Boosting Hyperspectral Image Classification With Unsupervised Feature Learning

The deep learning-based method has shown promising competence in image classification. Its success can be attributed to the ability to learn discriminative feature representation given plenty of labeled data. However, in real-hyperspectral image (HSI) classification applications, since pixel labeling is difficult and costly, the labels we can obtain within an HSI are always limited and noisy (i.e., inaccurate), which consequently causes overfitting of the deep learning-based method. To address this problem, we propose a novel unified deep learning network to employ both labeled and unlabeled data for training, with which the unsupervised structure knowledge, e.g., intracluster similarity and intercluster dissimilarity, inherently contained in those unlabeled data can be exploited to boost the conventional supervised classification. Specifically, we first explore the unsupervised structure knowledge in unlabeled data via a clustering method and formulate a supervised clustering task on those data with the obtained cluster labels. Then, we propose a multitask network to jointly address both the conventional classification task and the formulated supervised clustering task. With a shared feature extraction module and a high-level feature fusion module, the unsupervised structure knowledge contained in unlabeled data can be effectively introduced into the classification task, which is beneficial to learn a more discriminative feature representation and, thus, well mitigates the overfitting problem and improves the classification results. Experimental results on three data sets demonstrate the proposed method can effectively label the unlabeled data within an HSI, especially when the training labels are limited and noisy.