Spectral Unmixing Cluster Validity Index for Multiple Sets of Endmembers

被引:14
作者
Anderson, Derek T. [1 ]
Zare, Alina [2 ]
机构
[1] Mississippi State Univ, Dept Elect & Comp Engn, ECE, Mississippi State, MS 39762 USA
[2] Univ Missouri, Dept Elect & Comp Engn, Columbia, MO 65211 USA
关键词
Cluster validity; endmember; hyperspectral; piece-wise convex; spectral variation; unmixing; MIXTURE ANALYSIS; VARIABILITY; SELECTION; FUZZY;
D O I
10.1109/JSTARS.2012.2189556
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A hyperspectral pixel is generally composed of a relatively small number of endmembers. Several unmixing methods have been developed to enforce this concept through sparsity promotion or piece-wise convex mixing models. Piece-wise convex unmixing methods often require as parameters the number of endmembers and the number of sets of endmembers needed. However, these values are often unknown in advance and difficult to estimate. In this article, a new cluster validity index for multiple sets of endmembers is developed. The proposed index is used to evaluate spectral unmixing results and identify optimal parameter sets for piece-wise convex unmixing methods. No other conventional cluster validity index is directly applicable or theoretically well-suited for the piece-wise convex model. Specifically, we focus on addressing cases in which endmembers may or may not be located in a dense region of the data. Additionally, we focus on cases in which hyperspectral data is well distributed within a convex cluster (not exhibiting significant holes or gaps). The proposed validity index is applied to both simulated and real hyperspectral data. Results show that the proposed method consistently selects the best parameter set.
引用
收藏
页码:1282 / 1295
页数:14
相关论文
共 38 条
[1]   Comparing Fuzzy, Probabilistic, and Possibilistic Partitions [J].
Anderson, Derek T. ;
Bezdek, James C. ;
Popescu, Mihail ;
Keller, James M. .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2010, 18 (05) :906-918
[2]  
[Anonymous], 2012, AV CUPR DAT SUB
[3]  
[Anonymous], Pattern Recognition with Fuzzy Objective Function Algorithms
[4]   Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis [J].
Bateson, CA ;
Asner, GP ;
Wessman, CA .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2000, 38 (02) :1083-1094
[5]  
Bezdek J. C., 1974, J CYBERNIT, V3, P55
[6]   Hyperspectral subspace identification [J].
Bioucas-Dias, Jose M. ;
Nascimento, Jose M. P. .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2008, 46 (08) :2435-2445
[7]  
Broadwater J., 2009, P IEEE IGARSS JUL, pIV
[8]   Spatially Adaptive Hyperspectral Unmixing [J].
Canham, Kelly ;
Schlamm, Ariel ;
Ziemann, Amanda ;
Basener, Bill ;
Messinger, David .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2011, 49 (11) :4248-4262
[9]   Estimation of number of spectrally distinct signal sources in hyperspectral imagery [J].
Chang, CI ;
Du, Q .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2004, 42 (03) :608-619
[10]   CLUSTER SEPARATION MEASURE [J].
DAVIES, DL ;
BOULDIN, DW .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1979, 1 (02) :224-227