (PAN) have laid the foundation for fusion two types images. In recent years, MS PAN (also known as MS-Pansharpening) has gained attention an important research area in remote sensing (RS) processing. This paper proposes MS-Pansharpening algorithm based on dual constraint Guided Filte...

Since the hardware limitations of satellite sensors, spatial resolution multispectral (MS) images is still not consistent with panchromatic (PAN) images. It especially important to obtain MS high in field remote sensing image fusion. In order and spectral resolutions, a novel PAN fusion method based on weighted mean curvature filter (WMCF) decomposition proposed this paper. Firstly, local frequ...

ABSTRACT: Due to the development of spatial resolution, spectral resolution and multi-image tasking of high-resolution satellite images, the generation of digital surface model (DSM) from high-resolution satellite images becomes an important and practical application. Nowadays, different image matching approaches such as global matching and semi-global matching has been developed to obtain dens...

The term “image fusion” covers multiple techniques used to combine the geometric detail of a high-resolution panchromatic image and the color information of a lowresolution multispectral image to produce a final image with the highest possible spatial information content while still preserving good spectral information quality. During the last twenty years, many methods such as Principal Compon...

For many urban studies it is necessary to obtain remote sensing images with high hyperspectral and spatial resolution by fusing the panchromatic images. In this article, we propose a deep learning model of an encoder–decoder residual network (EDRN) for image fusion. First, combined circumvent independence features. Second, established extracting representative encoded decoded Finally, networks ...

Image fusion aims at the exploitation of the information conveyed by data acquired by different imaging sensors. A notable application is merging images acquired from space by panchromatic and multior hyper-spectral sensors that exhibit complementary spatial and spectral resolution. Multiresolution analysis has been recognized efficient for image fusion. The Generalized Laplacian Pyramid (GLP),...

I<span>Image-fusion provide users with detailed information about the urban and rural environment, which is useful for applications such as planning management when higher spatial resolution images are not available. There different image fusion methods. This paper implements, evaluates, compares six satellite image-fusion methods, namely wavelet 2D-M transform, gram schmidt, high-frequen...

Major technical constraints like minimum data storage at satellite platform in space, less bandwidth for communication with earth station, etc. limits the satellite sensors from capturing images with high spatial and high spectral resolutions simultaneously. To overcome this limitation, image fusion has proved to be a potential tool in remote sensing applications which integrates the informatio...