Abstract
Ratio transformation methods are widely used for image fusion of high-resolution optical satellites. The premise for the use the ratio transformation is that there is a zero-bias linear relationship between the panchromatic band and the corresponding multi-spectral bands. However, there are bias terms and residual terms with large values in reality, depending on the sensors, the response spectral ranges, and the land-cover types. To address this problem, this paper proposes a panchromatic and multi-spectral image fusion method based on the panchromatic spectral decomposition (PSD). The low-resolution panchromatic and multi-spectral images are used to solve the proportionality coefficients, the bias coefficients, and the residual matrixes. These coefficients are substituted into the high-resolution panchromatic band and decompose it into the high-resolution multi-spectral bands. The experiments show that this method can make the fused image acquire high color fidelity and sharpness, it is robust to different sensors and features, and it can be applied to the panchromatic and multi-spectral fusion of high-resolution optical satellites.
Highlights
In the conflict between spectral resolution and spatial resolution, the existing optical satellites generally provide high-resolution panchromatic images but low-resolution multi-spectral images [1].Panchromatic images provide the overall spatial structure information, which can describe the structural details of features
This paper proposes a fusion method called panchromatic spectral decomposition
The ratio transformation methods belong to the coefficient enhancement methods, which are commonly used in the field of high-resolution optical remote sensing
Summary
In the conflict between spectral resolution and spatial resolution, the existing optical satellites generally provide high-resolution panchromatic images but low-resolution multi-spectral images [1]. Panchromatic images provide the overall spatial structure information, which can describe the structural details of features. The multi-spectral images provide spectral information of the features, which facilitates feature recognition, as well as their classification and interpretation. The spatial and spectral structures are both important components of the remote-sensing information. The panchromatic and multi-spectral image fusion technology can combine the advantages of both components to obtain high-resolution multi-spectral images [2]. Fusion methods for panchromatic and multi-spectral images can be roughly divided into coefficient enhancement methods and component transformation methods. The coefficient enhancement methods sharpen the multi-spectral bands through the panchromatic band.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
