Spatial Pooling Transformer Network and Noise-Tolerant Learning for Noisy Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a hot topic in remote sensing. A large number of studies have been proposed and achieved excellent performance. Most of them rely on accurate annotations. However, this requirement cannot always be met. Due to the complex contents within HSIs and the uncontrollable external interference factors, incorrect labels are inevitable. Thus, the study of noisy HSI classification is boomed. Some attempts have been made, and their central ideas are to filter the noisy samples from the training set. Although feasible, this would result in information loss, i.e., the contents covered by the removed samples are ignored. Besides, the characteristics of HSIs are not fully considered in many models. To overcome the above limitations, we develop a spatial pooling transformer network (SPTNet) and a noise-tolerant learning algorithm in this paper. SPTNet first uses a spectral feature extraction (SFE) module to capture the rich spectral information from HSI patches. Then, three spatial pooling transformers (SPTs) are constructed and stacked to explore the spatial knowledge and depress confusing clues caused by the HSI patch division. Finally, a standard transformer encoder is used to enhance the obtained spectral-spatial features for the downstream classification. To use SPTNet to handle noisy HSI classification, the noise-tolerant learning algorithm is designed. It encloses two parts, i.e., a data partition scheme and a label-independent similarity regularization. The data partition scheme divides the training data into clean and noisy sets. Then, the clean samples are used to train SPTNet with the classification loss function. At the same time, similarity regularization helps SPTNet to comprehensively understand HSIs by analyzing the resemblance between clean and noisy samples. Integrating two parts into a co-training framework, SPTNets can be trained under a noisy scenario. Four popular HSI datasets are selected to testify to our methods. The positive results demonstrate that the combination of SPTNet and the noise-tolerant learning algorithm is helpful to the noisy HSI classification. Our source codes are available at https://github.com/TangXu-Group/Hyperspectral-Images-Classification/tree/main/SPTNet-NTLA.