Reducing the Effect of the Endmembers' Spectral Variability by Selecting the Optimal Spectral Bands
暂无分享,去创建一个
Mehdi Mokhtarzade | Mohammad Javad Valadan Zoej | Omid Ghaffari | M. J. V. Zoej | M. Mokhtarzade | O. Ghaffari
[1] S. Delalieux,et al. An automated waveband selection technique for optimized hyperspectral mixture analysis , 2010 .
[2] Xiuping Jia,et al. Collinearity and orthogonality of endmembers in linear spectral unmixing , 2012, Int. J. Appl. Earth Obs. Geoinformation.
[3] Mario Winter,et al. N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data , 1999, Optics & Photonics.
[4] Gary A. Shaw,et al. Spectral Imaging for Remote Sensing , 2003 .
[5] Antonio J. Plaza,et al. Sparse Unmixing of Hyperspectral Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[6] José M. Bioucas-Dias,et al. Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.
[7] J. Boardman,et al. Mapping target signatures via partial unmixing of AVIRIS data: in Summaries , 1995 .
[8] Chein-I Chang,et al. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery , 2001, IEEE Trans. Geosci. Remote. Sens..
[9] Antonio J. Plaza,et al. Spatial-Spectral Preprocessing Prior to Endmember Identification and Unmixing of Remotely Sensed Hyperspectral Data , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[10] Chein-I Chang,et al. A New Growing Method for Simplex-Based Endmember Extraction Algorithm , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[11] Antonio J. Plaza,et al. Automated Extraction of Image-Based Endmember Bundles for Improved Spectral Unmixing , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[12] K. C. Ho,et al. Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing , 2014, IEEE Signal Processing Magazine.
[13] Chein-I Chang,et al. An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis , 2000, IEEE Trans. Inf. Theory.
[14] Bo Du,et al. Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF , 2016, Remote. Sens..
[15] Ye Zhang,et al. A Novel Geometry-Based Feature-Selection Technique for Hyperspectral Imagery , 2007, IEEE Geoscience and Remote Sensing Letters.
[16] Pol Coppin,et al. Endmember variability in Spectral Mixture Analysis: A review , 2011 .
[17] Robert P. W. Duin,et al. Dimensionality Reduction of Hyperspectral Data via Spectral Feature Extraction , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[18] Chein-I. Chang. Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .
[19] Chein-I Chang,et al. Constrained band selection for hyperspectral imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[20] John A. Richards,et al. Remote Sensing Digital Image Analysis: An Introduction , 1999 .
[21] Antonio J. Plaza,et al. Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery , 2016, Remote. Sens..
[22] Bo Du,et al. A Mutation Operator Accelerated Quantum-Behaved Particle Swarm Optimization Algorithm for Hyperspectral Endmember Extraction , 2017, Remote. Sens..
[23] Jeff Settle,et al. On the effect of variable endmember spectra in the linear mixture model , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[24] D. Lobell,et al. A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .
[25] Qian Du,et al. Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis , 2008, IEEE Geoscience and Remote Sensing Letters.
[26] Chein-I Chang,et al. Comparative Study and Analysis Among ATGP, VCA, and SGA for Finding Endmembers in Hyperspectral Imagery , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[27] Chein-I Chang,et al. Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach , 1994, IEEE Trans. Geosci. Remote. Sens..
[28] Antonio J. Plaza,et al. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[29] Barat Mojaradi,et al. Unsupervised Feature Selection Using Geometrical Measures in Prototype Space for Hyperspectral Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[30] Jiayi Ma,et al. Hyperspectral Unmixing with Robust Collaborative Sparse Regression , 2016, Remote. Sens..
[31] Ioannis D. Schizas,et al. Unsupervised Hyperspectral Unmixing via Kernelized Correlations , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.
[32] Qian Du,et al. A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification , 1999, IEEE Trans. Geosci. Remote. Sens..
[33] José M. Bioucas-Dias,et al. Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[34] Sean Hughes,et al. Clustering by Fast Search and Find of Density Peaks , 2016 .
[35] P. Switzer,et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .