2,155 research outputs found
Image Pre-processing Algorithms for Detection of Small/Point Airborne Targets
The problem of detecting small/point targets in infra-red imagery is an important research area for defence applications. The challenge is to achieve high sensitivity for detection of dim point like small targets with low false alarms and high detection probability. To detect the target in such scenario, pre-processing algorithms are used to predict the complex background and then to subtract predicted background from the original image. The difference image is passed to the detection algorithm to further distinguish between target and background and/or noise. The aim of the study is to fit the background as closely as possible in the original image without diminishing the target signal. A number of pre-processing algorithms (spatial, temporal and spatio-temporal) have been reported in the literature. In this paper a survey of different pre-processing algorithm is presented. An improved hybrid morphological filter, which provides high gain in signal-to-noise plus clutter ratio (SCNR), has been proposed for detection of small/point targets.Defence Science Journal, 2009, 59(2), pp.166-174, DOI:http://dx.doi.org/10.14429/dsj.59.150
Flying Target Detection and Recognition by Feature Fusion
This paper presents a near-realtime visual detection and
recognition approach for flying target detection and recognition. Detection is based on fast and robust background modeling and shape extraction, while recognition of target classes is based on shape and texture fused querying on a-priori built real datasets. Main application areas are
passive defense and surveillance scenarios
Neptune at Summer Solstice: Zonal Mean Temperatures from Ground-Based Observations 2003-2007
Imaging and spectroscopy of Neptune's thermal infrared emission is used to
assess seasonal changes in Neptune's zonal mean temperatures between Voyager-2
observations (1989, heliocentric longitude Ls=236) and southern summer solstice
(2005, Ls=270). Our aim was to analyse imaging and spectroscopy from multiple
different sources using a single self-consistent radiative-transfer model to
assess the magnitude of seasonal variability. Globally-averaged stratospheric
temperatures measured from methane emission tend towards a quasi-isothermal
structure (158-164 K) above the 0.1-mbar level, and are found to be consistent
with spacecraft observations of AKARI. This remarkable consistency, despite
very different observing conditions, suggests that stratospheric temporal
variability, if present, is 5 K at 1 mbar and 3 K at 0.1 mbar during
this solstice period. Conversely, ethane emission is highly variable, with
abundance determinations varying by more than a factor of two. The retrieved
C2H6 abundances are extremely sensitive to the details of the T(p) derivation.
Stratospheric temperatures and ethane are found to be latitudinally uniform
away from the south pole (assuming a latitudinally-uniform distribution of
stratospheric methane). At low and midlatitudes, comparisons of synthetic
Voyager-era images with solstice-era observations suggest that tropospheric
zonal temperatures are unchanged since the Voyager 2 encounter, with cool
mid-latitudes and a warm equator and pole. A re-analysis of Voyager/IRIS 25-50
{\mu}m mapping of tropospheric temperatures and para-hydrogen disequilibrium
suggests a symmetric meridional circulation with cold air rising at
mid-latitudes (sub-equilibrium para-H2 conditions) and warm air sinking at the
equator and poles (super-equilibrium para-H2 conditions). The most significant
atmospheric changes are associated with the polar vortex (absent in 1989).Comment: 35 pages, 19 figures. Accepted for publication in Icaru
LBT observations of the HR 8799 planetary system: First detection of HR8799e in H band
We have performed H and Ks band observations of the planetary system around
HR 8799 using the new AO system at the Large Binocular Telescope and the PISCES
Camera. The excellent instrument performance (Strehl ratios up to 80% in H
band) enabled detection the inner planet HR8799e in the H band for the first
time. The H and Ks magnitudes of HR8799e are similar to those of planets c and
d, with planet e slightly brighter. Therefore, HR8799e is likely slightly more
massive than c and d. We also explored possible orbital configurations and
their orbital stability. We confirm that the orbits of planets b, c and e are
consistent with being circular and coplanar; planet d should have either an
orbital eccentricity of about 0.1 or be non-coplanar with respect to b and c.
Planet e can not be in circular and coplanar orbit in a 4:2:1 mean motion
resonances with c and d, while coplanar and circular orbits are allowed for a
5:2 resonance. The analysis of dynamical stability shows that the system is
highly unstable or chaotic when planetary masses of about 5 MJup for b and 7
MJup for the other planets are adopted. Significant regions of dynamical
stability for timescales of tens of Myr are found when adopting planetary
masses of about 3.5, 5, 5, and 5 Mjup for HR 8799 b, c, d, and e respectively.
These masses are below the current estimates based on the stellar age (30 Myr)
and theoretical models of substellar objects.Comment: 13 pages, 10 figures, A&A, accepte
Three Small Planets Transiting a Hyades Star
We present the discovery of three small planets transiting K2-136 (LP 358
348, EPIC 247589423), a late K dwarf in the Hyades. The planets have orbital
periods of , , and
days, and radii of , , and , respectively. With an age of
600-800 Myr, these planets are some of the smallest and youngest transiting
planets known. Due to the relatively bright (J=9.1) host star, the planets are
compelling targets for future characterization via radial velocity mass
measurements and transmission spectroscopy. As the first known star with
multiple transiting planets in a cluster, the system should be helpful for
testing theories of planet formation and migration.Comment: Accepted to The Astronomical Journa
Texture Image Segmentation using Morphology in Wavelet Transforms
One of the essential and crucial steps for image understanding, interpretation, analysis and recognition is the image segmentation. This paper advocates a new segme- ntation scheme using morphology on wavelet decomposed images. The present paper provides a good segmentation on natural images and textures by dividing an image into non overlapping regions, which are homogenous in terms of certain features such as texture, spatial coordinates etc. using simple morphological operations. Morphological enhancement technique based on Top Hat transforms enhances the local contrast in this paper. The morphological treatment and followed by Otsu2019;s threshold overcomes the problem of noise and thin gaps, and also smooth the final regions. The experimental results on four different databases demonstrate the success of the proposed method, compared to many other methods
Unlocking the capabilities of explainable fewshot learning in remote sensing
Recent advancements have significantly improved the efficiency and
effectiveness of deep learning methods for imagebased remote sensing tasks.
However, the requirement for large amounts of labeled data can limit the
applicability of deep neural networks to existing remote sensing datasets. To
overcome this challenge, fewshot learning has emerged as a valuable approach
for enabling learning with limited data. While previous research has evaluated
the effectiveness of fewshot learning methods on satellite based datasets,
little attention has been paid to exploring the applications of these methods
to datasets obtained from UAVs, which are increasingly used in remote sensing
studies. In this review, we provide an up to date overview of both existing and
newly proposed fewshot classification techniques, along with appropriate
datasets that are used for both satellite based and UAV based data. Our
systematic approach demonstrates that fewshot learning can effectively adapt to
the broader and more diverse perspectives that UAVbased platforms can provide.
We also evaluate some SOTA fewshot approaches on a UAV disaster scene
classification dataset, yielding promising results. We emphasize the importance
of integrating XAI techniques like attention maps and prototype analysis to
increase the transparency, accountability, and trustworthiness of fewshot
models for remote sensing. Key challenges and future research directions are
identified, including tailored fewshot methods for UAVs, extending to unseen
tasks like segmentation, and developing optimized XAI techniques suited for
fewshot remote sensing problems. This review aims to provide researchers and
practitioners with an improved understanding of fewshot learnings capabilities
and limitations in remote sensing, while highlighting open problems to guide
future progress in efficient, reliable, and interpretable fewshot methods.Comment: Under review, once the paper is accepted, the copyright will be
transferred to the corresponding journa
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