3DCNN-NF: Few-Shot Hyperspectral Image Change Detection Based on 3-D Convolution Neural Network and Normalizing Flow

In recent years, deep learning has shown promising results in change detection (CD) of hyperspectral images (HSIs). However, these algorithms often require a large number of labeled samples, which can be costly in practical applications. This article proposes a few-shot HSI CD method based on a 3-D convolution neural network and normalizing flow. To mitigate the issue of limited training samples, we develop a low-parameter baseline CD model utilizing 3-D convolution neural networks to extract spectral-spatial features while preventing overfitting. In addition, the proposed method analyzes the spectral-spatial distribution of hyperspectral variation by normalizing flow and generates hyperspectral tensor samples that approximate the distribution, thus enhancing CD performance. Furthermore, to improve the sample generation speed, we employ soft labels and label smoothing techniques to assign high-quality labels to the generated samples, thereby increasing the number of available samples. The method is evaluated on three datasets, and the experimental results demonstrate the efficacy of the proposed approach for detecting changes with limited training samples. The proposed method offers a promising solution to the challenge of detecting changes with limited training samples in HSIs.