scholarly journals Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks

2019 ◽  
Vol 11 (22) ◽  
pp. 2635 ◽  
Author(s):  
Massimiliano Gargiulo ◽  
Antonio Mazza ◽  
Raffaele Gaetano ◽  
Giuseppe Ruello ◽  
Giuseppe Scarpa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Near Infrared ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Limiting Factor ◽  
Considerable Saving ◽  
Access Policy ◽  
Single Sensor ◽  
Sentinel 2

Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their associated open access policy. Due to a sensor design trade-off, images are acquired (and delivered) at different spatial resolutions (10, 20 and 60 m) according to specific sets of wavelengths, with only the four visible and near infrared bands provided at the highest resolution (10 m). Although this is not a limiting factor in general, many applications seem to emerge in which the resolution enhancement of 20 m bands may be beneficial, motivating the development of specific super-resolution methods. In this work, we propose to leverage Convolutional Neural Networks (CNNs) to provide a fast, upscalable method for the single-sensor fusion of Sentinel-2 (S2) data, whose aim is to provide a 10 m super-resolution of the original 20 m bands. Experimental results demonstrate that the proposed solution can achieve better performance with respect to most of the state-of-the-art methods, including other deep learning based ones with a considerable saving of computational burden.

scholarly journals PlanetScope Radiometric Normalization and Sentinel-2 Super-Resolution (2.5 m): A Straightforward Spectral-Spatial Fusion of Multi-Satellite Multi-Sensor Images Using Residual Convolutional Neural Networks

2020 ◽  
Vol 12 (15) ◽  
pp. 2366
Author(s):  
Nicolas Latte ◽  
Philippe Lejeune
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
State Of The Art ◽  
Super Resolution ◽  
Neural Network Architecture ◽  
Research Areas ◽  
Novel Method ◽  
Spatial Fusion ◽  
Sentinel 2

Sentinel-2 (S2) imagery is used in many research areas and for diverse applications. Its spectral resolution and quality are high but its spatial resolutions, of at most 10 m, is not sufficient for fine scale analysis. A novel method was thus proposed to super-resolve S2 imagery to 2.5 m. For a given S2 tile, the 10 S2 bands (four at 10 m and six at 20 m) were fused with additional images acquired at higher spatial resolution by the PlanetScope (PS) constellation. The radiometric inconsistencies between PS microsatellites were normalized. Radiometric normalization and super-resolution were achieved simultaneously using state-of–the-art super-resolution residual convolutional neural networks adapted to the particularities of S2 and PS imageries (including masks of clouds and shadows). The method is described in detail, from image selection and downloading to neural network architecture, training, and prediction. The quality was thoroughly assessed visually (photointerpretation) and quantitatively, confirming that the proposed method is highly spatially and spectrally accurate. The method is also robust and can be applied to S2 images acquired worldwide at any date.

scholarly journals Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training

2018 ◽  
Vol 10 (3) ◽  
pp. 394 ◽  
Author(s):  
Darren Pouliot ◽  
Rasim Latifovic ◽  
Jon Pasher ◽  
Jason Duffe
Keyword(s):  
Neural Networks ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Convolution Neural Networks ◽  
Sentinel 2

scholarly journals Single-Image Super-Resolution of Sentinel-2 Low Resolution Bands with Residual Dense Convolutional Neural Networks

2021 ◽  
Vol 13 (24) ◽  
pp. 5007
Author(s):  
Luis Salgueiro ◽  
Javier Marcello ◽  
Verónica Vilaplana
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Superior Performance ◽  
Single Image ◽  
Low Resolution ◽  
Spatial Coverage ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Sentinel 2

Sentinel-2 satellites have become one of the main resources for Earth observation images because they are free of charge, have a great spatial coverage and high temporal revisit. Sentinel-2 senses the same location providing different spatial resolutions as well as generating a multi-spectral image with 13 bands of 10, 20, and 60 m/pixel. In this work, we propose a single-image super-resolution model based on convolutional neural networks that enhances the low-resolution bands (20 m and 60 m) to reach the maximal resolution sensed (10 m) at the same time, whereas other approaches provide two independent models for each group of LR bands. Our proposed model, named Sen2-RDSR, is made up of Residual in Residual blocks that produce two final outputs at maximal resolution, one for 20 m/pixel bands and the other for 60 m/pixel bands. The training is done in two stages, first focusing on 20 m bands and then on the 60 m bands. Experimental results using six quality metrics (RMSE, SRE, SAM, PSNR, SSIM, ERGAS) show that our model has superior performance compared to other state-of-the-art approaches, and it is very effective and suitable as a preliminary step for land and coastal applications, as studies involving pixel-based classification for Land-Use-Land-Cover or the generation of vegetation indices.

Efficient downscaling of satellite oceanographic data with convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 46-47
Author(s):  
Nikita Saxena
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Sea Surface ◽  
Clear Sky ◽  
Oceanographic Data ◽  
Computationally Expensive ◽  
Sky Conditions ◽  
Data Inference

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.

scholarly journals RSPCN: Super-Resolution of Digital Elevation Model Based on Recursive Sub-Pixel Convolutional Neural Networks

2021 ◽  
Vol 10 (8) ◽  
pp. 501
Author(s):  
Ruichen Zhang ◽  
Shaofeng Bian ◽  
Houpu Li
Keyword(s):  
Neural Networks ◽  
Digital Elevation Model ◽  
Convolutional Neural Networks ◽  
Adaptive Learning ◽  
Nearest Neighbor ◽  
Super Resolution ◽  
Recursion Theory ◽  
Research Issues ◽  
Digital Elevation ◽  
Elevation Model

The digital elevation model (DEM) is known as one kind of the most significant fundamental geographical data models. The theory, method and application of DEM are hot research issues in geography, especially in geomorphology, hydrology, soil and other related fields. In this paper, we improve the efficient sub-pixel convolutional neural networks (ESPCN) and propose recursive sub-pixel convolutional neural networks (RSPCN) to generate higher-resolution DEMs (HRDEMs) from low-resolution DEMs (LRDEMs). Firstly, the structure of RSPCN is described in detail based on recursion theory. This paper explores the effects of different training datasets, with the self-adaptive learning rate Adam algorithm optimizing the model. Furthermore, the adding-“zero” boundary method is introduced into the RSPCN algorithm as a data preprocessing method, which improves the RSPCN method’s accuracy and convergence. Extensive experiments are conducted to train the method till optimality. Finally, comparisons are made with other traditional interpolation methods, such as bicubic, nearest-neighbor and bilinear methods. The results show that our method has obvious improvements in both accuracy and robustness and further illustrate the feasibility of deep learning methods in the DEM data processing area.

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