Abstract
A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest simplex volumes. At each searching step of this extraction algorithm, searching for the simplex with the largest volume is equivalent to searching for a new orthogonal basis which has the largest norm. The new endmember corresponds to the new basis with the largest volume. This algorithm runs very fast and can also avoid the dilemma in traditional simplex-based endmember extraction algorithms such as N-FINDR which generally produces different sets of final endmembers if different initial conditions are used. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which cannot be done by the traditional simplex-based algorithms. Experimental results of both artificial simulated images and practical remote sensing images demonstrate that the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels.
This research was supported by the grant from the National Natural Science Foundation of China (No. 60672116).
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Tao, X., Wang, B., Zhang, L. (2007). A New Approach to Decomposition of Mixed Pixels Based on Orthogonal Bases of Data Space. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_104
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DOI: https://doi.org/10.1007/978-3-540-74171-8_104
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