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Artificial Intelligence in Nighttime Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (1 October 2023) | Viewed by 16049

Special Issue Editor

Payne Institute for Public Policy, Colorado School of Mines, 816 15th St, Golden, CO 80401, USA
Interests: artificial intelligence; multispectral remote sensing; nighttime observations; gas flares; VIIRS Nightfire; VIIRS Boat Detector; high performance computing; pattern recognition; computational geophysics

Special Issue Information

Dear Colleagues,

The Special Issue is focused on the crossroads of two complementary technologies, remote sensing and machine learning. Remote sensing is facing a steady exponential growth of real-time and archive data volume, spatial resolution and versatility of the sensor platforms. The new generation of massively parallel graphical and tensor processors has enabled the machine learning with deep convolutional and recursive neural networks for detection, classification and annotation in the natural language of objects visible from space, temporal change events and their interactions in static scenes and dynamic scenarios. This market opportunity for storage and compute power was recognized by Google, Amazon and Azure cloud platforms, which are now hosting high demand satellite data, provide GPU clusters for neural nets, Earth Engine for geospatial data analysis, and virtual satellite ground stations for real-time data ingest.

Application of the machine learning to the remote sensing data remains a challenge because the satellite imagery is often gigabytes of size, and may contain over a dozen channels, some outside of the visible spectrum and stored in spatially referenced formats. The authors are encouraged to submit research papers on the machine learning applications in a wide scope of remote sensing problems, including, but not limited to, data quality control, image enhancement and fusion from visible, infrared and microwave bands, object detection and tracking, such as roads, build-up, vessels or trees, and change prediction and propagation forecast, e.g. forest fires. This may require the design of new neural network topologies, training techniques, cloud data processing pipelines and massively parallel clusters. To assure the scientific quality and reproducibility of the results, the authors are encouraged to follow the open source model in sharing the training data, source code and network weights as a digital supplement to their papers.

The key phrase of this Special Issue is “night-time remote sensing”. This is a new and fascinating direction of research similar to astronomy, when the telescopes are looking down from space on earth. It detects light emitting sources, contrary to the reflective surfaces visible under the sunlight. Non-trivial and computationally intensive tasks of the detection and classification of light and heat coming from cities, transportation, oil and gas exploration, industrial sites, image fusion and superresolution, suppression of clouds and aurora all will profit from automation provided by machine learning and artificial intelligence.

Dr. Mikhail Zhizhin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Nighttime remote sensing
  • City lights
  • Gas flares
  • Machine learning
  • Neural networks
  • Image fusion
  • Superresolution

Published Papers (6 papers)

