QSCDNet: A Hybrid Quantum Spectral Change Detection Network for Hyperspectral Image Change Detection

Hyperspectral image change detection (HSI-CD) is an important remote sensing technique for identifying fine-grained land-cover change. Deep learning methods such as convolutional neural networks (CNNs) and transformers have achieved good performance in HSI-CD. However, due to the pseudo-changes caused by imaging conditions, the spectral change characteristics within each pixel often exhibit uncertainties. In this study, differing from the traditional deep learning methods, we aimed to relate the abovementioned spectral change uncertainty to the perspective of the quantum state and built a dual-branch hybrid quantum neural network for HSI-CD (QSCDNet). The quantum branch consists of several 2-D quantum spectral change convolutional blocks (QSCCBs). These blocks provide a wider variety of expression forms of the spectral change, independent of the pseudo-change impact, through a parameterized quantum circuit (PQC), to better extract the change information of the spectral features. The CNN branch is designed based on a channel attention network architecture to provide traditional network change features. The output features of the quantum network branch and the CNN branch are then fused based on a cross-domain change feature fusion module (CDFM). The impact of the number of QSCCBs was also analyzed to verify the effectiveness of increasing the depth of the quantum network structure. The proposed method was tested on three public HSI-CD datasets and compared with the state-of-the-art methods to validate its potential in the field of HSI-CD.