Visible and near-infrared separation using conditional generative adversarial network

2018 ◽  
Author(s):  
Younghyeon Park ◽  
Byeungwoo Jeon
Keyword(s):  
Near Infrared ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network

2021 ◽  
Vol 13 (8) ◽  
pp. 1512
Author(s):  
Quan Xiong ◽  
Liping Di ◽  
Quanlong Feng ◽  
Diyou Liu ◽  
Wei Liu ◽  
...  
Keyword(s):  
Land Surface ◽  
Near Infrared ◽  
Similarity Index ◽  
Data Availability ◽  
Optical Data ◽  
Generative Adversarial Network ◽  
Spectral Angle Mapper ◽  
Adversarial Network ◽  
Target Date ◽  
Sentinel 2

Sentinel-2 images have been widely used in studying land surface phenomena and processes, but they inevitably suffer from cloud contamination. To solve this critical optical data availability issue, it is ideal to fuse Sentinel-1 and Sentinel-2 images to create fused, cloud-free Sentinel-2-like images for facilitating land surface applications. In this paper, we propose a new data fusion model, the Multi-channels Conditional Generative Adversarial Network (MCcGAN), based on the conditional generative adversarial network, which is able to convert images from Domain A to Domain B. With the model, we were able to generate fused, cloud-free Sentinel-2-like images for a target date by using a pair of reference Sentinel-1/Sentinel-2 images and target-date Sentinel-1 images as inputs. In order to demonstrate the superiority of our method, we also compared it with other state-of-the-art methods using the same data. To make the evaluation more objective and reliable, we calculated the root-mean-square-error (RSME), R2, Kling–Gupta efficiency (KGE), structural similarity index (SSIM), spectral angle mapper (SAM), and peak signal-to-noise ratio (PSNR) of the simulated Sentinel-2 images generated by different methods. The results show that the simulated Sentinel-2 images generated by the MCcGAN have a higher quality and accuracy than those produced via the previous methods.

scholarly journals SEGMENTATION OF SINGLE STANDING DEAD TREES IN HIGH-RESOLUTION AERIAL IMAGERY WITH GENERATIVE ADVERSARIAL NETWORK-BASED SHAPE PRIORS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 717-723
Author(s):  
P. Polewski ◽  
J. Shelton ◽  
W. Yao ◽  
M. Heurich
Keyword(s):  
Near Infrared ◽  
Spectral Response ◽  
Mathematical Formulation ◽  
Dimensional Manifold ◽  
Shape Priors ◽  
Tree Crown ◽  
Infrared Band ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Adversarial Network

Abstract. The use of multispectral imagery for monitoring biodiversity in ecosystems is becoming widespread. A key parameter of forest ecosystems is the distribution of dead wood. This work addresses the segmentation of individual dead tree crowns in nadir-view aerial infrared imagery. While dead vegetation produces a distinct spectral response in the near infrared band, separating adjacent trees within large swaths of dead stands remains a challenge. We tackle this problem by casting the segmentation task within the active contour framework, a mathematical formulation combining learned models of the object’s shape and appearance as prior information. We explore the use of a deep convolutional generative adversarial network (DCGAN) in the role of the shape model, replacing the original linear mixture-of-eigenshapes formulation. Also, we rely on probabilities obtained from a deep fully convolutional network (FCN) as the appearance prior. Experiments conducted on manually labeled reference polygons show that the DCGAN is able to learn a low-dimensional manifold of tree crown shapes, outperforming the eigenshape model with respect to the similarity of the reproduced and referenced shapes on about 45 % of the test samples. The DCGAN is successful mostly for less convex shapes, whereas the baseline remains superior for more regular tree crown polygons.

A DenseUnet generative adversarial network for near-infrared face image colorization

2021 ◽  
Vol 183 ◽  
pp. 108007
Author(s):  
Jiangtao Xu ◽  
Kaige Lu ◽  
Xingping Shi ◽  
Shuzhen Qin ◽  
Han Wang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Face Image ◽  
Generative Adversarial Network ◽  
Image Colorization ◽  
Adversarial Network

scholarly journals Generation of the NIR Spectral Band for Satellite Images with Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5646 ◽  
Author(s):  
Svetlana Illarionova ◽  
Dmitrii Shadrin ◽  
Alexey Trekin ◽  
Vladimir Ignatiev ◽  
Ivan Oseledets
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Near Infrared ◽  
Spectral Band ◽  
Model Performance ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
High Resolution Satellite Imagery ◽  
Segmentation Task ◽  
The Impact

The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for landcover classification, especially concerning vegetation assessment. Despite the usefulness of NIR, it does not always accomplish common RGB. Modern achievements in image processing via deep neural networks make it possible to generate artificial spectral information, for example, to solve the image colorization problem. In this research, we aim to investigate whether this approach can produce not only visually similar images but also an artificial spectral band that can improve the performance of computer vision algorithms for solving remote sensing tasks. We study the use of a generative adversarial network (GAN) approach in the task of the NIR band generation using only RGB channels of high-resolution satellite imagery. We evaluate the impact of a generated channel on the model performance to solve the forest segmentation task. Our results show an increase in model accuracy when using generated NIR compared to the baseline model, which uses only RGB (0.947 and 0.914 F1-scores, respectively). The presented study shows the advantages of generating the extra band such as the opportunity to reduce the required amount of labeled data.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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