HYPERSPECTRAL TARGET DETECTION VIA ENSEMBLE LEARNING DEEP MULTIPLE INSTANCE NEURAL NETWORK

In practice, the inaccurate labeling problem in hyperspectral images often has a great impact on the target detection accuracy of hyperspectral images. Modeling this inaccurate labeling problem using multiple instance learning (MIL) is a proven way. In this paper, we propose a deep multiple instance learning method based on ensemble learning, in which 1D convolution neural networks (1D CNN) are adopted to realize an end-to-end hyperspectral target detection structure. In the proposed deep MIL target detection method, the instance-level scores are reconstructed by deep ensemble learning combined with different MIL pooling layers, which helps to estimate the instance labels of positive bags. The method achieved good results in both simulated and real hyperspectral data, showing the effectiveness of the algorithm.