48 research outputs found
Spatial-Spectral Manifold Embedding of Hyperspectral Data
In recent years, hyperspectral imaging, also known as imaging spectroscopy,
has been paid an increasing interest in geoscience and remote sensing
community. Hyperspectral imagery is characterized by very rich spectral
information, which enables us to recognize the materials of interest lying on
the surface of the Earth more easier. We have to admit, however, that high
spectral dimension inevitably brings some drawbacks, such as expensive data
storage and transmission, information redundancy, etc. Therefore, to reduce the
spectral dimensionality effectively and learn more discriminative spectral
low-dimensional embedding, in this paper we propose a novel hyperspectral
embedding approach by simultaneously considering spatial and spectral
information, called spatial-spectral manifold embedding (SSME). Beyond the
pixel-wise spectral embedding approaches, SSME models the spatial and spectral
information jointly in a patch-based fashion. SSME not only learns the spectral
embedding by using the adjacency matrix obtained by similarity measurement
between spectral signatures, but also models the spatial neighbours of a target
pixel in hyperspectral scene by sharing the same weights (or edges) in the
process of learning embedding. Classification is explored as a potential
strategy to quantitatively evaluate the performance of learned embedding
representations. Classification is explored as a potential application for
quantitatively evaluating the performance of these hyperspectral embedding
algorithms. Extensive experiments conducted on the widely-used hyperspectral
datasets demonstrate the superiority and effectiveness of the proposed SSME as
compared to several state-of-the-art embedding methods
Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification
Despite the fact that nonlinear subspace learning techniques (e.g. manifold
learning) have successfully applied to data representation, there is still room
for improvement in explainability (explicit mapping), generalization
(out-of-samples), and cost-effectiveness (linearization). To this end, a novel
linearized subspace learning technique is developed in a joint and progressive
way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning
str\textbf{a}teg\textbf{y} (J-Play), with its application to multi-label
classification. The J-Play learns high-level and semantically meaningful
feature representation from high-dimensional data by 1) jointly performing
multiple subspace learning and classification to find a latent subspace where
samples are expected to be better classified; 2) progressively learning
multi-coupled projections to linearly approach the optimal mapping bridging the
original space with the most discriminative subspace; 3) locally embedding
manifold structure in each learnable latent subspace. Extensive experiments are
performed to demonstrate the superiority and effectiveness of the proposed
method in comparison with previous state-of-the-art methods.Comment: accepted in ECCV 201
Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection
Geospatial object detection of remote sensing imagery has been attracting an
increasing interest in recent years, due to the rapid development in spaceborne
imaging. Most of previously proposed object detectors are very sensitive to
object deformations, such as scaling and rotation. To this end, we propose a
novel and efficient framework for geospatial object detection in this letter,
called Fourier-based rotation-invariant feature boosting (FRIFB). A
Fourier-based rotation-invariant feature is first generated in polar
coordinate. Then, the extracted features can be further structurally refined
using aggregate channel features. This leads to a faster feature computation
and more robust feature representation, which is good fitting for the coming
boosting learning. Finally, in the test phase, we achieve a fast pyramid
feature extraction by estimating a scale factor instead of directly collecting
all features from image pyramid. Extensive experiments are conducted on two
subsets of NWPU VHR-10 dataset, demonstrating the superiority and effectiveness
of the FRIFB compared to previous state-of-the-art methods
An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements
Spectral imaging has recently gained traction for face recognition in
biometric systems. We investigate the merits of spectral imaging for face
recognition and the current challenges that hamper the widespread deployment of
spectral sensors for face recognition. The reliability of conventional face
recognition systems operating in the visible range is compromised by
illumination changes, pose variations and spoof attacks. Recent works have
reaped the benefits of spectral imaging to counter these limitations in
surveillance activities (defence, airport security checks, etc.). However, the
implementation of this technology for biometrics, is still in its infancy due
to multiple reasons. We present an overview of the existing work in the domain
of spectral imaging for face recognition, different types of modalities and
their assessment, availability of public databases for sake of reproducible
research as well as evaluation of algorithms, and recent advancements in the
field, such as, the use of deep learning-based methods for recognizing faces
from spectral images
Palmprint Recognition in Uncontrolled and Uncooperative Environment
Online palmprint recognition and latent palmprint identification are two
branches of palmprint studies. The former uses middle-resolution images
collected by a digital camera in a well-controlled or contact-based environment
with user cooperation for commercial applications and the latter uses
high-resolution latent palmprints collected in crime scenes for forensic
investigation. However, these two branches do not cover some palmprint images
which have the potential for forensic investigation. Due to the prevalence of
smartphone and consumer camera, more evidence is in the form of digital images
taken in uncontrolled and uncooperative environment, e.g., child pornographic
images and terrorist images, where the criminals commonly hide or cover their
face. However, their palms can be observable. To study palmprint identification
on images collected in uncontrolled and uncooperative environment, a new
palmprint database is established and an end-to-end deep learning algorithm is
proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1)
contains 7881 images from 2035 palms collected from the Internet. The proposed
algorithm consists of an alignment network and a feature extraction network and
is end-to-end trainable. The proposed algorithm is compared with the
state-of-the-art online palmprint recognition methods and evaluated on three
public contactless palmprint databases, IITD, CASIA, and PolyU and two new
databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental
results showed that the proposed algorithm outperforms the existing palmprint
recognition methods.Comment: Accepted in the IEEE Transactions on Information Forensics and
Securit
Spatial-Spectral Manifold Embedding of Hyperspectral Data
In recent years, hyperspectral imaging, also known as imaging spectroscopy, has been paid an increasing interest in geoscience and
remote sensing community. Hyperspectral imagery is characterized by very rich spectral information, which enables us to recognize the materials of interest lying on the surface of the Earth more easier. We have to admit, however, that high spectral dimension inevitably brings some drawbacks, such as expensive data storage and transmission, information redundancy, etc. Therefore, to reduce the spectral dimensionality effectively and learn more discriminative spectral low-dimensional embedding, in this paper we propose a novel hyperspectral embedding approach by simultaneously considering spatial and spectral information, called spatialspectral manifold embedding (SSME). Beyond the pixel-wise spectral embedding approaches, SSME models the spatial and spectral information jointly in a patch-based fashion. SSME not only learns the spectral embedding by using the adjacency matrix obtained by similarity measurement between spectral signatures, but also models the spatial neighbours of a target pixel in hyperspectral scene by sharing the same weights (or edges) in the process of learning embedding. Classification is explored as a potential strategy
to quantitatively evaluate the performance of learned embedding representations. Classification is explored as a potential application for quantitatively evaluating the performance of these hyperspectral embedding algorithms. Extensive experiments conducted on the widely-used hyperspectral datasets demonstrate the superiority and effectiveness of the proposed SSME as compared to several state-of-the-art embedding methods
LW-ODF: A Light-Weight Object Detection Framework for Optical Remote Sensing Imagery
In this paper, we propose to extract the multi-scaled and rotation-insensitive deep features to address the issues of object multi-solutions and rotations in geospatial object detection. To this end, we develop a novel object detection framework where a rotation-insensitive convolution neural network is applied for extracting multi-scaled and directioninsensitive feature representation and then the learned features can be fed into the ensemble classifier learning with fast feature pyramid. Such a non-end-to-end learning strategy intuitively reduces the computational cost without the additional performance loss, yielding an effective and efficient light-weight object detection framework. Experimental results conducted on the NWPU VHR-10 dataset demonstrate
that the proposed framework outperforms several state-ofthe-art baselines