Application of hyperspectral band selection method based on deep reinforcement learning to low-value recyclable waste classification

Hyperspectral images (HSIs) have proven effective for classification of Low-value Recyclable Waste (LVRW). However, the high correlation between bands in HSIs introduces redundant information. In this paper, to overcome the challenge of low applicability of existing band selection methods of LVRW HSIs, we propose a Dueling Double Deep Q Network based on Supervised Band Selection method (D3QN-SBS). Specifically, we formulate band selection as a reinforcement learning problem, treating it as a combinatorial optimization task that explores band combinations within a discrete space, using overall accuracy as the reward to tune the policy and optimise the state-action value function. The results of comparative experiments show that D3QN-SBS outperforms other methods when selecting 2–10 bands, where achieves an accuracy of about 99.24 %, 99.10 %, 99.05 %, and 99.16 % in k-NN, SVM-RBF, RF, and MLP based on 10 bands, the precision, recall, and F1-score are nearly 100 % for OTHERS and are more than 99 % for PS. In K-fold cross-validation, the majority of the folds under four classifiers achieves above 98 % for four evaluation metrics and the average F1-scores are 99.25 %, 99.01 %, 99.06 %, and 99.17 %. This approach can be deployed in LVRW sorting equipment, contributing to the advancement of hyperspectral imaging technologies in plastic waste sorting field.