2,428 research outputs found
Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing
NASA Thesaurus Supplement: A three part cumulative supplement to the 1982 edition of the NASA Thesaurus (supplement 3)
The three part cumulative NASA Thesaurus Supplement to the 1982 edition of the NASA Thesaurus includes Part 1, Hierarchical Listing, Part 2, Access Vocabulary, and Part 3, Deletions. The semiannual supplement gives complete hierarchies for new terms and includes new term indications for entries new to this supplement
HATSouth: a global network of fully automated identical wide-field telescopes
HATSouth is the world's first network of automated and homogeneous telescopes
that is capable of year-round 24-hour monitoring of positions over an entire
hemisphere of the sky. The primary scientific goal of the network is to
discover and characterize a large number of transiting extrasolar planets,
reaching out to long periods and down to small planetary radii. HATSouth
achieves this by monitoring extended areas on the sky, deriving high precision
light curves for a large number of stars, searching for the signature of
planetary transits, and confirming planetary candidates with larger telescopes.
HATSouth employs 6 telescope units spread over 3 locations with large longitude
separation in the southern hemisphere (Las Campanas Observatory, Chile; HESS
site, Namibia; Siding Spring Observatory, Australia). Each of the HATSouth
units holds four 0.18m diameter f/2.8 focal ratio telescope tubes on a common
mount producing an 8.2x8.2 arcdeg field, imaged using four 4Kx4K CCD cameras
and Sloan r filters, to give a pixel scale of 3.7 arcsec/pixel. The HATSouth
network is capable of continuously monitoring 128 square arc-degrees. We
present the technical details of the network, summarize operations, and present
weather statistics for the 3 sites. On average each of the 6 HATSouth units has
conducted observations on ~500 nights over a 2-year time period, yielding a
total of more than 1million science frames at 4 minute integration time, and
observing ~10.65 hours per day on average. We describe the scheme of our data
transfer and reduction from raw pixel images to trend-filtered light curves and
transiting planet candidates. Photometric precision reaches ~6 mmag at 4-minute
cadence for the brightest non-saturated stars at r~10.5. We present detailed
transit recovery simulations to determine the expected yield of transiting
planets from HATSouth. (abridged)Comment: 25 pages, 11 figures, 1 table, submitted to PAS
Aerospace Medicine and Biology: A continuing bibliography (supplement 160)
This bibliography lists 166 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1976
- …