382 research outputs found
Pansharpening techniques to detect mass monument damaging in Iraq
The recent mass destructions of monuments in Iraq cannot be monitored with the terrestrial survey methodologies, for obvious reasons
of safety. For the same reasons, it’s not advisable the use of classical aerial photogrammetry, so it was obvious to think to the use of
multispectral Very High Resolution (VHR) satellite imagery. Nowadays VHR satellite images resolutions are very near airborne
photogrammetrical images and usually they are acquired in multispectral mode. The combination of the various bands of the images
is called pan-sharpening and it can be carried on using different algorithms and strategies. The correct pansharpening methodology,
for a specific image, must be chosen considering the specific multispectral characteristics of the satellite used and the particular
application. In this paper a first definition of guidelines for the use of VHR multispectral imagery to detect monument destruction in
unsafe area, is reported.
The proposed methodology, agreed with UNESCO and soon to be used in Libya for the coastal area, has produced a first report
delivered to the Iraqi authorities. Some of the most evident examples are reported to show the possible capabilities of identification of
damages using VHR images
Target-adaptive CNN-based pansharpening
We recently proposed a convolutional neural network (CNN) for remote sensing
image pansharpening obtaining a significant performance gain over the state of
the art. In this paper, we explore a number of architectural and training
variations to this baseline, achieving further performance gains with a
lightweight network which trains very fast. Leveraging on this latter property,
we propose a target-adaptive usage modality which ensures a very good
performance also in the presence of a mismatch w.r.t. the training set, and
even across different sensors. The proposed method, published online as an
off-the-shelf software tool, allows users to perform fast and high-quality
CNN-based pansharpening of their own target images on general-purpose hardware
Hyperspectral Super-Resolution with Coupled Tucker Approximation: Recoverability and SVD-based algorithms
We propose a novel approach for hyperspectral super-resolution, that is based
on low-rank tensor approximation for a coupled low-rank multilinear (Tucker)
model. We show that the correct recovery holds for a wide range of multilinear
ranks. For coupled tensor approximation, we propose two SVD-based algorithms
that are simple and fast, but with a performance comparable to the
state-of-the-art methods. The approach is applicable to the case of unknown
spatial degradation and to the pansharpening problem.Comment: IEEE Transactions on Signal Processing, Institute of Electrical and
Electronics Engineers, in Pres
A convex formulation for hyperspectral image superresolution via subspace-based regularization
Hyperspectral remote sensing images (HSIs) usually have high spectral
resolution and low spatial resolution. Conversely, multispectral images (MSIs)
usually have low spectral and high spatial resolutions. The problem of
inferring images which combine the high spectral and high spatial resolutions
of HSIs and MSIs, respectively, is a data fusion problem that has been the
focus of recent active research due to the increasing availability of HSIs and
MSIs retrieved from the same geographical area.
We formulate this problem as the minimization of a convex objective function
containing two quadratic data-fitting terms and an edge-preserving regularizer.
The data-fitting terms account for blur, different resolutions, and additive
noise. The regularizer, a form of vector Total Variation, promotes
piecewise-smooth solutions with discontinuities aligned across the
hyperspectral bands.
The downsampling operator accounting for the different spatial resolutions,
the non-quadratic and non-smooth nature of the regularizer, and the very large
size of the HSI to be estimated lead to a hard optimization problem. We deal
with these difficulties by exploiting the fact that HSIs generally "live" in a
low-dimensional subspace and by tailoring the Split Augmented Lagrangian
Shrinkage Algorithm (SALSA), which is an instance of the Alternating Direction
Method of Multipliers (ADMM), to this optimization problem, by means of a
convenient variable splitting. The spatial blur and the spectral linear
operators linked, respectively, with the HSI and MSI acquisition processes are
also estimated, and we obtain an effective algorithm that outperforms the
state-of-the-art, as illustrated in a series of experiments with simulated and
real-life data.Comment: IEEE Trans. Geosci. Remote Sens., to be publishe
Fusion of MultiSpectral and Panchromatic Images Based on Morphological Operators
International audienceNonlinear decomposition schemes constitute an alternative to classical approaches for facing the problem of data fusion. In this paper we discuss the application of this methodology to a popular remote sensing application called pansharpening, which consists in the fusion of a low resolution multispectral image and a high resolution panchromatic image. We design a complete pansharpening scheme based on the use of morphological half gradients operators and demonstrate the suitability of this algorithm through the comparison with state of the art approaches. Four datasets acquired by the Pleiades, Worldview-2, Ikonos and Geoeye-1 satellites are employed for the performance assessment, testifying the effectiveness of the proposed approach in producing top-class images with a setting independent of the specific sensor
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