A Bag-Level Data Imbalanced Multiple Instance Hyperspectral Target Representation
Jiaxin Shan, Zhiqiang Gong, Ping Zhong · 2018
Recently, multiple instance learning (MIL) tends to be a popular method for hyperspectral target representation since it does not require the detailed information about the target. However, the data imbalance problem occurred in MIL usually has negative effects on the representation of hyperspectral target. To solve the problem, this paper propose a bag-level data balance-inducing multiple instance learning method for hyperspectral target representation. The method aims to balance the positive and negative bags by synthesizing new positive bags since the number of positive bags in the training data set is much smaller than that of negative bags. Experimental results show that the proposed method can improve the target representation ability and enhance the target detection performance.