153 research outputs found
A Review on Deep Learning in UAV Remote Sensing
Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images,
time-series, natural language, audio, video, and many others. In the remote
sensing field, surveys and literature revisions specifically involving DNNs
algorithms' applications have been conducted in an attempt to summarize the
amount of information produced in its subfields. Recently, Unmanned Aerial
Vehicles (UAV) based applications have dominated aerial sensing research.
However, a literature revision that combines both "deep learning" and "UAV
remote sensing" thematics has not yet been conducted. The motivation for our
work was to present a comprehensive review of the fundamentals of Deep Learning
(DL) applied in UAV-based imagery. We focused mainly on describing
classification and regression techniques used in recent applications with
UAV-acquired data. For that, a total of 232 papers published in international
scientific journal databases was examined. We gathered the published material
and evaluated their characteristics regarding application, sensor, and
technique used. We relate how DL presents promising results and has the
potential for processing tasks associated with UAV-based image data. Lastly, we
project future perspectives, commentating on prominent DL paths to be explored
in the UAV remote sensing field. Our revision consists of a friendly-approach
to introduce, commentate, and summarize the state-of-the-art in UAV-based image
applications with DNNs algorithms in diverse subfields of remote sensing,
grouping it in the environmental, urban, and agricultural contexts.Comment: 38 pages, 10 figure
Counting and Locating High-Density Objects Using Convolutional Neural Network
This paper presents a Convolutional Neural Network (CNN) approach for
counting and locating objects in high-density imagery. To the best of our
knowledge, this is the first object counting and locating method based on a
feature map enhancement and a Multi-Stage Refinement of the confidence map. The
proposed method was evaluated in two counting datasets: tree and car. For the
tree dataset, our method returned a mean absolute error (MAE) of 2.05, a
root-mean-squared error (RMSE) of 2.87 and a coefficient of determination
(R) of 0.986. For the car dataset (CARPK and PUCPR+), our method was
superior to state-of-the-art methods. In the these datasets, our approach
achieved an MAE of 4.45 and 3.16, an RMSE of 6.18 and 4.39, and an R of
0.975 and 0.999, respectively. The proposed method is suitable for dealing with
high object-density, returning a state-of-the-art performance for counting and
locating objects.Comment: 15 pages, 10 figures, 8 table
Semantic Segmentation with Labeling Uncertainty and Class Imbalance
Recently, methods based on Convolutional Neural Networks (CNN) achieved
impressive success in semantic segmentation tasks. However, challenges such as
the class imbalance and the uncertainty in the pixel-labeling process are not
completely addressed. As such, we present a new approach that calculates a
weight for each pixel considering its class and uncertainty during the labeling
process. The pixel-wise weights are used during training to increase or
decrease the importance of the pixels. Experimental results show that the
proposed approach leads to significant improvements in three challenging
segmentation tasks in comparison to baseline methods. It was also proved to be
more invariant to noise. The approach presented here may be used within a wide
range of semantic segmentation methods to improve their robustness.Comment: 15 pages, 9 figures, 3 table
Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images
rban forests contribute to maintaining livability and increase the resilience of cities in the face of population growth and climate change. Information about the geographical distribution of individual trees is essential for the proper management of these systems. RGB high-resolution aerial images have emerged as a cheap and efficient source of data, although detecting and mapping single trees in an urban environment is a challenging task. Thus, we propose the evaluation of novel methods for single tree crown detection, as most of these methods have not been investigated in remote sensing applications. A total of 21 methods were investigated, including anchor-based (one and two-stage) and anchor-free state-of-the-art deep-learning methods. We used two orthoimages divided into 220 non-overlapping patches of 512 Ă 512 pixels with a ground sample distance (GSD) of 10 cm. The orthoimages were manually annotated, and 3382 single tree crowns were identified as the ground-truth. Our findings show that the anchor-free detectors achieved the best average performance with an AP50 of 0.686. We observed that the two-stage anchor-based and anchor-free methods showed better performance for this task, emphasizing the FSAF, Double Heads, CARAFE, ATSS, and FoveaBox models. RetinaNet, which is currently commonly applied in remote sensing, did not show satisfactory performance, and Faster R-CNN had lower results than the best methods but with no statistically significant difference. Our findings contribute to a better understanding of the performance of novel deep-learning methods in remote sensing applications and could be used as an indicator of the most suitable methods in such applications
Silicate fertilization in semi-hydroponic strawberry cultivation
Strawberry-producing technologies are rapidly developing for the cultivation of small red fruits. The Southern Minas Gerais
stands out in Brazilâs production. In this context, the search for production improvements via nutrition and quality maintenance
is indispensable for cultivation. This study aimed to evaluate how different silicon doses can influence the production and
quality of strawberry fruits from national and imported seedlings. For the experiment, strawberry seedlings from the âSan
Andreasâ cultivar, of national and Chilean origins, were used. The plants were grown in a semi-hydroponic system and
corresponding silicon doses (0, 500, 1000, 1500 and 3000 mg Lâ1) were tested via foliar application. A split-plot design was
used, in a 5Ă2 factorial scheme, with four replications and 10 plants per plot. By physical and physicochemical evaluations,
alterations in the production and quality of the fruits were analyzed. The foliar application of silicon â to complement the
strawberry nutrition â did not increase the productivity or quality of the fruits, regardless of the nationality of the seedlings.
