Abstract
The traditional hyperspectral target detection usually recognizes a single type of object at one time. However, there are usually various categories of targets in real scenarios, and it is necessary to simultaneously detect multiple types of targets. Although some detection methods have been proposed, most of them suffer from the limited non-linear spectral expression ability to distinguish different types of hyperspectral targets. To overcome this problem, we propose an ensemble learning-based multi-objective constrained energy minimization (E-IMTCEM) for hyperspectral multi-target detection in this paper. Specifically, E-IMTCEM combines ensemble learning to improve both the non-linear spectral expression ability and detection ability in the task of hyperspectral multi-target detection. The experimental results on simulated hyperspectral images show the effectiveness of the proposed method.
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Wu, Q., Liu, Z. (2022). Constrained Energy Minimization for Hyperspectral Multi-target Detection Based on Ensemble Learning. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13088. Springer, Cham. https://doi.org/10.1007/978-3-030-95408-6_31
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