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- W2354522275 abstract "In order to overcome the limitations including high computational complexity,the requirement of preprocessing of original data and poor extraction accuracy in the hyperspectral remote sensing image endmember extraction algorithms which produce endmembers based on maximum volume transform in the feature space,an endmember extraction algorithm was proposed after the correlation of image data in the high dimensional feature space was analyzed.A projection vector orthogonalized a less-one-dimension hyperplane,onto which projection operators compress the low-dimension correlation data into a point,was calculated by using base transformation in feature space,hence,an isolate pixel was determined as an endmember which has the maximum distance from the point,and endmembers extracted in each step were fed back as a part of input of the next step to ensure the extraction accuracy.As a result of the calculation of the distance in the high dimensional feature space instead of volume,the proposed algorithm can extract endmembers in a short time effectively and reduce computation significantly.Each extraction step depending on a subspace reflected linear correlation of the whole image in the feature space leads to that the preprocess of the original image become unnecessary,therefore,the extraction accuracy is enhanced due to avoiding the hazard of loss of small targets.Experiments using simulation image and real image show the effectiveness of the algorithm." @default.
- W2354522275 created "2016-06-24" @default.
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- W2354522275 date "2012-01-01" @default.
- W2354522275 modified "2023-09-24" @default.
- W2354522275 title "Hyperspectral Image Endmember Extractio Algorithm Based on Linear Transformation" @default.
- W2354522275 hasPublicationYear "2012" @default.
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