Boosting Hyperspectral Image Classification with Dual Hierarchical Learning

Abstract

Hyperspectral image (HSI) classification aims at predicting the pixel-wise labels in an image, where there are only a few labeled pixel samples (hard labels) for training. It is a challenging task since the classification process is susceptible to over-fitting under training with limited samples. To relieve this problem, we propose a method based on dual hierarchical learning. First, we employ a connectionist hyperspectral convolution (HC) network to capture the representations of the pixels from different receptive fields. Specifically, an HC is designed to learn the correlation among adjacent pixels and is further extended to a connectionist hierarchical structure. These operations use the correlation to enhance one-pixel learning from multiple receptive fields. Second, we analyze the properties in the hyperspectral image and introduce a hierarchical pseudo label generation algorithm to enrich the supervision of the label information. Finally, we design a dual hierarchical learning strategy to help all HC layers learn from both the hard labels and the hierarchical pseudo labels. In other words, it addresses the HSI classification problem from different views. For inference, we employ two fusion strategies to find a better prediction. The experimental results on four popular HSI benchmarks, i.e., Salinas-A, IndianPines, PaviaU, and PaviaC, demonstrate the effectiveness of the proposed method. Our code is publicly available on GitHub: https://github.com/ShuoWangCS/HSI-DHL.