SRT: A Spectral Reconstruction Network for GF-1 PMS Data Based on Transformer and ResNet

Mu, Kai; Zhang, Ziyuan; Qian, Yurong; Liu, Suhong; Sun, Mengting; Qi, Ranran

doi:10.3390/rs14133163

Cited by 6 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the mean relative error (MRAE) and root mean square error (RMSE) are selected as evaluation metrics for spectral reconstruction [ 31 , 32 ]. MRAE is utilized to measure the average relative error between the reconstructed spectra and the actual spectra, as defined by Equation (1).…”

Section: Methodsmentioning

confidence: 99%

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

Zhao,

Luo,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

Aircraft failures can result in the leakage of fuel, hydraulic oil, or other lubricants onto the runway during landing or taxiing. Damage to fuel tanks or oil lines during hard landings or accidents can also contribute to these spills. Further, improper maintenance or operational errors may leave oil traces on the runway before take-off or after landing. Identifying oil spills in airport runway videos is crucial to flight safety and accident investigation. Advanced image processing techniques can overcome the limitations of conventional RGB-based detection, which struggles to differentiate between oil spills and sewage due to similar coloration; given that oil and sewage have distinct spectral absorption patterns, precise detection can be performed based on multispectral images. In this study, we developed a method for spectrally enhancing RGB images of oil spills on airport runways to generate HSI images, facilitating oil spill detection in conventional RGB imagery. To this end, we employed the MST++ spectral reconstruction network model to effectively reconstruct RGB images into multispectral images, yielding improved accuracy in oil detection compared with other models. Additionally, we utilized the Fast R-CNN oil spill detection model, resulting in a 5% increase in Intersection over Union (IOU) for HSI images. Moreover, compared with RGB images, this approach significantly enhanced detection accuracy and completeness by 25.3% and 26.5%, respectively. These findings clearly demonstrate the superior precision and accuracy of HSI images based on spectral reconstruction in oil spill detection compared with traditional RGB images. With the spectral reconstruction technique, we can effectively make use of the spectral information inherent in oil spills, thereby enhancing detection accuracy. Future research could delve deeper into optimization techniques and conduct extensive validation in real airport environments. In conclusion, this spectral reconstruction-based technique for detecting oil spills on airport runways offers a novel and efficient approach that upholds both efficacy and accuracy. Its wide-scale implementation in airport operations holds great potential for improving aviation safety and environmental protection.

show abstract

Section: Methodsmentioning

confidence: 99%

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

Zhao,

Luo,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In recent years, the continuous development of remote-sensing satellite technology has made acquiring remote-sensing images easy. 4,5,6,7,8 High-resolution remote sensing images can provide finer spectral, texture, and other features, and fast and accurate extraction of roads from high-resolution remote sensing images is a convenient and effective method for road extraction. 9,10,11,12 However, remote sensing satellite images are taken from a high altitude, so they cover a wide area.…”

Section: Introductionmentioning

confidence: 99%

“…Obtaining detailed and accurate road information plays a critical role in urban planning, 1 autonomous driving, 2 geographic information system upgrading, 3 and other fields. In recent years, the continuous development of remote-sensing satellite technology has made acquiring remote-sensing images easy 4 , 5 , 6 , 7 , 8 . High-resolution remote sensing images can provide finer spectral, texture, and other features, and fast and accurate extraction of roads from high-resolution remote sensing images is a convenient and effective method for road extraction 9 , 10 , 11 , 12 .…”

Section: Introductionmentioning

confidence: 99%

JAED-Net: joint attention encoder–decoder network for road extraction from remote sensing images

Tuerxun

Qian

et al. 2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

In road extraction from remote sensing images, the road environment is complex and blocked by trees, buildings, and other objects, making it impossible to extract practical (continuous and complete) road information. We propose a joint attention encoder-decoder network (JAED-Net) for road extraction from remote sensing images to solve these problems. First, JAED-Net encodes a modified residual network as the backbone for road feature extraction. A joint attention module is added to the encoder to enhance the network's ability to learn and express road features. Then, strip convolution is added to the decoder, so the network retains more spatial features, such as the width and connectivity of roads during upsampling. Finally, a hybrid weighted loss function is introduced to train the network and ensure stability because of the unbalanced ratio of road and background pixels in remote sensing images. Experimental validation of the proposed network is performed on three publicly available datasets.

show abstract

“…We opted for the convolutional architecture instead of a recurrent alternative due to its training and inference efficiency. The ResNeXt architecture was chosen due to the versatility of ResNet-type [18] architectures, which allows for the design of models that can be customized for specific tasks by stacking multiple blocks of the same type. Moreover, we empirically showed that the inclusion of multiple past radar measurements led to more accurate predictions further in the future.…”

Section: Introductionmentioning

confidence: 99%

NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes

et al. 2022

View full text Add to dashboard Cite

With the recent increase in the occurrence of severe weather phenomena, the development of accurate weather nowcasting is of paramount importance. Among the computational methods that are used to predict the evolution of weather, deep learning techniques offer a particularly appealing solution due to their capability for learning patterns from large amounts of data and their fast inference times. In this paper, we propose a convolutional network for weather forecasting that is based on radar product prediction. Our model (NeXtNow) adapts the ResNeXt architecture that has been proposed in the computer vision literature to solve the spatiotemporal prediction problem. NeXtNow consists of an encoder–decoder convolutional architecture, which maps radar measurements from the past onto radar measurements that are recorded in the future. The ResNeXt architecture was chosen as the basis for our network due to its flexibility, which allows for the design of models that can be customized for specific tasks by stacking multiple blocks of the same type. We validated our approach using radar data that were collected from the Romanian National Meteorological Administration (NMA) and the Norwegian Meteorological Institute (MET) and we empirically showed that the inclusion of multiple past radar measurements led to more accurate predictions further in the future. We also showed that NeXtNow could outperform XNow, which is a convolutional architecture that has previously been proposed for short-term radar data prediction and has a performance that is comparable to those of other similar approaches in the nowcasting literature. Compared to XNow, NeXtNow provided improvements to the critical success index that ranged from 1% to 17% and improvements to the root mean square error that ranged from 5% to 6%.

show abstract

SRT: A Spectral Reconstruction Network for GF-1 PMS Data Based on Transformer and ResNet

Cited by 6 publications

References 39 publications

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

JAED-Net: joint attention encoder–decoder network for road extraction from remote sensing images

NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes

Contact Info

Product

Resources

About