Purpose
This paper aims to present a novel approach of image super-resolution based on deep–shallow cascaded convolutional neural networks for reconstructing a clear and high-resolution (HR) remote sensing image from a low-resolution (LR) input.
Design/methodology/approach
The proposed approach directly learns the residuals and mapping between simulated LR and their corresponding HR remote sensing images based on deep and shallow end-to-end convolutional networks instead of assuming any specific restored models. Extra max-pooling and up-sampling are used to achieve a multiscale space by concatenating low- and high-level feature maps, and an HR image is generated by combining LR input and the residual image. This model ensures a strong response to spatially local input patterns by using a large filter and cascaded small filters. The authors adopt a strategy based on epochs to update the learning rate for boosting convergence speed.
Findings
The proposed deep network is trained to reconstruct high-quality images for low-quality inputs through a simulated dataset, which is generated with Set5, Set14, Berkeley Segmentation Data set and remote sensing images. Experimental results demonstrate that this model considerably enhances remote sensing images in terms of spatial detail and spectral fidelity and outperforms state-of-the-art SR methods in terms of peak signal-to-noise ratio, structural similarity and visual assessment.
Originality/value
The proposed method can reconstruct an HR remote sensing image from an LR input and significantly improve the quality of remote sensing images in terms of spatial detail and fidelity.
A gas sensor with low power consumption was manufactured using a suspended microhotplatform (MHP) fabricated by MEMS technology. Tin oxide nanoparticles, synthesized by the hydrothermal method, were deposited on the MHP by dip coating in the form of slurry. The obtained SEM images indicated that nanoparticles were well connected and uniform in size. The gas sensing performance was also investigated in both dynamic and static test systems. In the dynamic test system, the response (R air /R gas) of the sensor exhibited a representative linear relationship with hydrogen at a power of 14.42 mW and changed from 1.17 to 4.51 at 5 to 100 ppm hydrogen. In the static test system, the sensor demonstrated an obvious response to ethanol, ammonia, and glycol; the response to the target gas of ammonia at a concentration of 120 ppm reached up to 23.94. The power consumption of the sensor was only 16 mW with a platinum (Pt) heating resistor at 255 ℃.
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