Journal ArticleDOI
Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis
TLDR
The experimental result shows that the proposed unsupervised band selection algorithms based on band similarity measurement can yield a better result in terms of information conservation and class separability than other widely used techniques.Abstract:
Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that, using a subset of original bands, the accuracy is unchanged or tolerably degraded, whereas computational burden is significantly relaxed. When the desired object information is known, this task can be achieved by finding the bands that contain the most information about these objects. When the desired object information is unknown, i.e., unsupervised band selection, the objective is to select the most distinctive and informative bands. It is expected that these bands can provide an overall satisfactory detection and classification performance. In this letter, we propose unsupervised band selection algorithms based on band similarity measurement. The experimental result shows that our approach can yield a better result in terms of information conservation and class separability than other widely used techniques.read more
Citations
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Journal ArticleDOI
Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification
TL;DR: The proposed framework employs local binary patterns to extract local image features, such as edges, corners, and spots, and employs the efficient extreme learning machine with a very simple structure as the classifier.
Journal ArticleDOI
Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking
Qi Wang,Jianzhe Lin,Yuan Yuan +2 more
TL;DR: This paper proposes to eliminate the drawbacks of traditional salient band selection methods by manifold ranking and puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper.
Journal ArticleDOI
Feature Mining for Hyperspectral Image Classification
TL;DR: An overview of both conventional and advanced feature reduction methods, with details on a few techniques that are commonly used for analysis of hyperspectral data.
Journal ArticleDOI
A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection
TL;DR: Experimental results demonstrate that the bands selected by the enhanced FDPC approach could achieve higher classification accuracy than the FDPC and other state-of-the-art band selection techniques, whereas the isolated-point-stopping criterion is a reasonable way to determine the preferable number of bands to be selected.
Journal ArticleDOI
An Efficient Method for Supervised Hyperspectral Band Selection
TL;DR: A new supervised band-selection algorithm that uses the known class signatures only without examining the original bands or the need of class training samples is proposed, which can complete the task much faster than traditional methods that test bands or band combinations.
References
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Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery
D.C. Heinz,Chein-I-Chang +1 more
TL;DR: The authors present a fully constrained least squares (FCLS) linear spectral mixture analysis method for material quantification, where no closed form can be derived for this method and an efficient algorithm is developed to yield optimal solutions.
Proceedings ArticleDOI
N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data
TL;DR: A method based upon the geometry of convex sets is proposed to find a unique set ofpurest pixels in an image, based on the fact that in N spectral dimensions, the N-volume contained by a simplex formed of the purest pixels is larger than any other volume formed from any other combination of pixels.
Mapping target signatures via partial unmixing of AVIRIS data
TL;DR: In this article, the authors proposed a method to map apparent target abundances in the presence of an arbitrary and unknown spectrally mixed background, which allows the target materials to be present in abundances that drive significant portions of scene covariance.
Journal ArticleDOI
Estimation of number of spectrally distinct signal sources in hyperspectral imagery
Chein-I Chang,Qian Du +1 more
TL;DR: A new definition of virtual dimensionality (VD) is introduced, defined as the minimum number of spectrally distinct signal sources that characterize the hyperspectral data from the perspective view of target detection and classification.
Journal ArticleDOI
A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data
TL;DR: A comparative study of standard endmember extraction algorithms using a custom-designed quantitative and comparative framework that involves both the spectral and spatial information indicates that endmember selection and subsequent mixed-pixel interpretation by a linear mixture model are more successful when methods combining spatial and spectral information are applied.