Dynamic dictionary learning strategies for sparse representation based hyperspectral image enhancement

Grohnfeldt, Claas; Burns, Tristan; Zhu, Xiao Xiang

doi:10.1109/whispers.2015.8075407

Cited by 2 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By following this strategy, various estimators are implemented in the image domain. One such recent approach is a sparse representation (Wei et al 2015a); and dictionary learning for multiresolution image fusion (Grohnfeldt et al 2015). The main task in sparse representation multiresolution image fusion is to find a representative dictionary of high resolution images which is the scope of dictionary learning techniques.…”

Section: Bayesian Fusionmentioning

confidence: 99%

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Mookambiga

Gomathi²

2016

Multidim Syst Sign Process

View full text Add to dashboard Cite

The volume of data grows with the advent of multiple types of remote sensing sensors and in order to extract the most useful information there is a need to combine the data gathered from the different sources. The widely used panchromatic and multispectral imageries in many applications offer decimetric and metric spatial resolution. However, the spectral resolution of these images is poor. Hyperspectral imaging has unique characteristics of providing very fine spectral resolution in a large number of bands with decametric spatial resolution and found to be highly useful for a wide span of application areas that requires high spectral resolution. The fusion of spectral and spatial information provides an effective way of enhancing the spatial quality of hyperspectral imagery as well as a method for preserving spectral quality. This fusion process is not a trivial task as always there has been a tradeoff between the preservation of spatial and spectral quality information as in the original sources of fusion. In this paper, a review on hyperspectral pansharpening and hyperspectral multispectral fusion based approaches has been reported. The widely adopted quantitative and qualitative performance measures to verify the fusion results are highlighted. In addition, the challenges in existing fusion techniques have also been discussed.

show abstract

Section: Bayesian Fusionmentioning

confidence: 99%

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Mookambiga

Gomathi²

2016

Multidim Syst Sign Process

View full text Add to dashboard Cite

show abstract

“…ERGAS relies on computing the normalized average error of each band in the enhanced image, therefore, low ERGAS values indicate high quality. Other evaluation metrics include Degree of Distortion (DD) [159], [197], [236], Q2 n [237]- [239], sub-pixel CC [240], and Spectral Angle Error (SAE) [241], [242]. Additionally, some researchers assess the quality of their enhanced HSI by observing the performance of standard classification algorithms on the enhanced HSI as opposed to the GT HSI in terms of Overall Accuracy (OA) and Kappa [243]- [247].…”

mentioning

confidence: 99%

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Aburaed

Alkhatib

Marshall

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is Hyperspectral Imaging (HSI). Unlike Multispectral Images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or Super Resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This paper reviews and discusses the most important algorithms relevant to this area of research between 2002-2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Meta-analysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions.

show abstract

Dynamic dictionary learning strategies for sparse representation based hyperspectral image enhancement

Cited by 2 publications

References 8 publications

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Contact Info

Product

Resources

About