10,417 research outputs found
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
Detection and segmentation of objects in overheard imagery is a challenging
task. The variable density, random orientation, small size, and
instance-to-instance heterogeneity of objects in overhead imagery calls for
approaches distinct from existing models designed for natural scene datasets.
Though new overhead imagery datasets are being developed, they almost
universally comprise a single view taken from directly overhead ("at nadir"),
failing to address a critical variable: look angle. By contrast, views vary in
real-world overhead imagery, particularly in dynamic scenarios such as natural
disasters where first looks are often over 40 degrees off-nadir. This
represents an important challenge to computer vision methods, as changing view
angle adds distortions, alters resolution, and changes lighting. At present,
the impact of these perturbations for algorithmic detection and segmentation of
objects is untested. To address this problem, we present an open source
Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks
from a broad range of viewing angles (-32.5 degrees to 54.0 degrees). Each of
these images cover the same 665 square km geographic extent and are annotated
with 126,747 building footprint labels, enabling direct assessment of the
impact of viewpoint perturbation on model performance. We benchmark multiple
leading segmentation and object detection models on: (1) building detection,
(2) generalization to unseen viewing angles and resolutions, and (3)
sensitivity of building footprint extraction to changes in resolution. We find
that state of the art segmentation and object detection models struggle to
identify buildings in off-nadir imagery and generalize poorly to unseen views,
presenting an important benchmark to explore the broadly relevant challenge of
detecting small, heterogeneous target objects in visually dynamic contexts.Comment: Accepted into IEEE International Conference on Computer Vision (ICCV)
201
Multi-label learning based semi-global matching forest
Semi-Global Matching (SGM) approximates a 2D Markov Random Field (MRF) via multiple 1D scanline optimizations, which serves as a good trade-off between accuracy and efficiency in dense matching. Nevertheless, the performance is limited due to the simple summation of the aggregated costs from all 1D scanline optimizations for the final disparity estimation. SGM-Forest improves the performance of SGM by training a random forest to predict the best scanline according to each scanline’s disparity proposal. The disparity estimated by the best scanline acts as reference to adaptively adopt close proposals for further post-processing. However, in many cases more than one scanline is capable of providing a good prediction. Training the random forest with only one scanline labeled may limit or even confuse the learning procedure when other scanlines can offer similar contributions. In this paper, we propose a multi-label classification strategy to further improve SGM-Forest. Each training sample is allowed to be described by multiple labels (or zero label) if more than one (or none) scanline gives a proper prediction. We test the proposed method on stereo matching datasets, from Middlebury, ETH3D, EuroSDR image matching benchmark, and the 2019 IEEE GRSS data fusion contest. The result indicates that under the framework of SGM-Forest, the multi-label strategy outperforms the single-label scheme consistently
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Methods to Retrieve the Cloud-Top Height in the Frame of the JEM-EUSO Mission
The Japanese Experiment Module-Extreme Universe
Space Observatory (JEM-EUSO) telescope will measure
ultrahigh-energy cosmic ray properties by detecting the UV
fluorescence light generated in the interaction between cosmic
rays and the atmosphere. Therefore, information on the state of
clouds in the atmosphere is crucial for a proper interpretation of
the data. For a real-time observation of the clouds in the telescope
field of view, the JEM-EUSO will use an atmospheric monitoring
system composed of a light detection and ranging and an infrared
(IR) camera. In this paper, the focus is on the IR camera data.
To retrieve the cloud-top height (CTH) from IR images, three
different methods are considered here. The first one is based
on bispectral stereo vision algorithms and requires two different
views of the same scene in different spectral bands. For the second
one, brightness temperatures provided by the IR camera are converted
to effective cloud-top temperatures, from which the CTH is
estimated using the vertical temperature profiles. A third method
that uses the primary numerical weather prediction model output
parameters, such as the cloud fraction, has also been considered
to retrieve the CTH. This paper presents a first analysis, in which
the heights retrieved by these three methodologies are compared
with the heights given by the Moderate Resolution Imaging
Spectroradiometer sensor installed on the polar satellite Terra.
Since all these methods are suitable for the JEM-EUSO mission,
they could be used in the future in a complementary way to
improve the accuracy of the CTH retrieval
Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from Spaceborne CCD Imagery
In this study, we present a large-scale earth surface reconstruction pipeline
for linear-array charge-coupled device (CCD) satellite imagery. While
mainstream satellite image-based reconstruction approaches perform
exceptionally well, the rational functional model (RFM) is subject to several
limitations. For example, the RFM has no rigorous physical interpretation and
differs significantly from the pinhole imaging model; hence, it cannot be
directly applied to learning-based 3D reconstruction networks and to more novel
reconstruction pipelines in computer vision. Hence, in this study, we introduce
a method in which the RFM is equivalent to the pinhole camera model (PCM),
meaning that the internal and external parameters of the pinhole camera are
used instead of the rational polynomial coefficient parameters. We then derive
an error formula for this equivalent pinhole model for the first time,
demonstrating the influence of the image size on the accuracy of the
reconstruction. In addition, we propose a polynomial image refinement model
that minimizes equivalent errors via the least squares method. The experiments
were conducted using four image datasets: WHU-TLC, DFC2019, ISPRS-ZY3, and GF7.
The results demonstrated that the reconstruction accuracy was proportional to
the image size. Our polynomial image refinement model significantly enhanced
the accuracy and completeness of the reconstruction, and achieved more
significant improvements for larger-scale images.Comment: 24 page
The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery
peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1
The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery
R. O’Haraemail
, S. Green
and T. McCarthy
DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019
PDF
Abstract
Article
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References
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Abstract
The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales
State of the art 3D technologies and MVV end to end system design
L’oggetto del presente lavoro di tesi è costituito dall’analisi e dalla recensione di tutte le tecnologie 3D: esistenti e in via di sviluppo per ambienti domestici; tenendo come punto di riferimento le tecnologie multiview video (MVV). Tutte le sezioni della catena dalla fase di cattura a quella di riproduzione sono analizzate. Lo scopo è di progettare una possibile architettura satellitare per un futuro sistema MVV televisivo, nell’ambito di due possibili scenari, broadcast o interattivo. L’analisi coprirà considerazioni tecniche, ma anche limitazioni commerciali
Recent Advances in Image Restoration with Applications to Real World Problems
In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included
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