BiG-FSLF: A Cross Heterogeneous Domain Few-Shot Learning Framework Based on Bidirectional Generation for Hyperspectral Image Change Detection

In recent years, hyperspectral image change detection (HSI-CD) based on deep learning has achieved high detection accuracy, but these methods obtain excellent detection results usually rely on having sufficient labeled samples to train the network. However, the production of HSI label is difficult, costly and inefficient. In practical tasks, often only a limited number of labeled samples can be obtained due to the limitation of timeliness. To address this problem, a cross heterogeneous domain few-shot learning framework based on bidirectional generation (BiG-FSLF) is proposed for HSI-CD, which aims to solve the few-shot problem of HSI-CD by few-shot learning (FSL), and to assist HSI-FSL perform better by obtaining learnable changed information (i.e., empirical knowledge) from another remote sensing data. Specifically, a multitask generation encoder (MLGenE) is designed to take on both the tasks of FSL and domain adaptation to achieve HSI-CD under the condition of cross heterogeneous domain few-shot. First, we take any pair of image data in a very high resolution image (VHRI) CD dataset as the source domain and HSI is used as the target domain, using sufficient labeled samples in source domain and a small number of labeled samples in target domain for FSL. Meanwhile, a bidirectional generation domain adaptation (BiGDA) method based on generative adversarial strategy is proposed to achieve adaptive alignment of the two heterogeneous domains (source and target domains) feature distributions, to mitigate the impact of the domain shift problem inherent to cross domain data on FSL. Abundant experiments with only five training samples on the publicly available popular HSI-CD datasets confirm that the proposed method can show great detection performance. The source code of the proposed framework will be released at https://github.com/lsylnnu/BiG-FSLF.