Dual spectral-spatial residual adaptive network for hyperspectral image classification in the presence of noisy labels

In real-world scenarios, Hyperspectral Image (HSI) datasets introduce potential noise inaccuracies due to multiple annotators. Label noise poses a significant challenge for practical deep learning, yet this issue is largely unexplored. Existing methods, which attempt to clean the noisy labelled data to increase classification accuracy, are computationally expensive and face the risk of removing correctly labelled data. In contrast, other methods that work with noisy labelled data but attempt to minimise the noise impact on classification by formulating a robust loss function lose classification accuracy when the ratio of incorrectly to correctly labelled data is high. This work proposes a Dual Spectral-Spatial Residual Adaptive (DSSRA) network to minimise the noise effect even when the amount of noisy labelled data is high. It offers the following contributions: (1) effective salient feature extraction modules to enhance the discriminatory representation of different classes in the proposed DSSRA network; (2) an adjusted noise tolerance loss (ANTL) function that down-weights the impact of learning with noisy labels. ANTL combines normalised focal loss and reverse cross-entropy to counter label noise; and (3) extensive testing on noisy versions of several benchmark HSI datasets. The results show that our DSSRA model outperforms the state-of-the-art HSI classification methods in handling noisy labels, offering a robust solution for real-world applications.