Multiple geometry atmospheric correction for image spectroscopy using deep learning

Xu, Fangcao; Cervone, Guido; Franch, Gabriele; Salvador, Mark

doi:10.1117/1.jrs.14.024518

Cited by 10 publications

(7 citation statements)

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“…Based on the sensitivity study done by Xu et al (2020), the solar components are at least 3 orders of magnitude smaller than thermal signatures in the longwave infrared spectrum, which is negligible in this research. Thus…”

Section: Multi-geometry Radiative Transfer Modelmentioning

confidence: 90%

“…There are currently only limited proposed solutions that also take spectra acquired simultaneously from multiple geometries into consideration. 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.…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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Section: Multi-geometry Radiative Transfer Modelmentioning

confidence: 90%

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

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show abstract

“…convolution-based neural networks. For example, Xu et al 8 employs a Convolutional Neural Network (CNN) and autoencoder model structure to perform atmospheric correction and target detection. Sun et al 9 uses similar methods to perform near-real-time atmospheric correction from time-dependent (video) training data.…”

Section: Atmospheric Correction and Machine Learningmentioning

confidence: 99%

A neural differential equation formulation for modeling atmospheric effects in hyperspectral images

Koch

Forland

Doster

et al. 2023

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX

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Atmospheric correction is the process for removing atmospheric effects from spectral data; a necessary step for recovering salient spectral properties. The complex interactions between the atmosphere and light are dominated by absorbance and scattering physics. Existing methods for modeling atmospheric interactions typically rely on deep knowledge of relevant environmental conditions and high-fidelity numerical simulations of the governing physics in order to obtain accurate estimates of these effects. Additionally, existing approaches often require a subject matter expert for pre/post-processing of the data. Model-based approaches for removing atmospheric effects struggle in situations where such domain expertise is not available, and require significant human effort and computational power even when that expertise is available. In contrast, we propose a data-driven approach the uses Neural Differential Equations (NDEs) to accurately learn the interactions between electromagnetic radiation and the atmospheric without access to location specific environmental information. Once trained, the NDE can be applied bi-directionally; to apply or remove atmospheric effects. We demonstrate the effectiveness and utility of these techniques on an example multi-spectral scene.

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“…Ogut, Bosch-Lluis, and Reising (2019) applied a deep-learning-based calibration technique for the calibration of high-frequency airborne microwave and millimeter-wave radiometer instruments. Xu, Cervone, Franch, and Salvador (2020) presented an artificial intelligence/deep learningbased solution to characterize the atmosphere at different vantage points and to retrieve target spectral properties. A deep learning emulator approach for atmospheric correction was developed, suggesting that a deep learning model can be trained to emulate a complex physical process (Duffy et al, 2019).…”

Section: Radiometric (Spectral) Processingmentioning

confidence: 99%

Deep learning for processing and analysis of remote sensing big data: a technical review

2021

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In recent years, the rapid development of Earth observation technology has produced an increasing growth in remote sensing big data, posing serious challenges for effective and efficient processing and analysis. Meanwhile, there has been a massive rise in deeplearning-based algorithms for remote sensing tasks, providing a large opportunity for remote sensing big data. In this article, we initially summarize the features of remote sensing big data. Subsequently, following the pipeline of remote sensing tasks, a detailed and technical review is conducted to discuss how deep learning has been applied to the processing and analysis of remote sensing data, including geometric and radiometric processing, cloud masking, data fusion, object detection and extraction, landuse/cover classification, change detection and multitemporal analysis. Finally, we discussed technical challenges and concluded directions for future research in deep-learning-based applications for remote sensing big data.

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Multiple geometry atmospheric correction for image spectroscopy using deep learning

Cited by 10 publications

References 0 publications

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

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

A neural differential equation formulation for modeling atmospheric effects in hyperspectral images

Deep learning for processing and analysis of remote sensing big data: a technical review

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