11,792 research outputs found
Multisource and Multitemporal Data Fusion in Remote Sensing
The sharp and recent increase in the availability of data captured by
different sensors combined with their considerably heterogeneous natures poses
a serious challenge for the effective and efficient processing of remotely
sensed data. Such an increase in remote sensing and ancillary datasets,
however, opens up the possibility of utilizing multimodal datasets in a joint
manner to further improve the performance of the processing approaches with
respect to the application at hand. Multisource data fusion has, therefore,
received enormous attention from researchers worldwide for a wide variety of
applications. Moreover, thanks to the revisit capability of several spaceborne
sensors, the integration of the temporal information with the spatial and/or
spectral/backscattering information of the remotely sensed data is possible and
helps to move from a representation of 2D/3D data to 4D data structures, where
the time variable adds new information as well as challenges for the
information extraction algorithms. There are a huge number of research works
dedicated to multisource and multitemporal data fusion, but the methods for the
fusion of different modalities have expanded in different paths according to
each research community. This paper brings together the advances of multisource
and multitemporal data fusion approaches with respect to different research
communities and provides a thorough and discipline-specific starting point for
researchers at different levels (i.e., students, researchers, and senior
researchers) willing to conduct novel investigations on this challenging topic
by supplying sufficient detail and references
HyperSpectral classification with adaptively weighted L1-norm regularization and spatial postprocessing
Sparse regression methods have been proven effective in a wide range of
signal processing problems such as image compression, speech coding, channel
equalization, linear regression and classification. In this paper a new convex
method of hyperspectral image classification is developed based on the sparse
unmixing algorithm SUnSAL for which a pixel adaptive L1-norm regularization
term is introduced. To further enhance class separability, the algorithm is
kernelized using an RBF kernel and the final results are improved by a
combination of spatial pre and post-processing operations. It is shown that the
proposed method is competitive with state of the art algorithms such as SVM-CK,
KSOMP-CK and KSSP-CK.Comment: v3: 11 pages, 2 Figures, 10 Tables. Updated the results for the
Indian Pines image; added the results for the Pavia University imag
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
Hyperspectral images (HSIs) are often corrupted by a mixture of several types
of noise during the acquisition process, e.g., Gaussian noise, impulse noise,
dead lines, stripes, and many others. Such complex noise could degrade the
quality of the acquired HSIs, limiting the precision of the subsequent
processing. In this paper, we present a novel tensor-based HSI restoration
approach by fully identifying the intrinsic structures of the clean HSI part
and the mixed noise part respectively. Specifically, for the clean HSI part, we
use tensor Tucker decomposition to describe the global correlation among all
bands, and an anisotropic spatial-spectral total variation (SSTV)
regularization to characterize the piecewise smooth structure in both spatial
and spectral domains. For the mixed noise part, we adopt the norm
regularization to detect the sparse noise, including stripes, impulse noise,
and dead pixels. Despite that TV regulariztion has the ability of removing
Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian
noise for some real-world scenarios. Then, we develop an efficient algorithm
for solving the resulting optimization problem by using the augmented Lagrange
multiplier (ALM) method. Finally, extensive experiments on simulated and
real-world noise HSIs are carried out to demonstrate the superiority of the
proposed method over the existing state-of-the-art ones.Comment: 15 pages, 20 figure
Kronecker PCA Based Robust SAR STAP
In this work the detection of moving targets in multiantenna SAR is
considered. As a high resolution radar imaging modality, SAR detects and
identifies stationary targets very well, giving it an advantage over classical
GMTI radars. Moving target detection is more challenging due to the "burying"
of moving targets in the clutter and is often achieved using space-time
adaptive processing (STAP) (based on learning filters from the spatio-temporal
clutter covariance) to remove the stationary clutter and enhance the moving
targets. In this work, it is noted that in addition to the oft noted low rank
structure, the clutter covariance is also naturally in the form of a space vs
time Kronecker product with low rank factors. A low-rank KronPCA covariance
estimation algorithm is proposed to exploit this structure, and a separable
clutter cancelation filter based on the Kronecker covariance estimate is
proposed. Together, these provide orders of magnitude reduction in the number
of training samples required, as well as improved robustness to corruption of
the training data, e.g. due to outliers and moving targets. Theoretical
properties of the proposed estimation algorithm are derived and the significant
reductions in training complexity are established under the spherically
invariant random vector model (SIRV). Finally, an extension of this approach
incorporating multipass data (change detection) is presented. Simulation
results and experiments using the real Gotcha SAR GMTI challenge dataset are
presented that confirm the advantages of our approach relative to existing
techniques.Comment: Tech report. Shorter version submitted to IEEE AE
Dictionary Learning for Adaptive GPR Landmine Classification
Ground penetrating radar (GPR) target detection and classification is a
challenging task. Here, we consider online dictionary learning (DL) methods to
obtain sparse representations (SR) of the GPR data to enhance feature
extraction for target classification via support vector machines. Online
methods are preferred because traditional batch DL like K-SVD is not scalable
to high-dimensional training sets and infeasible for real-time operation. We
also develop Drop-Off MINi-batch Online Dictionary Learning (DOMINODL) which
exploits the fact that a lot of the training data may be correlated. The
DOMINODL algorithm iteratively considers elements of the training set in small
batches and drops off samples which become less relevant. For the case of
abandoned anti-personnel landmines classification, we compare the performance
of K-SVD with three online algorithms: classical Online Dictionary Learning,
its correlation-based variant, and DOMINODL. Our experiments with real data
from L-band GPR show that online DL methods reduce learning time by 36-93% and
increase mine detection by 4-28% over K-SVD. Our DOMINODL is the fastest and
retains similar classification performance as the other two online DL
approaches. We use a Kolmogorov-Smirnoff test distance and the
Dvoretzky-Kiefer-Wolfowitz inequality for the selection of DL input parameters
leading to enhanced classification results. To further compare with
state-of-the-art classification approaches, we evaluate a convolutional neural
network (CNN) classifier which performs worse than the proposed approach.
