LiCa: Label-Indicate-Conditional-Alignment Domain Generalization for Pixel-Wise Hyperspectral Imagery Classification
Zhe Gao, Bin Pan, Xia Xu, Tao Li, Zhenwei Shi
Abstract
One of the major difficulties for hyperspectral imagery (HSI) classification is the hyperspectral-heterospectra, which refers to the same material presenting different spectra. Although joint spatial-spectral classification methods can relieve this problem, they may lead to falsely high accuracy because the test samples may be involved during the training process. How to address the hyperspectral-heterospectra problem remains a great challenge for pixel-wise hyperspectral imagery classification methods. Domain generalization is a promising technique that may contribute to the heterospectra problem, where the different spectra of the same material can be considered as several domains. In this paper, inspired by the theory of domain generalization, we provide a formulaic expression for hyperspectral-heterospectra. To be specific, we consider the spectra of one material as a conditional distribution and propose a domain-generalization-based method for pixel-wise HSI classification. The key of our proposed method is a new Label-indicate-Conditional-alignment (LiCa) block that focuses on aligning the spectral conditional distributions of different domains. In the LiCa block, we define two loss functions, cross-domain conditional alignment, and cross-domain entropy, to describe the heterogeneity of HSI. Moreover, we have provided the theoretical foundation for the newly-proposed loss functions, by analyzing the upper bound of classification error in any target domains. Experiments on several public data sets indicate that the LiCa block has achieved better generalization performance when compared with other pixel-wise classification methods.