Study on the methods of hyperspectral image saliency detection based on MBCNN

Hyperspectral imaging is resource-intensive due to the extensive spectral information it captures. This paper introduces an advanced hyperspectral saliency object detection method via a novel multibranch convolutional neural network (MBCNN), which synergizes convolutional neural networks and random forests to utilize both spectral and spatial data more efficiently. We have optimized the MBCNN by refining channel numbers to improve feature sampling rates, enhancing the neural network layers for robust representation, and developing a new loss function that integrates mean absolute error with precision-recall metrics to overcome common discrepancies between saliency map losses and actual detection precision or recall. These enhancements have led to a marked performance increase, evidenced by a 2.5% rise in AUC score to 0.941 and a 13% improvement in MAE score to 0.146 over the baseline MBCNN. Our experimental results confirm the significant advancements these modifications contribute to the network’s detection capabilities in hyperspectral saliency object detection. Code is available at https://github.com/Ccu-yankang/MBCNN_4_HSI.