The only difference regarding origin was associated the vigor of plants
Long-term persistence of supernumerary B chromosomes in multiple species of Astyanax fish
Background Eukaryote genomes frequently harbor supernumerary B chromosomes in addition to the âstandardâ A chromosome set. B chromosomes are thought to arise as byproducts of genome rearrangements and have mostly been considered intraspecific oddities. However, their evolutionary transcendence beyond species level has remained untested. Results Here we reveal that the large metacentric B chromosomes reported in several fish species of the genus Astyanax arose in a common ancestor at least 4 million years ago. We generated transcriptomes of A. scabripinnis and A. paranae 0B and 1B individuals and used these assemblies as a reference for mapping all gDNA and RNA libraries to quantify coverage differences between B-lacking and B-carrying genomes. We show that the B chromosomes of A. scabripinnis and A. paranae share 19 protein-coding genes, of which 14 and 11 were also present in the B chromosomes of A. bockmanni and A. fasciatus, respectively. Our search for B-specific single-nucleotide polymorphisms (SNPs) identified the presence of B-derived transcripts in B-carrying ovaries, 80% of which belonged to nobox, a gene involved in oogenesis regulation. Importantly, the B chromosome nobox paralog is expressed >â30Ă more than the A chromosome paralog. This indicates that the normal regulation of this gene is altered in B-carrying females, which could potentially facilitate B inheritance at higher rates than Mendelian law prediction. Conclusions Taken together, our results demonstrate the long-term survival of B chromosomes despite their lack of regular pairing and segregation during meiosis and that they can endure episodes of population divergence leading to species formation
The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment
The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in
operation since July 2014. This paper describes the second data release from
this phase, and the fourteenth from SDSS overall (making this, Data Release
Fourteen or DR14). This release makes public data taken by SDSS-IV in its first
two years of operation (July 2014-2016). Like all previous SDSS releases, DR14
is cumulative, including the most recent reductions and calibrations of all
data taken by SDSS since the first phase began operations in 2000. New in DR14
is the first public release of data from the extended Baryon Oscillation
Spectroscopic Survey (eBOSS); the first data from the second phase of the
Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2),
including stellar parameter estimates from an innovative data driven machine
learning algorithm known as "The Cannon"; and almost twice as many data cubes
from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous
release (N = 2812 in total). This paper describes the location and format of
the publicly available data from SDSS-IV surveys. We provide references to the
important technical papers describing how these data have been taken (both
targeting and observation details) and processed for scientific use. The SDSS
website (www.sdss.org) has been updated for this release, and provides links to
data downloads, as well as tutorials and examples of data use. SDSS-IV is
planning to continue to collect astronomical data until 2020, and will be
followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14
happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov
2017 (this is the "post-print" and "post-proofs" version; minor corrections
only from v1, and most of errors found in proofs corrected
Sloan Digital Sky Survey IV: mapping the Milky Way, nearby galaxies, and the distant universe
We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median ). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July
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