A practical hyperspectral target characterization task estimates a target signature from imprecisely labeled training data. The imprecisions arise from the characteristics of the real-world tasks. First, accurate pixel-level labels on training data are often unavailable. Second, the subpixel targets and occluded targets cause the training samples to contain mixed data and multiple target types. To address these imprecisions, this paper proposes a new hyperspectral target characterization method to produce diverse multiple hyperspectral target signatures under a multiple instance learning (MIL) framework. The proposed method uses only bag-level training samples and labels, which solves the problems arising from the mixed data and lack of pixel-level labels. Moreover, by formulating a multiple characterization MIL and including a diversity-promoting term, the proposed method can learn a set of diverse target signatures, which solves the problems arising from multiple target types in training samples. The experiments on hyperspectral target detections using the learned multiple target signatures over synthetic and real-world data show the effectiveness of the proposed method.