Blindly selecting method of training samples based hyper-spectral image´s intrinsic character for object recognition

Based on the intrinsic assembling feature of the hyper-spectral images, we present a method to select the training samples for object recognition without any other previous knowledge. Firstly, we use the Parzen´s window method to find the easily separable dimensions of the hyper-spectral images, then gain the smallest representative sample sets of all objects through intersecting the data of the same object of each easily separable dimensions, and obtain the object´s number and the training data sources for the neural networks (NN) at the same time; secondly, train the neural network ensembles using the data selected from the representative sample sets to label the other data. Lastly, we analyzed the hyper-spectral images to detect red tide using this method, which proved this method could recognize the red tide effectively.