2,222 research outputs found
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Hyperspectral images show similar statistical properties to natural grayscale
or color photographic images. However, the classification of hyperspectral
images is more challenging because of the very high dimensionality of the
pixels and the small number of labeled examples typically available for
learning. These peculiarities lead to particular signal processing problems,
mainly characterized by indetermination and complex manifolds. The framework of
statistical learning has gained popularity in the last decade. New methods have
been presented to account for the spatial homogeneity of images, to include
user's interaction via active learning, to take advantage of the manifold
structure with semisupervised learning, to extract and encode invariances, or
to adapt classifiers and image representations to unseen yet similar scenes.
This tutuorial reviews the main advances for hyperspectral remote sensing image
classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification
Hyperspectral image (HSI) classification, which aims to assign an accurate
label for hyperspectral pixels, has drawn great interest in recent years.
Although low rank representation (LRR) has been used to classify HSI, its
ability to segment each class from the whole HSI data has not been exploited
fully yet. LRR has a good capacity to capture the underlying lowdimensional
subspaces embedded in original data. However, there are still two drawbacks for
LRR. First, LRR does not consider the local geometric structure within data,
which makes the local correlation among neighboring data easily ignored.
Second, the representation obtained by solving LRR is not discriminative enough
to separate different data. In this paper, a novel locality and structure
regularized low rank representation (LSLRR) model is proposed for HSI
classification. To overcome the above limitations, we present locality
constraint criterion (LCC) and structure preserving strategy (SPS) to improve
the classical LRR. Specifically, we introduce a new distance metric, which
combines both spatial and spectral features, to explore the local similarity of
pixels. Thus, the global and local structures of HSI data can be exploited
sufficiently. Besides, we propose a structure constraint to make the
representation have a near block-diagonal structure. This helps to determine
the final classification labels directly. Extensive experiments have been
conducted on three popular HSI datasets. And the experimental results
demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.Comment: 14 pages, 7 figures, TGRS201
A Subband-Based SVM Front-End for Robust ASR
This work proposes a novel support vector machine (SVM) based robust
automatic speech recognition (ASR) front-end that operates on an ensemble of
the subband components of high-dimensional acoustic waveforms. The key issues
of selecting the appropriate SVM kernels for classification in frequency
subbands and the combination of individual subband classifiers using ensemble
methods are addressed. The proposed front-end is compared with state-of-the-art
ASR front-ends in terms of robustness to additive noise and linear filtering.
Experiments performed on the TIMIT phoneme classification task demonstrate the
benefits of the proposed subband based SVM front-end: it outperforms the
standard cepstral front-end in the presence of noise and linear filtering for
signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed
front-end with a conventional front-end such as MFCC yields further
improvements over the individual front ends across the full range of noise
levels
Hyperspectral images segmentation: a proposal
Hyper-Spectral Imaging (HIS) also known as chemical or spectroscopic imaging is an emerging technique that combines
imaging and spectroscopy to capture both spectral and spatial information from an object. Hyperspectral images are
made up of contiguous wavebands in a given spectral band. These images provide information on the chemical
make-up profile of objects, thus allowing the differentiation of objects of the same colour but which possess make-up
profile. Yet, whatever the application field, most of the methods devoted to HIS processing conduct data analysis without
taking into account spatial information.Pixels are processed individually, as an array of spectral data without any spatial
structure. Standard classification approaches are thus widely used (k-means, fuzzy-c-means hierarchical
classification...). Linear modelling methods such as Partial Least Square analysis (PLS) or non linear approaches like
support vector machine (SVM) are also used at different scales (remote sensing or laboratory applications). However,
with the development of high resolution sensors, coupled exploitation of spectral and spatial information to process
complex images, would appear to be a very relevant approach. However, few methods are proposed in the litterature.
The most recent approaches can be broadly classified in two main categories. The first ones are related to a direct
extension of individual pixel classification methods using just the spectral dimension (k-means, fuzzy-c-means or FCM,
Support Vector Machine or SVM). Spatial dimension is integrated as an additionnal classification parameter (Markov
fields with local homogeneity constrainst [5], Support Vector Machine or SVM with spectral and spatial kernels
combination [2], geometrically guided fuzzy C-means [3]...). The second ones combine the two fields related to each
dimension (spectral and spatial), namely chemometric and image analysis. Various strategies have been attempted. The
first one is to rely on chemometrics methods (Principal Component Analysis or PCA, Independant Component Analysis or
ICA, Curvilinear Component Analysis...) to reduce the spectral dimension and then to apply standard images processing technics on the resulting score images i.e. data projection on a subspace. Another approach is to extend the definition
of basic image processing operators to this new dimensionality (morphological operators for example [1, 4]).
However, the approaches mentioned above tend to favour only one description either directly or indirectly (spectral or
spatial). The purpose of this paper is to propose a hyperspectral processing approach that strikes a better balance in the
treatment of both kinds of information....Cet article présente une stratégie de segmentation d’images hyperspectrales liant de façon symétrique et
conjointe les aspects spectraux et spatiaux. Pour cela, nous proposons de construire des variables latentes
permettant de définir un sous-espace représentant au mieux la topologie de l’image. Dans cet article, nous
limiterons cette notion de topologie à la seule appartenance aux régions. Pour ce faire, nous utilisons d’une
part les notions de l’analyse discriminante (variance intra, inter) et les propriétés des algorithmes de
segmentation en région liées à celles-ci. Le principe générique théorique est exposé puis décliné sous la
forme d’un exemple d’implémentation optimisé utilisant un algorithme de segmentation en région type split
and merge. Les résultats obtenus sur une image de synthèse puis réelle sont exposés et commentés
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