A novel mixed pixels unmixing method for multispectral images

The key to mixed pixels unmixing is to determine a set of spectral endmembers that are representative of the surface components in the area covered by multispectral images. We propose a geometric method utilizing the spectrum of two or three bands to determine the endmembers under the assumption that the endmembers are the pure surface components that lie in the extremities of the scatter plot of 2- or 3-band reflectance. If there are more bands than endmembers, no additive noise and signature spectral matrix S has full column rank, we can estimate the endmemher abundances by unconstrained least squares method. Finally, the validity and feasibility of the algorithm are demonstrated by simulation results.

[1]  D. C. Heinz,et al.  Fully constrained least-squares based linear unmixing [hyperspectral image classification] , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[2]  Paul E. Johnson,et al.  Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site , 1986 .

[3]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[4]  S. Tompkins,et al.  Optimization of endmembers for spectral mixture analysis , 1997 .

[5]  Fabio Maselli,et al.  Automatic identification of end-members for the spectral decomposition of remotely sensed scenes , 1996, Remote Sensing.

[6]  Alan P. Schaum,et al.  Application of stochastic mixing models to hyperspectral detection problems , 1997, Defense, Security, and Sensing.

[7]  Chein-I Chang,et al.  Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach , 1994, IEEE Trans. Geosci. Remote. Sens..

[8]  Chein-I Chang,et al.  Constrained subpixel target detection for remotely sensed imagery , 2000, IEEE Trans. Geosci. Remote. Sens..

[9]  J. Boardman,et al.  Geometric mixture analysis of imaging spectrometry data , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[10]  Yiu-ming Cheung.  A competitive and cooperative learning approach to robust data clustering , 2004, Neural Networks and Computational Intelligence.

[11]  Yu-ming Cheung.  Rival penalization controlled competitive learning for data clustering with unknown cluster number , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[12]  D. Roberts,et al.  A new approach to quantifying abundances of materials in multispectral images , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[13]  A. Goetz,et al.  Terrestrial imaging spectroscopy , 1988 .

[14]  C. Tucker,et al.  Tropical Deforestation and Habitat Fragmentation in the Amazon: Satellite Data from 1978 to 1988 , 1993, Science.

[15]  Maurice D. Craig,et al.  Minimum-volume transforms for remotely sensed data , 1994, IEEE Trans. Geosci. Remote. Sens..