Research Article
Distributed Parallel Endmember Extraction of Hyperspectral Data Based on Spark
Algorithm 1
Endmember extraction based on IEA.
Input: Hyperspectral data , the number of endmembers m. | (1) Initialization: Threshold of (spectral angle), the number R for averaging the vectors | with the largest error, and endmember matrix . | (2) Calculate the mean vector of the hyperspectral data . | (3) Perform the constrained unmixing on using as endmember matrix, and | get the image of the errors (named “error image”) remaining after the unmixing. | repeat | (4) Find R pixel vectors with the largest error in the error image, and extract the subset of | the set of R vectors, consisting of all those vectors which fall within the angle of the | maximum error vector. | (5) Average the vectors in the subset to decrease the effects of outliers and noise, denoted | as , and update endmember matrix . | until m endmembers are extracted. |
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