Reinforcement Learning for Neural Architecture Search in Hyperspectral Unmixing
Zhu Han, Danfeng Hong, Lianru Gao, Swalpa Kumar Roy, Bing Zhang, Jocelyn Chanussot
Abstract
In this letter, a novel neural architecture search (NAS) method based on reinforcement learning, called RLNAS, is devised to realize the automatic architecture design in the field of hyperspectral unmixing (HU). This method first train the search network in the constructed self-supervised datasets based on hyperspectral images. The block-based searching and weight-sharing strategies are then introduced to reduce the computational cost in the training phase. The final optimal architecture is obtained by optimizing the multi-objective reward function to balance the trade-off between accuracy and computational efficiency. Compared with the state-of-the-art unmixing algorithms, the proposed RLNAS method can yield better unmixing results on synthetic and real hyperspectral datasets, which verifies its effectiveness and superiority. In addition, the proposed method offers promising potential of the NAS for HU.