End-to-End Super-Resolution for Remote-Sensing Images Using an Improved Multi-Scale Residual Network

Huan, Hai; Li, Pengcheng; Zou, Nan; Chao, Wang; Xie, Yaqin; Xie, Yong; Xu, Dongdong

doi:10.3390/rs13040666

Cited by 32 publications

(21 citation statements)

References 29 publications

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“…Similar to first-order coefficients, 𝑆 2 𝑥 denotes the secondorder coefficients and the support of 𝜓 𝜆 2 is centered in 𝜆 2 with a frequency bandwidth of 𝜆 2 𝑄 2 ⁄ for 𝜆 2 ≥ 2𝜋𝑄/𝑇 and 2𝜋/𝑇 for 𝜆 2 < 2𝜋𝑄/𝑇. Following the suggestion of [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], this paper will only be using 𝑆 1 𝑥 and 𝑆 2 𝑥.…”

Section: Wavelet Scatteringmentioning

confidence: 99%

“…They exploit the scaling factor as the learning function to improve network representation to be of an orthonormal basis. The studies of scaling properties have also been used in the image domain [33,34]. The work of [33] involve the exploitation of discrete wavelet transform scaling properties to decompose synthetic aperture radar sec-ice texture providing multiscale/ multiresolution.…”

Section: Introductionmentioning

confidence: 99%

“…The work of [33] involve the exploitation of discrete wavelet transform scaling properties to decompose synthetic aperture radar sec-ice texture providing multiscale/ multiresolution. While [34] uses the concept of multi-scaling on the convolution filter size (M×N) and dilation rate to create multiple feature maps with different receptive fields to improve reconstruction of super-resolution remote-sensing images. The dilation rate is a control for dilated convolution filter where when the dilation is 1, it is like a standard convolution filter.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Intelligent Low-Complexity Computing Interleaving Wavelet Scattering Based Mobile Shuffling Network for Acoustic Scene Classification

Kek

Chin

Li³

2022

IEEE Access

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The key towards a low complexity model for convolution neural network is in controlling the number of parameters of the network and ensuring that the input representation is not extremely large. Hence, to tackle low complexity for acoustic scene classification (ASC), this paper proposed an enhanced wavelet scattering representation with a combination of mobile network modules and shuffling modules. While wavelet scattering is made up of wavelet transform with multiple wavelet scales, the averaging operation to make the wavelet scattering invariant to translation limit the maximum timescale. Hence, wavelet scattering is affected by Heisenberg Uncertainty Principle. However, creating an input representation with multiple timescales does not meet the brief of low complexity modelling. Hence, we proposed a simple mixing of the different timescales of the first order and second order. The result is an input representation with nearly the same dimension as the normal wavelet scattering, but with enhanced multiscale. To further leverage on the 'interleaved' wavelet scattering, this paper presents sub-spectral shuffling, which is inspired by shuffling modules that use stochasticity to improve the model's generalization. Unlike channel shuffle that shuffles channel-wise and spatial shuffle that shuffles pixelwise, sub-spectral shuffle aims at shuffling the feature maps frequency-wise with the concept of binning. Each bin is being shuffled such that the high-frequency spectrum is shuffled to low-frequency spectrum position allowing the model to learn the general acoustic profile of a scene rather than memorizing what is happening at the low-frequency or high-frequency spectrum, which is erratic for ASC. In addition, this paper also studied temporal shuffling, which shuffles the feature maps temporal-wise, and evaluated sub-spectral shuffling, temporal shuffling, and channel shuffling individually. Our results demonstrated the superiority of sub-spectral shuffling and the modularity of shuffling modules. We then evaluate various combinations of the three shuffling modules on three acoustic scene classification datasets. Our best model combines the three shuffling modules and achieves 70.6% classification accuracy on DCASE 2021 Task 1a dataset, 82.15% on ESC-50 dataset, 81% on Urbansound8K, with ~65K parameters and a size of 126.6KB. In addition, the inclusion of shuffling modules has shown to increase the model performance and sub-spectral shuffling is especially useful in improving logloss, which is a metric used to determine the confident level of the model.