Moreover, when the acquired samples are randomly reduced by 25%, 50% and 75%,
sparse decomposition based classification with DL remains robust while the CNN
accuracy is drastically compromised.Comment: 16 pages, 11 figures, 10 table
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
A Survey on Object Detection in Optical Remote Sensing Images
Object detection in optical remote sensing images, being a fundamental but
challenging problem in the field of aerial and satellite image analysis, plays
an important role for a wide range of applications and is receiving significant
attention in recent years. While enormous methods exist, a deep review of the
literature concerning generic object detection is still lacking. This paper
aims to provide a review of the recent progress in this field. Different from
several previously published surveys that focus on a specific object class such
as building and road, we concentrate on more generic object categories
including, but are not limited to, road, building, tree, vehicle, ship,
airport, urban-area. Covering about 270 publications we survey 1) template
matching-based object detection methods, 2) knowledge-based object detection
methods, 3) object-based image analysis (OBIA)-based object detection methods,
4) machine learning-based object detection methods, and 5) five publicly
available datasets and three standard evaluation metrics. We also discuss the
challenges of current studies and propose two promising research directions,
namely deep learning-based feature representation and weakly supervised
learning-based geospatial object detection. It is our hope that this survey
will be beneficial for the researchers to have better understanding of this
research field.Comment: This manuscript is the accepted version for ISPRS Journal of
Photogrammetry and Remote Sensin
Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, Applications, and Prospects
The past few decades have witnessed the great progress of unmanned aircraft
vehicles (UAVs) in civilian fields, especially in photogrammetry and remote
sensing. In contrast with the platforms of manned aircraft and satellite, the
UAV platform holds many promising characteristics: flexibility, efficiency,
high-spatial/temporal resolution, low cost, easy operation, etc., which make it
an effective complement to other remote-sensing platforms and a cost-effective
means for remote sensing. Considering the popularity and expansion of UAV-based
remote sensing in recent years, this paper provides a systematic survey on the
recent advances and future prospectives of UAVs in the remote-sensing
community. Specifically, the main challenges and key technologies of
remote-sensing data processing based on UAVs are discussed and summarized
firstly. Then, we provide an overview of the widespread applications of UAVs in
remote sensing. Finally, some prospects for future work are discussed. We hope
this paper will provide remote-sensing researchers an overall picture of recent
UAV-based remote sensing developments and help guide the further research on
this topic
Volumetric Super-Resolution of Multispectral Data
Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7
ETM+) provide low-spatial high-spectral resolution multispectral (MS) or
high-spatial low-spectral resolution panchromatic (PAN) images, separately. In
order to reconstruct a high-spatial/high-spectral resolution multispectral
image volume, either the information in MS and PAN images are fused (i.e.
pansharpening) or super-resolution reconstruction (SRR) is used with only MS
images captured on different dates. Existing methods do not utilize temporal
information of MS and high spatial resolution of PAN images together to improve
the resolution. In this paper, we propose a multiframe SRR algorithm using
pansharpened MS images, taking advantage of both temporal and spatial
information available in multispectral imagery, in order to exceed spatial
resolution of given PAN images. We first apply pansharpening to a set of
multispectral images and their corresponding PAN images captured on different
dates. Then, we use the pansharpened multispectral images as input to the
proposed wavelet-based multiframe SRR method to yield full volumetric SRR. The
proposed SRR method is obtained by deriving the subband relations between
multitemporal MS volumes. We demonstrate the results on Landsat 7 ETM+ images
comparing our method to conventional techniques.Comment: arXiv admin note: text overlap with arXiv:1705.0125
Robust SAR STAP via Kronecker Decomposition
This paper proposes a spatio-temporal decomposition for the detection of
moving targets in multiantenna SAR. As a high resolution radar imaging
modality, SAR detects and localizes non-moving targets accurately, giving it an
advantage over lower resolution GMTI radars. Moving target detection is more
challenging due to target smearing and masking by clutter. Space-time adaptive
processing (STAP) is often used to remove the stationary clutter and enhance
the moving targets. In this work, it is shown that the performance of STAP can
be improved by modeling the clutter covariance as a space vs. time Kronecker
product with low rank factors. Based on this model, a low-rank Kronecker
product covariance estimation algorithm is proposed, and a novel separable
clutter cancelation filter based on the Kronecker covariance estimate is
introduced. The proposed method provides orders of magnitude reduction in the
required number of training samples, as well as improved robustness to
corruption of the training data. Simulation results and experiments using the
Gotcha SAR GMTI challenge dataset are presented that confirm the advantages of
our approach relative to existing techniques.Comment: to appear at IEEE AES. arXiv admin note: text overlap with
arXiv:1604.03622, arXiv:1501.0748
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