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Research

21 pages, 5036 KiB  
Article
Automated VIIRS Boat Detection Based on Machine Learning and Its Application to Monitoring Fisheries in the East China Sea
by Masaki E. Tsuda, Nathan A. Miller, Rui Saito, Jaeyoon Park and Yoshioki Oozeki
Remote Sens. 2023, 15(11), 2911; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112911 - 02 Jun 2023
Cited by 2 | Viewed by 1886
Abstract
Remote sensing is essential for monitoring fisheries. Optical sensors such as the day–night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) have been a crucial tool for detecting vessels fishing at night. It remains challenging to ensure stable detections under various [...] Read more.
Remote sensing is essential for monitoring fisheries. Optical sensors such as the day–night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) have been a crucial tool for detecting vessels fishing at night. It remains challenging to ensure stable detections under various conditions affected by the clouds and the moon. Here, we develop a machine learning based algorithm to generate automatic and consistent vessel detection. As DNB data are large and highly imbalanced, we design a two-step approach to train our model. We evaluate its performance using independent vessel position data acquired from on-ship radar. We find that our algorithm demonstrates comparable performance to the existing VIIRS boat detection algorithms, suggesting its possible application to greater temporal and spatial scales. By applying our algorithm to the East China Sea as a case study, we reveal a recent increase in fishing activity by vessels using bright lights. Our VIIRS boat detection results aim to provide objective information for better stock assessment and management of fisheries. Full article
(This article belongs to the Special Issue Artificial Intelligence in Nighttime Remote Sensing)
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19 pages, 9016 KiB  
Article
Night-Time Detection of Subpixel Emitters with VIIRS Mid-Wave Infrared Bands M12–M13
by Mikhail Zhizhin, Christopher Elvidge and Alexey Poyda
Remote Sens. 2023, 15(5), 1189; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051189 - 21 Feb 2023
Cited by 2 | Viewed by 1411
Abstract
In this paper, we present a new approach to subpixel infrared (IR) emitter detection in VIIRS mid-wave (MWIR) infrared bands M12–M13 at night, based on the presence of a tightly clustered background diagonal present in full granule scattergrams of M12 versus M13 radiances. [...] Read more.
In this paper, we present a new approach to subpixel infrared (IR) emitter detection in VIIRS mid-wave (MWIR) infrared bands M12–M13 at night, based on the presence of a tightly clustered background diagonal present in full granule scattergrams of M12 versus M13 radiances. This diagonal is found universally in night-time VIIRS data collected worldwide. The diagonal feature is absent during the day due to solar reflectance. The existence of the diagonal is attributed to close spacing in the bandpass centers of the VIIRS’ two MWIR bands. Apparently, the M12 and M13 emissivities are highly correlated to background objects, such clouds, ocean and land surfaces. The VIIRS Nightfire (VNF) algorithm detects pixels containing IR emitters based on their departure from the background diagonal. This paper outlines the method and compares VNF results with those from MODIS and VIIRS hotspot detection. Full article
(This article belongs to the Special Issue Artificial Intelligence in Nighttime Remote Sensing)
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16 pages, 5470 KiB  
Article
Revealing the Spatiotemporal Patterns of Anthropogenic Light at Night within Ecological Conservation Redline Using Series Satellite Nighttime Imageries (2000–2020)
by Fangming Jiang, Yang Ye, Zhen He, Jianwu Cai, Aihua Shen, Rui Peng, Binjie Chen, Chen Tong and Jinsong Deng
Remote Sens. 2022, 14(14), 3461; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143461 - 19 Jul 2022
Cited by 3 | Viewed by 1615
Abstract
With the rapid urbanization process, the construction of lighting facilities is increasing, whereas artificial light at nighttime (ALAN) negatively affects organisms in protected areas and threatens ecosystems. Therefore, a deep research of ALAN within protected areas is significant for better preserving biodiversity by [...] Read more.
With the rapid urbanization process, the construction of lighting facilities is increasing, whereas artificial light at nighttime (ALAN) negatively affects organisms in protected areas and threatens ecosystems. Therefore, a deep research of ALAN within protected areas is significant for better preserving biodiversity by scientific ALAN management. Taking the ecological conservation redline (ECR) in Zhejiang Province as a case study, we consistently applied remotely sensed ALAN data from 2000 to 2020 for exploring spatiotemporal changing characteristics of ALAN. More importantly, both human living and ecological safety were considered to classify ALAN status in 2019 in order to propose rational suggestions for management. The results showed ALAN intensified and expanded, increasing from 3.05 × 1012 nW·sr−1 to 5.24 × 1013 nW·sr−1 at an average growth rate of 2.35 × 1012 nW·sr−1·year−1. Hotspot analysis and bivariate spatial clustering identified the aggregation situation of ALAN and the population. They showed that statistically significant ALAN hotspots accounted for only 20.40% of the study area while providing 51.82% of the total ALAN. Based on the mismatches between human demand and ALAN supply, two crucial areas were identified where regulation is needed most, and targeted policy recommendations were put forward. The study results can contribute to the effective regulation of ALAN in protected areas. Full article
(This article belongs to the Special Issue Artificial Intelligence in Nighttime Remote Sensing)
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19 pages, 14789 KiB  
Article
Spectral Unmixing Based Approach for Measuring Gas Flaring from VIIRS NTL Remote Sensing Data: Case of the Flare FIT-M8-101A-1U, Algeria
by Fatima Zohra Benhalouche, Farah Benharrats, Mohammed Amine Bouhlala and Moussa Sofiane Karoui
Remote Sens. 2022, 14(10), 2305; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102305 - 10 May 2022
Cited by 5 | Viewed by 2193
