Transfering Super Resolution Convolutional Neural Network For Remote Sensing Data Sharpening

Iftene, Meziane; Arabi, Mahdi; Karoui, Moussa Sofiane

doi:10.1109/whispers.2018.8747223

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…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].…”

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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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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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End-to-End Change Detection in Satellite Remote Sensing Imagery

Iftene

Larabi

Karoui

2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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Swin–MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN

Rong,

Jiang,

Huang

et al. 2023

Applied Sciences

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Pan-sharpening aims to create high-resolution spectrum images by fusing low-resolution hyperspectral (HS) images with high-resolution panchromatic (PAN) images. Inspired by the Swin transformer used in image classification tasks, this research constructs a three-stream pan-sharpening network based on the Swin transformer and a multi-scale feature extraction module. Unlike the traditional convolutional neural network (CNN) pan-sharpening model, we use the Swin transformer to establish global connections with the image and combine it with a multi-scale feature extraction module to extract local features of different sizes. The model combines the advantages of the Swin transformer and CNN, enabling fused images to maintain good local detail and global linkage by mitigating distortion in hyperspectral images. In order to verify the effectiveness of the method, this paper evaluates fused images with subjective visual and quantitative indicators. Experimental results show that the method proposed in this paper can better preserve the spatial and spectral information of images compared to the classical and latest models.

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Transfering Super Resolution Convolutional Neural Network For Remote Sensing Data Sharpening

Cited by 3 publications

References 8 publications

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

End-to-End Change Detection in Satellite Remote Sensing Imagery

Swin–MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN

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