2,160 research outputs found
Hyperspectral colon tissue cell classification
A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy
Customizing kernel functions for SVM-based hyperspectral image classification
Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by building tailor-made kernels. Observation of real-life spectral imagery from the AVIRIS hyperspectral sensor shows that the useful information for classification is not equally distributed across bands, which provides potential to enhance the SVM's performance through exploring different kernel functions. Spectrally weighted kernels are, therefore, proposed, and a set of particular weights is chosen by either optimizing an estimate of generalization error or evaluating each band's utility level. To assess the effectiveness of the proposed method, experiments are carried out on the publicly available 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. Results indicate that the method is generally effective in improving performance: spectral weighting based on learning weights by gradient descent is found to be slightly better than an alternative method based on estimating ";relevance"; between band information and ground trut
A new kernel method for hyperspectral image feature extraction
Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required
Optimal Clustering Framework for Hyperspectral Band Selection
Band selection, by choosing a set of representative bands in hyperspectral
image (HSI), is an effective method to reduce the redundant information without
compromising the original contents. Recently, various unsupervised band
selection methods have been proposed, but most of them are based on
approximation algorithms which can only obtain suboptimal solutions toward a
specific objective function. This paper focuses on clustering-based band
selection, and proposes a new framework to solve the above dilemma, claiming
the following contributions: 1) An optimal clustering framework (OCF), which
can obtain the optimal clustering result for a particular form of objective
function under a reasonable constraint. 2) A rank on clusters strategy (RCS),
which provides an effective criterion to select bands on existing clustering
structure. 3) An automatic method to determine the number of the required
bands, which can better evaluate the distinctive information produced by
certain number of bands. In experiments, the proposed algorithm is compared to
some state-of-the-art competitors. According to the experimental results, the
proposed algorithm is robust and significantly outperform the other methods on
various data sets
Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier
Over the past decades, the hyperspectral remote sensing technology
development has attracted growing interest among scientists in various domains.
The rich and detailed spectral information provided by the hyperspectral
sensors has improved the monitoring and detection capabilities of the earth
surface substances. However, the high dimensionality of the hyperspectral
images (HSI) is one of the main challenges for the analysis of the collected
data. The existence of noisy, redundant and irrelevant bands increases the
computational complexity, induce the Hughes phenomenon and decrease the
target's classification accuracy. Hence, the dimensionality reduction is an
essential step to face the dimensionality challenges. In this paper, we propose
a novel filter approach based on the maximization of the spectral interaction
measure and the support vector machines for dimensionality reduction and
classification of the HSI. The proposed Max Relevance Max Synergy (MRMS)
algorithm evaluates the relevance of every band through the combination of
spectral synergy, redundancy and relevance measures. Our objective is to select
the optimal subset of synergistic bands providing accurate classification of
the supervised scene materials. Experimental results have been performed using
three different hyperspectral datasets: "Indiana Pine", "Pavia University" and
"Salinas" provided by the "NASA-AVIRIS" and the "ROSIS" spectrometers.
Furthermore, a comparison with the state of the art band selection methods has
been carried out in order to demonstrate the robustness and efficiency of the
proposed approach.
Keywords: Hyperspectral images, remote sensing, dimensionality reduction,
classification, synergic, correlation, spectral interaction information, mutual
infor
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images
Recently, the hyperspectral sensors have improved our ability to monitor the
earth surface with high spectral resolution. However, the high dimensionality
of spectral data brings challenges for the image processing. Consequently, the
dimensionality reduction is a necessary step in order to reduce the
computational complexity and increase the classification accuracy. In this
paper, we propose a new filter approach based on information gain for
dimensionality reduction and classification of hyperspectral images. A special
strategy based on hyperspectral bands selection is adopted to pick the most
informative bands and discard the irrelevant and noisy ones. The algorithm
evaluates the relevancy of the bands based on the information gain function
with the support vector machine classifier. The proposed method is compared
using two benchmark hyperspectral datasets (Indiana, Pavia) with three
competing methods. The comparison results showed that the information gain
filter approach outperforms the other methods on the tested datasets and could
significantly reduce the computation cost while improving the classification
accuracy. Keywords: Hyperspectral images; dimensionality reduction; information
gain; classification accuracy.
Keywords: Hyperspectral images; dimensionality reduction; information gain;
classification accuracy
- …