Hyperspectral Remote Sensing : Dimensional Reduction and End member Extraction
نویسنده
چکیده
In this work, we present an algorithm to overcome the computational complexity of hyperspectral (HS) image data to detect multiple targets/endmembers accurately and efficiently by reducing time and complexity. In order to overcome the computational complexity standard deviation and chi square distance metric methods are considered. The number of endmembers is estimated by unbiased iterative correlation method. Hyperspectral remote sensing is widely used in real time applications such as; Surveillance, Mineralogy, Physics and Agriculture.
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تاریخ انتشار 2012