A Deep Network Architecture for Super-Resolution-Aided Hyperspectral Image Classification With Classwise Loss

Hao, Siyuan; Wang, Wei; Ye, Yuanxin; Li, Enyu; Bruzzone, Lorenzo

doi:10.1109/tgrs.2018.2832228

Cited by 66 publications

(31 citation statements)

References 37 publications

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“…Upscaling low-quality (low-resolution, blurred) input images to produce high-quality feature maps to improve classification performance is one of the most popular ways for low-quality image classification or object detection [ 53 , 54 , 55 , 56 , 57 ]. Na, et al [ 56 ] introduced an SR method on cropped regions or candidates to improve object detection and classification performances.…”

Section: Related Workmentioning

confidence: 99%

Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19

Qin

2020

Sensors

203

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The rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) has resulted in a global pandemic. Correct facemask wearing is valuable for infectious disease control, but the effectiveness of facemasks has been diminished, mostly due to improper wearing. However, there have not been any published reports on the automatic identification of facemask-wearing conditions. In this study, we develop a new facemask-wearing condition identification method by combining image super-resolution and classification networks (SRCNet), which quantifies a three-category classification problem based on unconstrained 2D facial images. The proposed algorithm contains four main steps: Image pre-processing, facial detection and cropping, image super-resolution, and facemask-wearing condition identification. Our method was trained and evaluated on the public dataset Medical Masks Dataset containing 3835 images with 671 images of no facemask-wearing, 134 images of incorrect facemask-wearing, and 3030 images of correct facemask-wearing. Finally, the proposed SRCNet achieved 98.70% accuracy and outperformed traditional end-to-end image classification methods using deep learning without image super-resolution by over 1.5% in kappa. Our findings indicate that the proposed SRCNet can achieve high-accuracy identification of facemask-wearing conditions, thus having potential applications in epidemic prevention involving COVID-19.

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Section: Related Workmentioning

confidence: 99%

Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19

Qin

2020

Sensors

203

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“…In addition, when one needs to use the models to build an extension of the super-resolution task, it is necessary to implement it repeatedly for all the frameworks. Examples of the extension include image classification using super-resolved images [12] and robustness analysis of superresolution against adversarial perturbations [13].…”

Section: Models With Different Frameworkmentioning

confidence: 99%

Srzoo: An Integrated Repository For Super-Resolution Using Deep Learning

Choi

Kim

Lee

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Deep learning-based image processing algorithms, including image super-resolution methods, have been proposed with significant improvement in performance in recent years. However, their implementations and evaluations are dispersed in terms of various deep learning frameworks and various evaluation criteria. In this paper, we propose an integrated repository for the super-resolution tasks, named SRZoo, to provide state-of-the-art super-resolution models in a single place. Our repository offers not only converted versions of existing pre-trained models, but also documentation and toolkits for converting other models. In addition, SRZoo provides platform-agnostic image reconstruction tools to obtain super-resolved images and evaluate the performance in place. It also brings the opportunity of extension to advanced image-based researches and other image processing models. The software, documentation, and pre-trained models are publicly available on GitHub 1 .

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“…Three hyperspectral image datasets were used to verify the effectiveness of CFDTRF-LDM. The first dataset was Indian Pines [51], which was acquired in 1992 by the airborne visible infrared imaging spectrometer (AVIRIS) sensor in the Indian Pines region of Northwestern Indiana. It contains 220 spectral bands with a spatial size of 145 × 145 pixels.…”

Section: Hyperspectral Data Descriptionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Fusion of Curvature Filter and Domain Transform Recursive Filter

Liao

Wang

2019

Remote Sensing

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In recent decades, in order to enhance the performance of hyperspectral image classification, the spatial information of hyperspectral image obtained by various methods has become a research hotspot. For this work, it proposes a new classification method based on the fusion of two spatial information, which will be classified by a large margin distribution machine (LDM). First, the spatial texture information is extracted from the top of the principal component analysis for hyperspectral images by a curvature filter (CF). Second, the spatial correlation information of a hyperspectral image is completed by using domain transform recursive filter (DTRF). Last, the spatial texture information and correlation information are fused to be classified with LDM. The experimental results of hyperspectral images classification demonstrate that the proposed curvature filter and domain transform recursive filter with LDM(CFDTRF-LDM) method is superior to other classification methods.

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A Deep Network Architecture for Super-Resolution-Aided Hyperspectral Image Classification With Classwise Loss

Cited by 66 publications

References 37 publications

Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19

Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19

Srzoo: An Integrated Repository For Super-Resolution Using Deep Learning

Hyperspectral Image Classification Based on Fusion of Curvature Filter and Domain Transform Recursive Filter

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