Interdomain Collaboration Between Hyperspectral and VHR Remote Sensing Images: A Cross-Scene Few-Shot Learning Framework for Change Detection

Hyperspectral image change detection (HSI-CD) based on deep learning (DL) has made significant progress. However, these methods rely significantly on the number of labeled data. Annotating HSI is a highly complex task that requires professional knowledge for guidance, resulting in a scarcity of high-quality labeled samples. The emergence of few-shot learning (FSL), which supports model learning from limited labeled samples, can address this issue. However, FSL-based methods still face some challenges: 1) existing methods mainly rely on single-source or homogenous cross-domain HSI data, which is difficult to adequately cope with the problem of scarcity of HSI labeled data; 2) most existing methods usually only focus on local features within patches and neglect interrelationships between patches, which is also important for model learning; and 3) transformers modeling long-range relationships rely on extensive labeled data, making it difficult to perform well in few-shot scenarios. Therefore we propose a cross-scene FSL framework based on interdomain collaboration (CSIDC-FSL) for HSI-CD. Specifically, the following is proposed: 1) FSL is performed on very high-resolution image (VHRI) and HSI, aiming to use the learnable information in VHRI with low annotation cost to help HSI-CD, reducing the dependence of the model on HSI annotation data while enabling multilevel feature hybrid perceptual CD; 2) a dual-information integrated mapping module (DI2M) is proposed, which designs a CNN and transformer integrated structure that can simultaneously focus on local features and class-wise long-range relationships to break the constraints of local perception of CNN while optimizing the performance of transformer under few-shot situations; and 3) the interdomain joint information allocation module (IDM) is designed to capture cross-scene domain-wise distribution features, and mitigate the impact of distribution differences in cross-scene data (VHRI and HSI) on knowledge learning and migration through the collaboratively consistent interdomain features. Under the condition of five samples per class, the CD results of CSIDC-FSL are better than those of recently advanced algorithms, with average improvements of 1.46%–1.5% for overall accuracy (OA) and average accuracy (AA), respectively. The code will be made available at
https://github.com/lsylnnu/CSIDC-FSL
.