Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network

Sun, Jian; Xu, Fangcao; Cervone, Guido; Gervais, Melissa; Wauthier, C.; Salvador, Mark

doi:10.31223/osf.io/jhqvz

Cited by 2 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The commonly used radiometric correction methods are absolute and relative radiometric correction [2]. However, absolute radiation correction requires a large number of imaging and other parameters [3], so relative radiation correction is used more often [4,5]. Relative radiometric correction uses the grayscale value of the feature in a multitemporal image instead of the reflected irradiance or reflectance of the feature [6,7] and matches the grayscale value of the image to be corrected with that of the reference image.…”

Section: Introductionmentioning

confidence: 99%

Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images

Fan

Zhang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The differences between the imaging environments of sensors lead to great differences in remote sensing images of the same area in different seasons. Relative radiation correction has high practical value as the main method to reduce such differences. However, the differences in vegetation radiation caused by seasonal changes are difficult to correct by traditional radiation correction methods. The corrected results also have difficulty achieving better results at the level of human eye visual perception. Moreover, the traditional measurement of the relative radiation correction result image quality index is not consistent with the human eye visual perception effect. To address the above two problems, this paper performs seasonal relative radiation correction on high-resolution remote sensing images by CycleGAN based on a convolutional neural network, including two transformations: 1) the transformation of remote sensing images from autumn-winter to spring-summer and 2) the transformation of remote sensing images from spring-summer to autumn-winter. The similarity between the relative radiation-corrected image and the reference image is measured by the convolutional neural network model with the ability to discriminate distances. The results show that the visual effect of this method is significantly better than that of other relative radiation correction methods, and the visual perception distance is consistent with the human eye visual perception judgment. The changed area still retains its original feature characteristics. The visual perception distance of the conversion from autumn-winter to spring-summer images is improved by 9% compared with other state-of-the-art methods. The visual perception distance of spring-summer images to autumn-winter images is improved by 3%. We expect that the method in this paper can be used for preprocessing to improve the accuracy of algorithms for remote sensing image classification, image change detection, etc.

show abstract

Section: Introductionmentioning

confidence: 99%

Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images

Fan

Zhang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Xu et al (2020) proposed an encoder-decoder neural network to retrieve surface emissivity by removing atmospheric effects from LWIR multiscan hyperspectral data. Sun et al (2021) exploited a time-dependent CNN for reflectivity retrieval in the visible and near infrared hyperspectral images, which outperforms the state-of-the-art methods. However, they are both based on simulated data and have not been applied to the real-world collected data.…”

Section: Introductionmentioning

confidence: 99%

Ill-posed Surface Emissivity Retrieval from Multi-Geometry Hyperspectral Images using a Hybrid Deep Neural Network

Xu¹,

Sun²,

Cervone³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Atmospheric correction is a fundamental task in remote sensing because observations are taken either of the atmosphere or looking through the atmosphere. Atmospheric correction errors can significantly alter the spectral signature of the observations, and lead to invalid classifications or target detection. This is even more crucial when working with hyperspectral data, where a precise measurement of spectral properties is required. State-of-the-art physicsbased atmospheric correction approaches require extensive prior knowledge about sensor characteristics, collection geometry, and environmental characteristics of the scene being collected. These approaches are computationally expensive, prone to inaccuracy due to lack of sufficient environmental and collection information, and often impossible for real-time applications.In this paper, a geometry-dependent hybrid neural network is proposed for automatic atmospheric correction using multi-scan hyperspectral data collected from different geometries. Atmospheric correction requires solving the downwelling, upwelling and transmission components of the atmosphere contemporaneously. However, these are dependent on each other. This leads to ill-posed mathematical problem solutions. The proposed network is able to maintain the physical relationship among different radiative components from the observed at-sensor total radiance.It is equipped with a novel network structure and a loss function, which are trained using two different longwave (7.5 -13.5µm) hyperspectral dataset, one simulated using MODTRAN, and the second observed using the Blue Heron instrument in a dedicated field study. The proposed network can characterize the atmosphere without any additional meteorological data.A grid-search method is also proposed to solve the temperature emissivity separation problem. Results show that the proposed network has the capacity to accurately characterize the atmosphere and estimate target emissivity spectra with a Mean Absolute Error (MAE) under 0.02 for 29 different materials. This solution can lead to accurate atmospheric correction to improve target detection for real time applications.

show abstract

Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network

Cited by 2 publications

References 25 publications

Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images

Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images

Ill-posed Surface Emissivity Retrieval from Multi-Geometry Hyperspectral Images using a Hybrid Deep Neural Network

Contact Info

Product

Resources

About