Panchromatic Enhanced Super-Resolution of Multi-Spectral Imagery

Remote Sensing, 2018

High resolution (HR) hyperspectral (HS) images have found widespread applications in terrestrial remote sensing applications, including vegetation monitoring, military surveillance and reconnaissance, fire damage assessment, and many others. They also find applications in planetary missions such as Mars surface characterization. However, resolutions of most HS imagers are limited to tens of meters. Existing resolution enhancement techniques either require additional multispectral (MS) band images or use a panchromatic (pan) band image. The former poses hardware challenges, whereas the latter may have limited performance. In this paper, we present a new resolution enhancement algorithm for HS images that only requires an HR color image and a low resolution (LR) HS image cube. Our approach integrates two newly developed techniques: (1) A hybrid color mapping (HCM) algorithm, and (2) A Plug-and-Play algorithm for single image super-resolution. Comprehensive experiments (objective (five...

IntechOpen eBooks, 2024

A panchromatic band (Pan-band) spectrally covers a number of the other bands (multispectral-bands, MS). The Pan-band is of higher spatial resolution than the MS. The respective advantages of the two are combined through pan-sharpening with the resultant image adopting the higher spatial resolution of the Pan-band and the colour information of the MS. Various techniques have evolved but most of them cannot pan-sharpen more than three MS, and none of them can pan-sharpen more than three MS at a go, nor pan-sharpen a multispectral image not geographically covered by the Pan-band. This novel concept overcomes the first problem. The sequel to this chapter will address the second problem through reverse pan-sharpening. The concept argues that for a given pixel in the Pan-band, the strata of digital numbers (DNs) in the MS combine to give rise to a panchromatic-DN. The concept estimates respective coefficients of strata of DNs in the encompassed bands corresponding to pure blocks of pixels in the Pan-band. On the basis of the coefficients, encompassed bands' DN contributions to the panchromatic-DN are computed from the Pan-band DN. The resultant DN contributions are regressed on the MS-DNs and one of the encompassed MS pan-sharpened on the basis of its model. The other multi-spectral bands are pansharpened through it.

2010

In this paper the authors present a super resolution approach which is based on iterative data fusion algorithms. The proposed data fusion can be implemented using a plurality of spectral images as well as a plurality of images with varied resolution generated from different regions of the field of view. The data fusion suggested in this paper is gradual, allowing the build up of one single high resolution image from low resolution images and partial high resolution images. The iterative procedure used in this paper is based on iterative ping-pong computation between the spatial domain and its spectral distribution, similar to the Gerchberg-Saxton approach but with dynamic parameters. The iterative approach enables the retrieval of high resolution data from mostly-low resolution data. In both approaches mentioned, one may mix high and low resolution information by the insertion of properly defined constrains, and achieve an enhanced image in terms of clarity, resolution, correlation with true data and contrast.

EURASIP Journal on Advances in Signal Processing, 2011

There exist a number of satellites on different earth observation platforms, which provide multispectral images together with a panchromatic image, that is, an image containing reflectance data representative of a wide range of bands and wavelengths. Pansharpening is a pixel-level fusion technique used to increase the spatial resolution of the multispectral image while simultaneously preserving its spectral information. In this paper, we provide a review of the pan-sharpening methods proposed in the literature giving a clear classification of them and a description of their main characteristics. Finally, we analyze how the quality of the pansharpened images can be assessed both visually and quantitatively and examine the different quality measures proposed for that purpose.

2016 24th Signal Processing and Communication Application Conference (SIU), 2016

, 112 pages Hyperspectral (HS) imagery consists of hundred of narrow contiguous bands extending beyond the visible spectrum. It is a three dimensional data cube with two dimensional spatial information and a spectral dimension. Despite having high spectral resolution, HS images have lower spatial resolution due to technological restrictions. This degrades the performance in HS imaging applications. Therefore, increasing the resolution is a necessity, however the problem is an ill-posed problem. In this thesis, we address this problem, namely super-resolution reconstruction (SRR) of HS images from different perspectives and propose robust solutions. First method proposes a maximum a posteriori (MAP) based SRR technique for HS images when there is only one HS image and no other source of information. The novelty of the method is converting ill-posed SRR problem in spectral domain to a quadratic optimization problem in abundance map domain. Using smoothness prior and inherent properties of abundance maps in the quadratic optimization, a unique solution is obtained. Moreover, in order to avoid over smoothing, a post processing is applied to preserve textures in the abundance maps. Finally, high resolution (HR) HS image is reconstructed using the extracted endmembers and the enhanced abundances. v Second proposed method is a fusion based SRR method. This method enhances the spatial resolution of HS image by fusing with a coinciding HR RGB or multispectral (MS) image. Again, fusion problem is converted to a quadratic optimization problem in the abundance map domain. Moreover, proposed MAP based approach is also merged into the quadratic equation. That is, this method is a superposition of MAP based and fusion based approaches and closing the gaps of the both methods. Superposition of two methods leads to more robust and efficient SRR method. Similarly, after solving quadratic problem, HR HS image reconstructed from HR abundance maps and endmember signatures. Experiments are implemented on real HS datasets and compared to state-of-the-art alternative methods using different quantitative image metrics. Spectral consistency, a critical issue for HS images, is also analysed in the experiments. Results demonstrate that proposed methods perform better than competitors based on quantitative metrics while keeping spectral consistency.

