Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner

Gao, Jianhao; Li, Jie; Jiang, Menghui

doi:10.3390/rs13163226

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…The deep neural networks or incorporation of subpixel-based classifiers may solve the problem of mixed pixel interference and handle large volumes of data in real time 74 – 76 The subpixel-, pixel-, and superpixel-based image fusion methods may also be followed to improve the detection of land surface changes via the hyperspectral dataset 77 . Moreover, the proposed technique can also be explored for various other areas, such as soil type and erosion, disease detection, and crop identification.…”

Section: Resultsmentioning

confidence: 99%

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

Dahiya

Gupta

Singh

2023

J. Appl. Rem. Sens.

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Remote sensing is an effective way to analyze land surface changes on regular basis globally. In the previous literature, numerous change detection models were developed to detect the multitemporal or seasonal variations using different optical datasets, such as multispectral and hyperspectral. But there are many challenges involved in the processing of numerous bands, especially in the case of hyperspectral datasets, such as computational constraints and radiometric/atmospheric distortion. A simple framework-based artificial neural network (ANN) and postclassification comparison (PCC), named ANN-based PCC (ANPC), has been proposed to detect the multitemporal changes over agricultural land using hyperspectral, i.e., Earth Observation (EO-1) Hyperion and multispectral, i.e., Landsat-8. For cross-referencing, a comparative analysis is performed with respect to well-known PCC-based change detection methods, i.e., random forest-based post-comparison (RFPC) and support vector machine-based post-comparison (SVMPC). Experimental outcomes confirmed the effectiveness of ANPC (with an accuracy of more than 90%) in the extraction of multitemporal changes as compared to RFPC and SVMPC (with an accuracy of <90%). Our study enhances the utilization of the hyperspectral dataset (due to narrow spectral bands) in the extraction of critical information about the earth's surface parameters.

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Section: Resultsmentioning

confidence: 99%

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

Dahiya

Gupta

Singh

2023

J. Appl. Rem. Sens.

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“…A similar blind approach idea is presented in [174], where Wei et al used a deep recursive residual network to fuse LR-HSI with HR-MSI. Other works in this area include [175]- [177], [177]- [193], [278]- [291].…”

Section: Deep Learning-based Fusionmentioning

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

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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.

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“…In recent years, with the rapid development of modern computer technology and the maturity of some algorithms, image fusion technology has been rapidly and greatly developed, and has great application value and prospect in remote sensing imaging, medical image, forest protection, industrial production and other fields. Existing technologies related to hyperspectral image super-resolution reconstruction based on fusion mainly include: component replacement method [5][6] , multi-resolution analysis method [7][8][9], sparse representation method [10][11][12] and deep learning method [14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral image fusion based on dual-resolution fusion feature mutual guidance network

Lu¹,

Ma²,

Yan³

et al. 2023

Optics for Arts, Architecture, and Archaeology (O3A) IX

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In recent years, the applications of hyperspectral imaging in the protection and analysis of cultural relics have received widespread attention. However, due to the limitation of imaging sensors, the spatial resolution of existing hyperspectral images is low, which hinders the development of hyperspectral digitization of cultural relics. Hyperspectral (HS) and RGB image fusion technology can generate hyperspectral images with high spatial resolution, which has gradually become a research hotspot. Inspired by the astounding performance of deep learning in various hyperspectral image processing tasks, this paper proposes a hyperspectral image fusion method based on dual-resolution fusion feature mutual guidance network (DRFFMG). Firstly, two feature extraction networks for HS and RGB images with different resolution pairs are designed to increase the richness of extracted features and reduce the loss of original hyperspectral information. Then, the spatial and spectral features extracted from the above feature extraction networks are fused, and a fusion feature mutual guidance module is designed to promote the mutual learning of different spatial features through information transmission, effectively reducing spatial distortion. Finally, the desired high spatial resolution HS image is restored from the fused features through an image reconstruction network. Experiments demonstrate that the proposed DRFFMG network can produce fusion images competitive with even better to state of the arts, and retain spectral information while improving spatial resolution.

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Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner

Cited by 7 publications

References 33 publications

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Hyperspectral image fusion based on dual-resolution fusion feature mutual guidance network

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