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Section: Wavelet Scatteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Intelligent Low-Complexity Computing Interleaving Wavelet Scattering Based Mobile Shuffling Network for Acoustic Scene Classification

Kek

Chin

Li³

2022

IEEE Access

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show abstract

“…However, the selection of the distance threshold is very difficult, and the threshold selection is inappropriate, which easily leads to the segmented gestures including arms. Or when the gesture is close to the body, the palm information cannot be segmented [15]. In order to overcome the influence caused by the selection of the threshold, the depth mining of image information is carried out by detecting the information of the gesture skeleton node.…”

Section: Dynamic Gesture Contour Featurementioning

confidence: 99%

Dynamic Gesture Contour Feature Extraction Method Using Residual Network Transfer Learning

2021

Wireless Communications and Mobile Computing

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The current dynamic gesture contour feature extraction method has the problems that the recognition rate of dynamic gesture contour feature and the recognition accuracy of dynamic gesture type are low, the recognition time is long, and comprehensive is poor. Therefore, we propose a dynamic gesture contour feature extraction method using residual network transfer learning. Sensors are used to integrate dynamic gesture information. The distance between the dynamic gesture and the acquisition device is detected by transfer learning, the dynamic gesture image is segmented, and the characteristic contour image is initialized. The residual network method is used to accurately identify the contour and texture features of dynamic gestures. Fusion processing weights are used to trace the contour features of dynamic gestures frame by frame, and the contour area of dynamic gestures is processed by gray and binarization to realize the extraction of contour features of dynamic gestures. The results show that the dynamic gesture contour feature recognition rate of the proposed method is 91%, the recognition time is 11.6 s, and the dynamic gesture type recognition accuracy rate is 92%. Therefore, this method can effectively improve the recognition rate and type recognition accuracy of dynamic gesture contour features and shorten the time for dynamic gesture contour feature recognition, and the F value is 0.92, with good comprehensive performance.

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“…Chen et al designed a self-supervised spectral-spatial residual network [47]. The authors of [48] proposed multi-scale dilation residual block to deal with the super-resolution problem of remote sensing images.…”

Section: Image Super-resolution With Convolutional Neural Networkmentioning

confidence: 99%

Saliency-Guided Remote Sensing Image Super-Resolution

Liu

Zhao

et al. 2021

Remote Sensing

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Deep learning has recently attracted extensive attention and developed significantly in remote sensing image super-resolution. Although remote sensing images are composed of various scenes, most existing methods consider each part equally. These methods ignore the salient objects (e.g., buildings, airplanes, and vehicles) that have more complex structures and require more attention in recovery processing. This paper proposes a saliency-guided remote sensing image super-resolution (SG-GAN) method to alleviate the above issue while maintaining the merits of GAN-based methods for the generation of perceptual-pleasant details. More specifically, we exploit the salient maps of images to guide the recovery in two aspects: On the one hand, the saliency detection network in SG-GAN learns more high-resolution saliency maps to provide additional structure priors. On the other hand, the well-designed saliency loss imposes a second-order restriction on the super-resolution process, which helps SG-GAN concentrate more on the salient objects of remote sensing images. Experimental results show that SG-GAN achieves competitive PSNR and SSIM compared with the advanced super-resolution methods. Visual results demonstrate our superiority in restoring structures while generating remote sensing super-resolution images.

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End-to-End Super-Resolution for Remote-Sensing Images Using an Improved Multi-Scale Residual Network

Cited by 32 publications

References 29 publications

An Intelligent Low-Complexity Computing Interleaving Wavelet Scattering Based Mobile Shuffling Network for Acoustic Scene Classification

An Intelligent Low-Complexity Computing Interleaving Wavelet Scattering Based Mobile Shuffling Network for Acoustic Scene Classification

Dynamic Gesture Contour Feature Extraction Method Using Residual Network Transfer Learning

Saliency-Guided Remote Sensing Image Super-Resolution

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