IEEE Transactions on Image Processing, 2005

Hyperspectral images are used for aerial and space imagery applications, including target detection, tracking, agricultural, and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles, and sensor noise degrade the quality of these images. Improving their resolution has a high payoff, but applying superresolution techniques separately to every spectral band is problematic for two main reasons. First, the number of spectral bands can be in the hundreds, which increases the computational load excessively. Second, considering the bands separately does not make use of the information that is present across them. Furthermore, separate band super resolution does not make use of the inherent low dimensionality of the spectral data, which can effectively be used to improve the robustness against noise. In this paper, we introduce a novel super-resolution method for hyperspectral images. An integral part of our work is to model the hyperspectral image acquisition process. We propose a model that enables us to represent the hyperspectral observations from different wavelengths as weighted linear combinations of a small number of basis image planes. Then, a method for applying super resolution to hyperspectral images using this model is presented. The method fuses information from multiple observations and spectral bands to improve spatial resolution and reconstruct the spectrum of the observed scene as a combination of a small number of spectral basis functions.

Taylor & Francis, 2020

Pan-sharpening is a remote sensing image fusion technique that generates a high-resolution multispectral (HRMS) image on combining a low resolution multispectral (MS) image and a panchromatic (PAN) image. In this paper, a new optimisation model is proposed for pan-sharpening. The proposed model consists of three terms: (i) a data synthesis fidelity term formulated on inferring the relationship between source MS image and fused image to preserve the spectral information, (ii) a total generalised variation-based prior term to inject the significant spatial details from PAN image to pan-sharpened image, and (iii) a spectral distortion reduction term that exploits the correlation between multispectral image bands. To solve the resultant convex optimisation problem, an efficient and convergence guaranteed operator splitting framework based on the alternating direction method of multipliers (ADMM) algorithm is formulated. Finally, the proposed model is experimentally validated using full-resolution and reduced-resolution data. The pan-sharpened outcomes exhibit the potential of the proposed method in enhancing the spatial and spectral quality. ARTICLE HISTORY

2011

2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)

Hyperspectral image (HSI) with narrow spectral bands can capture rich spectral information, but it sacrifices its spatial resolution in the process. Many machine-learningbased HSI super-resolution (SR) algorithms have been proposed recently. However, one of the fundamental limitations of these approaches is that they are highly dependent on image and camera settings and can only learn to map an input HSI with one specific setting to an output HSI with another. However, different cameras capture images with different spectral response functions and bands numbers due to the diversity of HSI cameras. Consequently, the existing machine-learning-based approaches fail to learn to super-resolve HSIs for a wide variety of input-output band settings. We propose a single Meta-Learning-Based Super-Resolution (MLSR) model, which can take in HSI images at an arbitrary number of input bands' peak wavelengths and generate SR HSIs with an arbitrary number of output bands' peak wavelengths. We leverage NTIRE2020 and ICVL datasets to train and validate the performance of the MLSR model. The results show that the single proposed model can successfully generate super-resolved HSI bands at arbitrary input-output band settings. The results are better or at least comparable to baselines that are separately trained on a specific input-output band setting.

Arabian Journal of Geosciences, 2020

Panchromatic and multispectral images produced by the earth observation satellites are fused; hence, a high-resolution multispectral image is obtained. Spectral quality of the fused images is of great importance since the quality of a large number of remote sensing products mainly depends on this feature. Due to the importance of the spectral quality of the fused images, its assessment is of particular significance as well. This article proposes an object-based strategy for the spectral quality assessment of the fused images to eliminate the limitations of the current pixel-based method. This kind of assessment is performed by focusing on homogeneous objects with similar spectral and textural behaviors. After determining an optimal metric, the object-based scheme was applied to five datasets from four types of satellite sensors and the spectral behavior of fusion methods was examined within the image classes. Although the spectral behavior of the fusion methods was not regular, the ...