Abstract
During the oil extraction procedure, natural gases escape from wells, and the process of recuperating such gases requires important investments from oil and gas companies. That is why, most often, they favor burning them with flares. This practice, which is frequently employed by [...] Read more.
During the oil extraction procedure, natural gases escape from wells, and the process of recuperating such gases requires important investments from oil and gas companies. That is why, most often, they favor burning them with flares. This practice, which is frequently employed by oil-producing companies, is a major cause of greenhouse gas emissions. Under growing demands from the World Bank and environmental defenders, many producer countries are devoted to decreasing gas flaring. For this reason, several researchers in the oil and gas industry, academia, and governments are working to propose new methods for estimating flared gas volumes, and among the most used techniques are those that exploit remote sensing data, particularly Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Light (NTL) ones. Indeed, it is possible to extract, from such data, some physical parameters of flames produced by gas flares. In this investigation, a linear spectral unmixing-based approach, which addresses the spectral variability phenomenon, was designed to estimate accurate physical parameters from VIIRS NTL data. Then, these parameters are used to derive flared gas volumes through intercepting zero polynomial regression models that exploit in situ measurements. Experiments based on synthetic data were first conducted to validate the proposed linear spectral unmixing-based approach. Second, experiments based on real VIIRS NTL data covering the flare, named FIT-M8-101A-1U and located in the Berkine basin (Hassi Messaoud) in Algeria, were carried out. Then, the obtained flared gas volumes were compared with in situ measurements. Full article
(This article belongs to the Special Issue Artificial Intelligence in Nighttime Remote Sensing)
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18 pages, 4730 KiB  
Article
Cross-Sensor Nighttime Lights Image Calibration for DMSP/OLS and SNPP/VIIRS with Residual U-Net
by Dmitry Nechaev, Mikhail Zhizhin, Alexey Poyda, Tilottama Ghosh, Feng-Chi Hsu and Christopher Elvidge
Remote Sens. 2021, 13(24), 5026; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245026 - 10 Dec 2021
Cited by 13 | Viewed by 3593
Abstract
Remote sensing of nighttime lights (NTL) is widely used in socio-economic studies of economic growth, urbanization, stability of power grid, environmental light pollution, pandemics and military conflicts. Currently, NTL data are collected with two sensors: (1) Operational Line-scan System (OLS) onboard the satellites [...] Read more.
Remote sensing of nighttime lights (NTL) is widely used in socio-economic studies of economic growth, urbanization, stability of power grid, environmental light pollution, pandemics and military conflicts. Currently, NTL data are collected with two sensors: (1) Operational Line-scan System (OLS) onboard the satellites from the Defense Meteorology Satellite Program (DMSP) and (2) Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi NPP (SNPP) and NOAA-20 satellites from the Joint Polar Satellite System (JPSS). However, the nighttime images acquired by these two sensors are incompatible in spatial resolution and dynamic range. To address this problem, we propose a method for the cross-sensor calibration with residual U-net convolutional neural network (CNN). The CNN produces DMSP-like NTL composites from the VIIRS annual NTL composites. The pixel radiances predicted from VIIRS are highly correlated with NTL observed with OLS (0.96 < R2 < 0.99). The method can be used to extend long-term series of annual NTL after the end of DMSP mission or to cross-calibrate same year NTL from different satellites to study diurnal variations. Full article
(This article belongs to the Special Issue Artificial Intelligence in Nighttime Remote Sensing)
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19 pages, 35774 KiB  
Article
Extending the DMSP Nighttime Lights Time Series beyond 2013
by Tilottama Ghosh, Kimberly E. Baugh, Christopher D. Elvidge, Mikhail Zhizhin, Alexey Poyda and Feng-Chi Hsu
Remote Sens. 2021, 13(24), 5004; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245004 - 09 Dec 2021
Cited by 21 | Viewed by 3878
Abstract
Data collected by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) sensors have been archived and processed by the Earth Observation Group (EOG) at the National Oceanic and Atmospheric Administration (NOAA) to make global maps of nighttime images since 1994. Over the [...] Read more.
Data collected by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) sensors have been archived and processed by the Earth Observation Group (EOG) at the National Oceanic and Atmospheric Administration (NOAA) to make global maps of nighttime images since 1994. Over the years, the EOG has developed automatic algorithms to make Stable Lights composites from the OLS visible band data by removing the transient lights from fires and fishing boats. The ephemeral lights are removed based on their high brightness and short duration. However, the six original satellites collecting DMSP data gradually shifted from day/night orbit to dawn/dusk orbit, which is to an earlier overpass time. At the beginning of 2014, the F18 satellite was no longer collecting usable nighttime data, and the focus had shifted to processing global nighttime images from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data. Nevertheless, it was soon discovered that the F15 and F16 satellites had started collecting pre-dawn nighttime data from 2012 onwards. Therefore, the established algorithms of the previous years were extended to process OLS data from 2013 onwards. Moreover, the existence of nighttime data from three overpass times for the year 2013–DMSP satellites F18 and F15 from early evening and pre-dawn, respectively, and the VIIRS from after midnight, made it possible to intercalibrate the images of three different overpass times and study the diurnal pattern of nighttime lights. Full article
(This article belongs to the Special Issue Artificial Intelligence in Nighttime Remote Sensing)
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