151 research outputs found
Enhancing Hyperspectral Image Quality using Nonlinear PCA
International audienceIn this paper, we propose a new method aiming at reducing the noise in hyperspectral images. It is based on the nonlinear generalization of Principal Component Analysis (NLPCA). The NLPCA is performed by an auto associative neural network that have the hyperspectral image as input and is trained to reconstruct the same image at the output. Thanks to its bottleneck structure, the AANN forces the hyper spectral image to be projected in a lower dimensionality feature space where noise as well as both linear and nonlinear correlations between spectral bands are removed. This process permits to obtain enhancements in terms of hyperspectral image quality. Experiments are conducted on different real hyper spectral images, with different contexts and resolutions. The results are qualitatively and quantitatively discussed and demonstrate the interest of the proposed method as compared to traditional approaches
Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images
Publisher's version (útgefin grein)Managing transmission and storage of hyperspectral (HS) images can be extremely difficult. Thus, the dimensionality reduction of HS data becomes necessary. Among several dimensionality reduction techniques, transform-based have found to be effective for HS data. While spatial transformation techniques provide good compression rates, the choice of the spectral decorrelation approaches can have strong impact on the quality of the compressed image. Since HS images are highly correlated within each spectral band and in particular across neighboring bands, the choice of a decorrelation method allowing to retain as much information content as possible is desirable. From this point of view, several methods based on PCA and Wavelet have been presented in the literature. In this paper, we propose the use of NLPCA transform as a method to reduce the spectral dimensionality of HS data. NLPCA represents in a lower dimensional space the same information content with less features than PCA. In these terms, aim of this research is focused on the analysis of the results obtained through the spectral decorrelation phase rather than taking advantage of both spectral and spatial compression. Experimental results assessing the advantage of NLPCA with respect to standard PCA are presented on four different HS datasets.This work was supported by the Agence Nationale de la Recherche [project APHYPIS]Peer Reviewe
Unsupervised spectral sub-feature learning for hyperspectral image classification
Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images. The proposed method learns a dictionary of sub-feature basis representations from the spectral domain, which allows effective use of the correlated spectral data. The learned dictionary is then used in encoding convolutional samples from the hyperspectral input pixels to an expanded but sparse feature space. Expanded hyperspectral feature representations enable linear separation between object classes present in an image. To evaluate the proposed method, we performed experiments on several commonly used HSI data sets acquired at different locations and by different sensors. Our experimental results show that the proposed method outperforms other pixel-wise classification methods that make use of unsupervised feature extraction approaches. Additionally, even though our approach does not use any prior knowledge, or labelled training data to learn features, it yields either advantageous, or comparable, results in terms of classification accuracy with respect to recent semi-supervised methods
Sparse representation based hyperspectral image compression and classification
Abstract
This thesis presents a research work on applying sparse representation to lossy hyperspectral image
compression and hyperspectral image classification. The proposed lossy hyperspectral image
compression framework introduces two types of dictionaries distinguished by the terms sparse
representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively.
The former is learnt in the spectral domain to exploit the spectral correlations, and the
latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in
hyperspectral images. To alleviate the computational demand of dictionary learning, either a
base dictionary trained offline or an update of the base dictionary is employed in the compression
framework. The proposed compression method is evaluated in terms of different objective
metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including
JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of
both SRSD and MSSD approaches.
For the proposed hyperspectral image classification method, we utilize the sparse coefficients
for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular,
the discriminative character of the sparse coefficients is enhanced by incorporating contextual
information using local mean filters. The classification performance is evaluated and compared
to a number of similar or representative methods. The results show that our approach could outperform
other approaches based on SVM or sparse representation.
This thesis makes the following contributions. It provides a relatively thorough investigation
of applying sparse representation to lossy hyperspectral image compression. Specifically,
it reveals the effectiveness of sparse representation for the exploitation of spectral correlations
in hyperspectral images. In addition, we have shown that the discriminative character of sparse
coefficients can lead to superior performance in hyperspectral image classification.EM201
Perceptual Display Strategies of Hyperspectral Imagery Based on PCA and ICA
This study investigated appropriate methodologies for displaying hyperspectral imagery based on knowledge of human color vision as applied to Hyperion and AVIRIS data. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were used to reduce the data dimensionality in order to make the data more amenable to visualization in three-dimensional color space. In addition, these two methods were chosen because of their underlying relationships to the opponent color model of human color perception. PCA and ICA-based visualization strategies were then explored by mapping the first three PCs or ICs to several opponent color spaces including CIELAB, HSV, YCrCb, and YUV. The gray world assumption, which states that given an image with sufficient amount of color variations, the average color should be gray, was used to set the mapping origins. The rendered images are well color balanced and can offer a first look capability or initial classification for a wide variety of spectral scenes
Remote Sensing Data Compression
A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin
Decorrelation of Neutral Vector Variables: Theory and Applications
In this paper, we propose novel strategies for neutral vector variable
decorrelation. Two fundamental invertible transformations, namely serial
nonlinear transformation and parallel nonlinear transformation, are proposed to
carry out the decorrelation. For a neutral vector variable, which is not
multivariate Gaussian distributed, the conventional principal component
analysis (PCA) cannot yield mutually independent scalar variables. With the two
proposed transformations, a highly negatively correlated neutral vector can be
transformed to a set of mutually independent scalar variables with the same
degrees of freedom. We also evaluate the decorrelation performances for the
vectors generated from a single Dirichlet distribution and a mixture of
Dirichlet distributions. The mutual independence is verified with the distance
correlation measurement. The advantages of the proposed decorrelation
strategies are intensively studied and demonstrated with synthesized data and
practical application evaluations
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